Patient information management method and system based on smart medical care

Abstract

The present invention discloses a patient information management method and system based on smart medical care. The present invention relates to the field of smart medical technology and solves the problem that when the patient information is actually processed, the corresponding patient information format conversion rate is slow. The present invention confirms that the patient information text needs to be provided by multiple different server ends, and then based on the associated formats of the multiple different server ends, the associated text of the patient information is converted. When the corresponding classified text is converted in format, the method with the shortest conversion time is adopted. Therefore, when the text is restored at a later stage, the restoration speed is still in the fastest restoration conversion mode, which can fully reduce the corresponding conversion time to ensure that the format conversion time of the corresponding information text can be fully reduced.

Description

Technical Field

[0001] The present invention relates to the field of smart medical technology, and specifically to a patient information management method and system based on smart medical care. Background Art

[0002] Smart healthcare is a new medical service model that uses modern information technology, such as the Internet of Things, cloud computing, and artificial intelligence, to improve the quality and efficiency of medical services.

[0003] The application with publication number CN117219240A discloses a maternal and child hospital information sharing method and system based on smart medical care, and uploads the compression result to the hospital information sharing platform; the determined compression discrete coefficient of the target hospital patient interaction information is adapted to the element description field of the target hospital patient interaction information set. In other words, the compression discrete coefficient used to compress the target hospital patient interaction information can be determined in a targeted manner according to the element description field of the target hospital patient interaction information set to obtain the compression result, and upload the compression result to the hospital information sharing platform. In this way, when the compressed hospital patient interaction information set meets the specified requirements, the quality of the hospital patient interaction information set can be guaranteed, and the compression effect of the hospital patient interaction information set can be improved, thereby realizing information sharing more reliably.

[0004] In the smart medical system, the patient information generated needs to be stored in the cloud, because hospitals are gradually joining smart medical care to share information. However, in the specific information sharing process, the formats that can be accepted by the network terminals of some hospitals are different. Therefore, when the patient information is actually processed, the corresponding patient information format conversion rate will be slow, and the corresponding medical staff will have to wait for a long time, and the patient information cannot complete the relevant information format conversion process in a relatively short time. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a patient information management method and system based on smart medical care, which solves the problem of slow conversion rate of the corresponding patient information format when actually processing the patient information.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A patient information management method based on smart medical care includes the following steps:

[0007] Step 1: Based on the patient information generated by the corresponding medical patients, such patient information is preferentially transmitted to the cloud for associated storage, and then based on the multiple partitioned server ends involved in this smart medical care, the associated formats of multiple partitioned server ends are confirmed, and based on the confirmed associated formats, the information text of such patient information is initially classified, and then the format of the classified texts of different categories is converted, and the multi-format text of this patient information is locked and stored. The specific sub-steps are:

[0008] S11, based on the determined association formats of the multiple partition servers, identifying different formats existing in the multiple association formats, and marking the identified different formats as formats to be classified;

[0009] S12. Based on the specific number of formats to be classified, the information text of the patient information is divided into the same number of classified texts, and then the data formats associated with the relevant classified texts are confirmed and marked as text formats. A group of formats to be classified with the fastest conversion rate of the corresponding classified texts are selected in turn, and the converted multi-format texts are stored, while keeping the sorting method of the original classified texts unchanged during storage. The specific method is as follows:

[0010] S121, randomly selecting a group of classified texts from a plurality of classified texts as processing texts, and based on the text formats in the processed texts, identifying the conversion rate between the text format and the data format from the format conversion rate table and calibrating it as V i-k , where i represents different text formats, k represents different data formats, and the format conversion rate table is a preset table. The table contains corresponding conversion rates between text formats and data formats, and the corresponding conversion rates are preset values;

[0011] S122, identifying the text content capacity R corresponding to the text format i , using R i ÷V i-k =T i-k Determine the conversion time T i-k , and then identify the different T generated by other text formats in this processed text i-k , based on the label k, different T i-k Perform summation and lock the sum value Z k , and then from multiple Z K Select the minimum value, use the k corresponding to the minimum value as the best classification format for this classification text, and directly perform text format conversion to confirm the format text of this classification text;

