Mobile nurse station offline data processing method

Abstract

The invention discloses an off-line data processing method for a mobile nurse station, relates to the technical field of off-line data processing, and solves the technical problems of dynamic data uploading and non-uniform project operation and cloud time of the mobile nurse station when the network is unsmooth. According to the method, the operation recording time independent of terminal operation is set, and the recording time is used for being connected with the cloud time during data transmission; dynamically acquiring inspection data corresponding to the change data based on the geographic position, sending an operation instruction based on the current network, and performing corresponding operation based on the operation instruction; according to the invention, precise synchronization of the project operation time and the cloud time can be guaranteed when the terminal time changes, and the adaptivity of data uploading in an unstable network environment is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of smart medical care and relates to an offline data processing technology, in particular to an offline data processing method for a mobile nurse station. Background Art

[0002] Mobile nurse station is an information system widely used in the medical field. It provides a flexible working platform for nurses through mobile devices. This platform can support nurses to directly access patient information and record nursing operations in the ward, greatly improving the efficiency and accuracy of nursing work. However, in actual use, due to incomplete network coverage or equipment failure in the hospital, the mobile nurse station may encounter offline situations, which has a certain impact on nursing work.

[0003] At present, most mobile nurse station offline data processing methods cannot selectively transmit data when the network is not smooth, but only transmit all data to the cloud. This will cause some non-critical data to occupy network resources, causing delays in the transmission of critical data and increasing the pressure of data transmission. At the same time, most mobile nurse station offline data processing methods cannot accurately record the time of each project operation when the terminal time is changed, making it difficult to accurately synchronize the project operation time with the cloud time.

[0004] Therefore, the present invention discloses a mobile nurse station offline data processing method for solving the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an offline data processing method for a mobile nurse station, which is used to solve the technical problems of dynamic data uploading of the mobile nurse station when the network is not smooth and the inconsistency between project operation and cloud time. The present invention solves the above problems by setting an operation recording time independent of terminal operation, and using this recording time to connect with the cloud time when transmitting data; dynamically obtaining inspection data corresponding to the changed data based on the geographic location, issuing operation instructions based on the current network, and performing corresponding operations based on the operation instructions.

[0006] To achieve the above object, a first aspect of the present invention provides a mobile nurse station offline data processing method, comprising:

[0007] S1: User logs in and determines whether the network is normal when logging into the terminal; if yes, download reference data and data to be inspected from the cloud, store the reference data in the terminal according to the header table and the detail table, and enable the maintenance time of the terminal; if no, wait until the network is normal to download reference data and data to be inspected, store reference data, and enable the maintenance time; wherein, the reference data is the inspection data most recently updated in the cloud before this analysis, and the data to be inspected is the data that needs to be inspected set in the cloud;

[0008] S2: storing the examination data obtained by the user according to the data to be examined into the database, comparing the examination data with the reference data to obtain the comparison result, and judging whether there is a change head table in the comparison result; if yes, jumping to S3; if no, issuing a reminder to check the consistency of the data; wherein the comparison result includes the change head table corresponding to the different data and the same head table corresponding to the same data; the examination data includes the patient's physiological indicators or body part examination data;

[0009] S3: dynamically acquire target data based on geographic location, issue operation instructions based on the current network, and perform corresponding operations based on the operation instructions; wherein the target data is the inspection data corresponding to the change header table; the operation instructions include instruction one and instruction two.

[0010] Preferably, the terminal activation maintenance time includes:

[0011] Obtain the network time on the cloud, use the network time as the start time of the terminal timing, trigger the timer timing, and add the start time and the timing time of the timer to obtain the maintenance time;

[0012] Maintenance time is used as the recording time of terminal operations; terminal operations include operation execution, data entry, data review and data deletion.

[0013] It is worth noting that the present invention does not use the time provided by the terminal, but the system sets a maintenance time independently of the terminal, and in subsequent operations or analyses, the maintenance time is recorded as the only standard time; this is done because the terminal may have operations such as time modification that cause time changes. The terminal uses maintenance time to ensure that when the user adjusts the terminal time, it does not affect the business operation time, and avoids the problem of time not corresponding to the server time caused by using the terminal time when the network is not smooth.