[0012] S123, for the remaining other classified texts, the same method as steps S121-S122 is adopted to confirm the format text of the classified text, and the k that has been selected as the best classification format is no longer selected;

[0013] S124, merging the generated multi-format texts according to the sorting method of the original classified texts and storing them in the cloud for text extraction by multiple different partition servers;

[0014] Step 2: Based on the associated formats corresponding to the multiple partitioned servers, in order to prevent the subsequent texts of the corresponding associated formats from interfering with each other during transmission, the associated frequencies of the corresponding associated formats during transmission are identified, and then based on the identification results, the best two groups of parallel transmission texts are locked from the corresponding multi-format texts, and relevant markings are performed. The specific sub-steps are as follows:

[0015] S21. Based on the determined multiple sets of associated formats, identify different formats between the multiple sets of associated formats, and then identify data frequencies of different formats in the data transmission process from historical completed data or cloud data, select a minimum value and a maximum value from the identified multiple sets of data frequencies, and lock the frequency interval belonging to the corresponding format;

[0016] S22, cross-analyzing multiple frequency intervals in different formats: preferentially sorting the frequency intervals in ascending order based on the median value of each frequency interval, and confirming a set of frequency intervals after sorting;

[0017] Starting from the first frequency interval of the frequency interval set: take the first frequency interval as the main interval, and preferentially select the last frequency interval in the frequency interval set for cross analysis with the main interval to determine the crossover range:

[0018] If the crossover range is located in the last group of frequency intervals and the proportion of the main interval is less than 20%, the last group of frequency intervals selected will be used as the subsidiary interval of the main interval;

[0019] When there is a group of values ​​whose proportion is not less than 20%, or when both groups of values ​​are not less than 20%, the second-to-last frequency interval is selected, and the interval cross analysis is performed, and so on, until the subsidiary interval belonging to the main interval is determined. If it is not determined, the confirmation operation of this main interval is stopped, and the subsidiary intervals of other frequency intervals are confirmed in the same way;

[0020] S23, confirming that there is an associated format corresponding to the frequency interval of the subsidiary interval, then determining the associated format corresponding to the subsidiary interval, and calibrating the format texts corresponding to the two groups of associated formats as parallel transmission texts;

[0021] Step 3: When the cloud receives a call instruction for specifying a multi-format text, based on the parallel transmission text determined by the multi-format text, the parallel transmission text is transmitted to the designated partition server end in a parallel transmission manner, and bandwidth is adjusted during the transmission process to change the transmission rate of the parallel transmission text. The specific sub-steps are:

[0022] S31, transmitting the determined parallel transmission texts simultaneously, and during the transmission process, dividing the bandwidth of the original transmission channel into two groups of micro-bandwidths for use by the two groups of transmission texts in the parallel transmission texts;

[0023] S32, recording in real time the transmission rates V1 and V2 of the two groups of transmission texts in the parallel transmission text, if |V1-V2|≤Y1, no processing is required, wherein Y1 is a preset value;

[0024] If |V1-V2|>Y1, the maximum transmission rate is selected from V1 and V2, and the micro-bandwidth of the channel associated with the maximum transmission rate is gradually allocated to the micro-bandwidth of another channel until |V1-V2|≤Y1, so as to ensure that the transmission rates of the two parallel transmission texts are relatively consistent during text transmission;

[0025] After the parallel transmission text is completed, the other formats of the multi-format text are transmitted, and then all the multi-format text is transmitted to the partition server end;

[0026] Step 4: After the designated partition server receives the multi-format text, it converts other formats within the multi-format text into associated formats according to the associated formats of the partition server, obtains the patient information text that can be recognized by the partition server, and displays it directly.