[0014] Preferably, the comparing the inspection data with the reference data to obtain a comparison result includes:

[0015] Get the header table number corresponding to each header table of the inspection data, and mark the header tables with the same number in the inspection data and the reference data as the same-numbered header tables; analyze whether there are differences between the same-numbered header tables based on text recognition technology; if yes, mark the corresponding header table as a variable header table; if not, mark the corresponding header table as the same header table; wherein the header table number is manually set.

[0016] It is worth noting that the present invention uses a method of comparing same-header tables when performing data comparison, and marks different same-header tables as variable header tables. In subsequent information uploads, the present invention does not upload all inspection data, but only uploads the inspection data corresponding to the variable header table. This can reduce the amount of data uploaded, improve data transmission efficiency and utilization of network resources.

[0017] It is worth noting that, in the present invention, when screening different data, numbering is used to perform a horizontal comparison between the inspection data and the reference data of the same inspection index, which can improve the efficiency of the inspection data.

[0018] Preferably, dynamically acquiring target data based on geographic location includes:

[0019] Extracting the geographical location of the current terminal, extracting the historical frequency of the terminal acquiring the target data in the vicinity of the current geographical location, and summarizing the historical frequency into a historical frequency group; wherein the range size of the vicinity is obtained by manual setting;

[0020] Obtain the variance of the historical frequency group, and determine whether the variance is less than a frequency variance threshold; if yes, retain the data of the historical frequency group; if no, remove the value with the largest difference from the mode in the historical frequency group, and re-determine the variance until the variance of the historical frequency group is less than the frequency variance threshold, and retain the remaining data of the historical frequency group; wherein the frequency variance threshold is obtained through experience;

[0021] Mark the historical frequency with the largest value in the data retained by the historical frequency group as the maximum frequency DPL, mark the historical frequency with the smallest value in the data retained by the historical frequency group as the minimum frequency XPL, obtain the average value PJP of the data retained by the historical frequency group, and obtain the frequency PL of the target data based on the formula PL = (α1×DPL+α2×XPL+PJP) / 2, and obtain the target data based on the frequency PL; wherein α1 and α2 are proportional adjustment coefficients, and α1+α2=1, α1<α2.

[0022] It is worth noting that the frequency PL used in the present invention is obtained by integrating the maximum value, minimum value and average value of the values ​​in the data retained by the historical frequency group. This can make the obtained frequency PL data more accurate, provide different acquisition frequencies for different inspections, and increase the adaptability of the terminal to obtain data.

[0023] Preferably, the issuing of an operation instruction based on the current network includes:

[0024] Determine whether the current network is normal; if yes, issue command one; if no, issue command two.

[0025] Preferably, the performing corresponding operations based on the operation instructions includes:

[0026] When instruction 1 is received, the target data is uploaded to the cloud, and the reference data in the cloud is updated based on the uploaded target data;

[0027] When instruction 2 is received, the target data is saved in the database, the uploaded data is retrieved, the current network is judged, and the operation instruction is reissued.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The present invention sets an operation record time that is independent of the terminal operation, and uses this record time to connect with the cloud time when transmitting data; dynamically obtains the inspection data corresponding to the changed data based on the geographical location, issues operation instructions based on the current network, and performs corresponding operations based on the operation instructions, thereby solving the technical problems of dynamic data upload of the mobile nurse station when the network is not smooth and the inconsistency between project operation and cloud time; the present invention can ensure the accurate synchronization of project operation time and cloud time when the terminal time changes, and ensure the adaptability of data upload in an unstable network environment.