[0027] Preferably, the patient information management system based on smart medical care includes:

[0028] The to-be-classified format confirmation terminal confirms the associated formats of the multiple partitioned server terminals based on the multiple partitioned server terminals involved in the smart medical care, and marks the identified different formats as to-be-classified formats based on the different formats existing in the multiple associated formats;

[0029] The format conversion storage end converts the format of the patient information text to be stored based on the determined format to be classified, and stores the converted multi-format text;

[0030] The parallel text marking end, based on the associated formats corresponding to the multiple partitioned server ends, identifies the associated frequencies of the corresponding associated formats during the transmission process so that the subsequent texts of the corresponding associated formats will not interfere with each other during transmission. Based on the recognition results, the best two groups of parallel transmission texts are locked from the corresponding multi-format texts and related markings are performed.

[0031] The transmission speed control end, based on the parallel transmission text determined by the multi-format text, transmits the parallel transmission text to the designated partition server end in a parallel transmission manner, and adjusts the bandwidth during the transmission process to change the transmission rate of the parallel transmission text;

[0032] The format conversion adapter converts the multi-format text into an associated format based on the associated format of the partition server after the multi-format text is transmitted, and obtains the patient information text that can be recognized by the partition server and displays it directly.

[0033] The present invention provides a patient information management method and system based on smart medical care. Compared with the prior art, it has the following beneficial effects:

[0034] The present invention is based on an interconnected system of smart medical care, confirms that the patient information text needs to be provided to multiple different server ends, and then converts the associated text of the patient information based on the associated formats of the multiple different server ends. When the corresponding classified text is converted into a format, the method with the shortest conversion time is adopted. Therefore, when the text is restored at a later stage, the restoration speed is still in the fastest restoration conversion mode, which can fully reduce the corresponding conversion time to ensure that the format conversion time of the corresponding information text can be fully reduced.

[0035] By identifying the relevant frequencies of the corresponding format data during the transmission process, correlation analysis is performed to identify whether the relevant texts of the corresponding format can be transmitted in parallel. If they can be directly transmitted in parallel, they are directly executed in the subsequent transmission method. If they cannot be transmitted in parallel, the original transmission method is used for transmission. This parallel transmission method can fully guarantee its transmission rate and reduce the transmission time.

[0036] Subsequently, the real-time bandwidth adjustment method can be used to ensure the transmission rate of the parallel text transmission, making the rate more consistent so that they can be completed at the same time during the transmission process, thereby ensuring the specific parallel transmission effect and reducing the transmission time. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the process of the present invention;

[0038] Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] See also Figure 1 , this application provides a patient information management method based on smart medical care, comprising the following steps:

[0041] Step 1: Based on the patient information generated by the corresponding medical patients, such patient information is preferentially transmitted to the cloud for associated storage, and then based on the multiple partitioned server ends involved in this smart medical treatment, the associated formats of multiple partitioned server ends are confirmed, and based on the confirmed associated formats, the information text of such patient information is initially classified, and then the format of the classified different classified texts is converted, and the multi-format text of this patient information is locked and stored. Specifically, after the relevant information of such patients is generated, in order to achieve a better smart medical data intercommunication effect, each different partitioned server end (which can be understood as different affiliated hospitals or different hospitals participating in smart medical treatment) can extract such patient information, so as to achieve the information intercommunication effect of multiple hospitals. The specific sub-steps of locking the multi-format text of this patient information are:

[0042] S11. Based on the determined association formats of the multiple partition servers, different formats of the multiple association formats are identified, and the identified different formats are marked as formats to be classified. The so-called different formats are different formats between the multiple association formats. For example, the multiple association formats are divided into A, A, B, B, C, and D. Six different association formats correspond to six different partition servers, but there are only four different formats in the six different partition servers, namely A, B, C, and D.

[0043] S12. Based on the specific number of formats to be classified, the information text of the patient information is divided into the same number of classified texts, and then the data format associated with the relevant classified texts is confirmed and marked as a text format, and a group of formats to be classified with the fastest conversion rate of the corresponding classified texts is selected in turn, and the converted multi-format text is stored, and the sorting method of the original classified text is kept unchanged during storage (that is, the classified texts will not be disrupted and the previous sorting method is still maintained), wherein the specific method of selecting the fastest conversion rate is:

[0044] S121, randomly selecting a group of classified texts from a plurality of classified texts as processing texts, and based on the text formats in the processed texts, identifying the conversion rate between the text format and the data format from the format conversion rate table and calibrating it as Vi-k , where i represents different text formats, k represents different data formats, and the format conversion rate table is a preset table. In this table, there are corresponding conversion rates between text formats and data formats, and the corresponding conversion rates are preset values, which are all prepared by operators in advance based on experience;

[0045] S122, identifying the text content capacity R corresponding to the text format i , using R i ÷V i-k =T i-k Determine the conversion time T i-k , and then identify the different T generated by other text formats in this processed text i-k , based on the label k (k represents different formats to be classified), different T i-k Perform summation and lock the sum value Z k , and then from multiple Z K Select the minimum value, use the k corresponding to the minimum value as the best classification format for this classification text, and directly perform text format conversion to confirm the format text of this classification text;

[0046] S123, for the remaining other classified texts, the same method as steps S121-S122 is adopted to confirm the format text of the classified text, and the k that has been selected as the best classification format is no longer selected;

[0047] S124, merging the generated multi-format texts according to the sorting method of the original classified texts and storing them in the cloud for text extraction by multiple different partition servers;

[0048] Combined with the example: it is proposed that there are three to-be-classified formats A, B and C, then the information text of the patient information is divided into three classification texts: W, E and R, it is proposed that there are two different file formats W1 and W2 in the W text content, then based on the corresponding preset table and the relevant capacity of the corresponding text content, the total time of the text formats W1 and W2 corresponding to the three different to-be-classified formats A, B and C can be locked, that is, the total value, and the minimum value is selected from the three groups of total values ​​to lock the best classification format of this classification text, assuming that the best classification format is B;

[0049] Then when the second classification text E is processed, the only classification formats to be classified for the associated conversion are A and C. Then the best classification format C can be selected in the same way;

[0050] Subsequently, the last classified text will only have one set of formats left, which can be directly associated and matched without analysis. The text format can be directly converted to determine the corresponding format text.

[0051] Since this information text needs to be provided to multiple different server ends, in order to fully reduce the format conversion time of the corresponding multi-format text, the information text can be preferentially stored with the corresponding format text of the same format as the corresponding server end (that is, corresponding to any format text in the format to be processed, corresponding to a group of related formats of the server end);

[0052] Subsequently, when the corresponding classified text is converted into a format, the method with the shortest conversion time is adopted. Therefore, when the text is restored later, the restoration speed is still in the fastest restoration conversion method, which can fully reduce the corresponding conversion time to ensure that the format conversion time of the corresponding information text can be fully reduced;

[0053] Step 2: Based on the associated formats corresponding to the multiple partitioned server ends, in order to prevent the subsequent texts of the corresponding associated formats from interfering with each other during transmission, the associated frequencies of the corresponding associated formats during the transmission process are identified, and then based on the identification results, the best two groups of parallel transmission texts are locked from the corresponding multi-format texts, and relevant markings are performed. Specifically, in order to ensure the subsequent data transmission efficiency, parallel transmission is generally adopted when performing text transmission. Therefore, in order to ensure that the corresponding two groups of parallel transmission texts can achieve better transmission effects during the parallel transmission process, the associated frequencies of the corresponding related data formats are identified from the historical completion data, and relevant matching and marking are performed to facilitate the subsequent specific transmission;

[0054] The specific sub-steps of locking the best two sets of parallel transmission texts are as follows:

[0055] S21, based on the determined multiple groups of associated formats, identify different formats between the multiple groups of associated formats (the associated formats may have the same format, so it is sufficient to determine different formats, and the same format does not need to be considered, which is also mentioned in step S11), and then identify the data frequencies of different formats during the data transmission process from the historical completed data or cloud data, select the minimum value and the maximum value from the identified multiple groups of data frequencies, and lock the frequency interval belonging to the corresponding format. Specifically, the numerical range of the frequency interval is generally very small, because the data frequencies of related data of different formats will not differ greatly when they are transmitted, so they generally fluctuate within a specified frequency interval;

[0056] S22, cross-analyzing multiple frequency intervals in different formats: preferentially sorting the frequency intervals in ascending order based on the median value of each frequency interval, and confirming a set of frequency intervals after sorting;

[0057] Starting from the first group of frequency intervals in the frequency interval set: take the first group of frequency intervals as the main interval, and preferentially select the last-to-last frequency interval in this frequency interval set for cross analysis with the main interval (the first group of frequency intervals) to determine the crossover range:

[0058] If the crossover range is located in the last group of frequency intervals and the proportion of the main interval is less than 20% (that is, the specific proportion of the corresponding crossover area in its original range area), the last group of frequency intervals selected will be used as the subsidiary interval of the main interval;

[0059] When there is a group of values ​​whose proportion is not less than 20%, or when both groups of values ​​are not less than 20%, the second-to-last frequency interval is selected, and the interval cross analysis is performed, and so on, until the subsidiary interval belonging to the main interval is determined. If it is not determined, the confirmation operation of this main interval is stopped, and the subsidiary intervals of other frequency intervals are confirmed in the same way (except for the frequency intervals that have been selected as subsidiary intervals and the frequency intervals with subsidiary intervals, the second group of frequency intervals are subsequently used as the main interval, and so on, and one by one is confirmed, in order to make the frequency difference within the corresponding frequency intervals larger. The larger the frequency difference, the lower the interference degree generated);

[0060] S23, confirming that there is an associated format corresponding to the frequency interval of the subsidiary interval, then determining the associated format corresponding to the subsidiary interval, calibrating the format texts corresponding to the two sets of associated formats as parallel transmission texts (there are two texts A and B, A is proposed to be the text associated with the format corresponding to the frequency interval, and B is the text associated with the subsidiary interval corresponding to the frequency interval);

[0061] Step 3: When the cloud receives a call instruction for specifying a multi-format text, based on the parallel transmission text determined by the multi-format text, the parallel transmission text is transmitted to the designated partition server end in a parallel transmission manner, and bandwidth adjustment is performed during the transmission process to change the transmission rate of the parallel transmission text so that the transmission rates of the two groups of parallel transmission texts are relatively consistent. The specific sub-steps of bandwidth adjustment are as follows:

[0062] S31, transmitting the determined parallel transmission texts simultaneously (the transmission is still the same transmission channel, but divided into two groups of channels, and one group of transmission texts in the parallel transmission texts is respectively associated and transmitted), and during the transmission process, the bandwidth of the original transmission channel is evenly divided into two groups of micro-bandwidths for use by the two groups of transmission texts in the parallel transmission texts;

[0063] S32, recording in real time the transmission rates V1 and V2 of the two groups of transmission texts in the parallel transmission text, if |V1-V2|≤Y1, no processing is required, wherein Y1 is a preset value, and its specific value is determined in advance by the operator based on experience;

[0064] If |V1-V2|>Y1, the maximum transmission rate is selected from V1 and V2, and the micro-bandwidth of the channel associated with the maximum transmission rate is gradually allocated to the micro-bandwidth of another channel until |V1-V2|≤Y1, so as to ensure that the transmission rates of the two parallel transmission texts are relatively consistent during text transmission;

[0065] After the parallel transmission of the text is completed, the other formats of the multi-format text are transmitted (single-channel single-text transmission). At this point, all the multi-format text is transmitted to the partition server end.

[0066] Specifically, through this real-time bandwidth adjustment method, the transmission rate of the parallel transmission text can be guaranteed to make the rate more consistent, so that they can be completed at the same time during the transmission process, thereby ensuring the specific parallel transmission effect and reducing the transmission time;

[0067] Step 4: After receiving the multi-format text, the designated partition server converts other formats in the multi-format text into the associated format according to the associated format of the partition server, obtains the patient information text that can be recognized by the partition server and directly displays it;

[0068] Specifically, the format restoration process here is relatively fast because the multi-format text already includes the associated formats that can be received by the corresponding partition server. Therefore, when performing format conversion, the speed is relatively fast and the time is relatively short, and the corresponding text information conversion process can be quickly completed.

[0069] Combination Figure 2 , patient information management system based on smart medical care, including:

[0070] The to-be-classified format confirmation terminal confirms the associated formats of the multiple partitioned server terminals based on the multiple partitioned server terminals involved in the smart medical care, and marks the identified different formats as to-be-classified formats based on the different formats existing in the multiple associated formats;

[0071] The format conversion storage end converts the format of the patient information text to be stored based on the determined format to be classified, and stores the converted multi-format text;

[0072] The parallel text marking end, based on the associated formats corresponding to the multiple partitioned server ends, identifies the associated frequencies of the corresponding associated formats during the transmission process so that the subsequent texts of the corresponding associated formats will not interfere with each other during transmission. Based on the recognition results, the best two groups of parallel transmission texts are locked from the corresponding multi-format texts and related markings are performed.

[0073] The transmission speed control end, based on the parallel transmission text determined by the multi-format text, transmits the parallel transmission text to the designated partition server end in a parallel transmission manner, and adjusts the bandwidth during the transmission process to change the transmission rate of the parallel transmission text;

[0074] The format conversion adapter converts the multi-format text into an associated format based on the associated format of the partition server after the multi-format text is transmitted, and obtains the patient information text that can be recognized by the partition server and displays it directly.

[0075] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0076] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A patient information management method based on smart medical care, characterized in that: The following steps are involved: Step 1: Based on the patient information generated by the corresponding medical patients, such patient information is preferentially transmitted to the cloud for associated storage, and then based on the multiple partitioned server ends involved in this smart medical care, the associated formats of multiple partitioned server ends are confirmed, and based on the confirmed associated formats, the information text of such patient information is initially classified, and then the format of the classified texts of different categories is converted, and the multi-format text of this patient information is locked and stored. The specific sub-steps are: S11, based on the determined association formats of the multiple partition servers, identifying different formats existing in the multiple association formats, and marking the identified different formats as formats to be classified; S12. Based on the specific number of formats to be classified, the information text of the patient information is divided into the same number of classified texts, and then the data format associated with the relevant classified texts is confirmed and marked as a text format. A group of formats to be classified with the fastest conversion rate of the corresponding classified texts is selected in turn, and the converted multi-format texts are stored. When storing, the sorting method of the original classified texts is kept unchanged. The specific method of selecting the fastest conversion rate is: S121, randomly selecting a group of classified texts from a plurality of classified texts as processing texts, and based on the text formats in the processed texts, identifying the conversion rate between the text format and the data format from the format conversion rate table and calibrating it as V i-k , where i represents different text formats, k represents different data formats, and the format conversion rate table is a preset table. The table contains corresponding conversion rates between text formats and data formats, and the corresponding conversion rates are preset values; S122, identifying the text content capacity R corresponding to the text format i , using R i ÷V i-k =T i-k Determine the conversion time T i-k , and then identify the different T generated by other text formats in this processed text i-k , based on the label k, different T i-k Perform summation and lock the sum value Z k , and then from multiple Z K Select the minimum value, use the k corresponding to the minimum value as the best classification format for this classification text, and directly perform text format conversion to confirm the format text of this classification text; S123, for the remaining other classified texts, the same method as steps S121-S122 is adopted to confirm the format text of the classified text, and the k that has been selected as the best classification format is no longer selected; S124, merging the generated multi-format texts according to the sorting method of the original classified texts and storing them in the cloud for text extraction by multiple different partition servers; Step 2: Based on the associated formats corresponding to the multiple partitioned servers, in order to prevent the subsequent texts of the corresponding associated formats from interfering with each other during transmission, the associated frequencies of the corresponding associated formats during transmission are identified, and then based on the identification results, the best two groups of parallel transmission texts are locked from the corresponding multi-format texts, and relevant markings are performed. The specific sub-steps are as follows: S21. Based on the determined multiple sets of associated formats, identify different formats between the multiple sets of associated formats, and then identify data frequencies of different formats in the data transmission process from historical completed data or cloud data, select a minimum value and a maximum value from the identified multiple sets of data frequencies, and lock the frequency interval belonging to the corresponding format; S22, cross-analyzing multiple frequency intervals in different formats: preferentially sorting the frequency intervals in ascending order based on the median value of each frequency interval, and confirming a set of frequency intervals after sorting; Starting from the first frequency interval of the frequency interval set: take the first frequency interval as the main interval, and preferentially select the last frequency interval in the frequency interval set for cross analysis with the main interval to determine the crossover range: If the crossover range is located in the last group of frequency intervals and the proportion of the main interval is less than 20%, the last group of frequency intervals selected will be used as the subsidiary interval of the main interval; When there is a group of values ​​whose proportion is not less than 20%, or when both groups of values ​​are not less than 20%, the second-to-last frequency interval is selected, and the interval cross analysis is performed, and so on, until the subsidiary interval belonging to the main interval is determined. If it is not determined, the confirmation operation of this main interval is stopped, and the subsidiary intervals of other frequency intervals are confirmed in the same way; S23, confirming that there is an associated format corresponding to the frequency interval of the subsidiary interval, then determining the associated format corresponding to the subsidiary interval, and calibrating the format texts corresponding to the two groups of associated formats as parallel transmission texts; Step 3: When the cloud receives a call instruction for specifying a multi-format text, based on the parallel transmission text determined by the multi-format text, the parallel transmission text is transmitted to the designated partition server end in a parallel transmission manner, and bandwidth is adjusted during the transmission process to change the transmission rate of the parallel transmission text. The specific sub-steps are: S31, transmitting the determined parallel transmission texts simultaneously, and during the transmission process, dividing the bandwidth of the original transmission channel into two groups of micro-bandwidths for use by the two groups of transmission texts in the parallel transmission texts; S32, recording in real time the transmission rates V1 and V2 of the two groups of transmission texts in the parallel transmission text, if |V1-V2|≤Y1, no processing is required, wherein Y1 is a preset value; If |V1-V2|>Y1, the maximum transmission rate is selected from V1 and V2, and the micro-bandwidth of the channel associated with the maximum transmission rate is gradually allocated to the micro-bandwidth of another channel until |V1-V2|≤Y1, so as to ensure that the transmission rates of the two parallel transmission texts are relatively consistent during text transmission; After the parallel transmission of the text is completed, the other formats of the multi-format text are transmitted, and then all the multi-format text is transmitted to the partition server end.

2. The patient information management method based on smart medical care according to claim 1, characterized in that: The following steps are also included: Step 4: After the designated partition server receives the multi-format text, it converts other formats within the multi-format text into associated formats according to the associated formats of the partition server, obtains the patient information text that can be recognized by the partition server, and displays it directly.

3. A patient information management system based on smart medical care, which is operated based on the patient information management method based on smart medical care according to any one of claims 1 to 2, characterized in that: include: The to-be-classified format confirmation terminal confirms the associated formats of the multiple partitioned server terminals based on the multiple partitioned server terminals involved in the smart medical care, and marks the identified different formats as to-be-classified formats based on the different formats existing in the multiple associated formats; The format conversion storage end converts the format of the patient information text to be stored based on the determined format to be classified, and stores the converted multi-format text; The parallel text marking end, based on the associated formats corresponding to the multiple partitioned server ends, identifies the associated frequencies of the corresponding associated formats during the transmission process so that the subsequent texts of the corresponding associated formats will not interfere with each other during transmission. Based on the recognition results, the best two groups of parallel transmission texts are locked from the corresponding multi-format texts and related markings are performed. The transmission speed control end, based on the parallel transmission text determined by the multi-format text, transmits the parallel transmission text to the designated partition server end in a parallel transmission manner, and adjusts the bandwidth during the transmission process to change the transmission rate of the parallel transmission text; The format conversion adapter converts the multi-format text into an associated format based on the associated format of the partition server after the multi-format text is transmitted, and obtains the patient information text that can be recognized by the partition server and displays it directly.

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