[0030] 2. The present invention uses a method of comparing the same header table when performing data comparison, and marks different same header tables as variable header tables. In the subsequent information upload, the present invention does not upload all the inspection data, but only uploads the inspection data corresponding to the variable header table. This can reduce the amount of data uploaded, improve data transmission efficiency and utilization of network resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 It is a schematic diagram of the operation steps of the present invention;

[0033] Figure 2 A schematic diagram of the operation steps for obtaining the comparison results of the present invention;

[0034] Figure 3 This is a schematic diagram of the operation steps for acquiring target data of the present invention. DETAILED DESCRIPTION

[0035] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0036] See also Figure 1-2 The first aspect of the present invention provides a method for processing offline data of a mobile nurse station, comprising:

[0037] S1: User logs in and determines whether the network is normal when logging into the terminal; if yes, download reference data and data to be inspected from the cloud, store the reference data in the terminal according to the header table and detail table, and enable the maintenance time of the terminal; if no, wait until the network is normal to download reference data and data to be inspected, store reference data, and enable the maintenance time; the reference data is the inspection data last updated on the cloud before this analysis, and the data to be inspected is the data set in the cloud that needs to be inspected;

[0038] S2: Store the examination data obtained by the user according to the data to be examined into the database, compare the examination data with the reference data to obtain the comparison result, and judge whether there is a change head table in the comparison result; if yes, jump to S3; if no, issue a reminder to check the consistency of the data; wherein the comparison result includes the change head table corresponding to the different data and the same head table corresponding to the same data; the examination data includes the patient's physiological indicators or body part examination data;

[0039] S3: dynamically acquire target data based on geographic location, issue operation instructions based on the current network, and perform corresponding operations based on the operation instructions; wherein the target data is the inspection data corresponding to the change header table; the operation instructions include instruction one and instruction two.

[0040] It should be noted that the header table and the detail table correspond one to one. The header table is a summary of important data of a certain inspection item, and the detail table is the detailed data corresponding to the header table.

[0041] Exemplarily, in this embodiment, the user logs into the terminal at 15:00;

[0042] The user logs in. Since the network is normal when logging in to the terminal, the reference data and the data to be inspected are downloaded from the cloud, and the reference data is stored in the terminal according to the header table and the detail table;

[0043] The network time on the cloud is obtained as 15:00, and 15:00 is used as the starting time of the terminal timing. The timer is triggered to start timing, and the maintenance time is obtained by adding 15:00 and the timing time of the timer. The maintenance time is used as the recording time of the terminal operation;

[0044] The examination data obtained by the user according to the data to be examined is stored in the database, and the head table number corresponding to each head table of the examination data is obtained, for example, the head table number of the heart rate is 1, and the head table number of the blood sugar is 2;

[0045] The header tables with the same number in the inspection data and the reference data are marked as the same header tables. When there are differences between the same header tables, the corresponding header tables are marked as changed header tables; when there are no differences between the same header tables, the corresponding header tables are marked as the same header tables;

[0046] Extract the geographical location of the current terminal, extract the historical frequency of the terminal obtaining the target data in the vicinity of the current geographical location, and summarize the historical frequency into a historical frequency group; obtain the variance of the historical frequency group, and determine whether the variance is less than the frequency variance threshold; if yes, retain the data of the historical frequency group; if no, remove the value with the largest difference from the mode in the historical frequency group, and re-judge the variance until the variance of the historical frequency group is less than the frequency variance threshold, and retain the remaining data of the historical frequency group;

[0047] Mark the historical frequency with the largest value in the data retained by the historical frequency group as the maximum frequency DPL, mark the historical frequency with the smallest value in the data retained by the historical frequency group as the minimum frequency XPL, obtain the average value PJP of the data retained by the historical frequency group, obtain the frequency PL of the target data based on the formula PL=(α1×DPL+α2×XPL+PJP) / 2, and obtain the target data based on the frequency PL;

[0048] Because the network corresponding to the current time is normal, instruction one is issued, and the system uploads the target data to the cloud, and updates the reference data in the cloud based on the uploaded target data.

[0049] The terminal activation maintenance time in this application includes:

[0050] Obtain the network time on the cloud, use the network time as the start time of the terminal timing, trigger the timer timing, and add the start time and the timer timing time to obtain the maintenance time;

[0051] Maintenance time is used as the recording time of terminal operations; terminal operations include operation execution, data entry, data review and data deletion.

[0052] In this application, the inspection data is compared with the reference data to obtain the comparison results, including:

[0053] Get the header table number corresponding to each header table of the inspection data, and mark the header tables with the same number in the inspection data and the reference data as the same-numbered header tables; analyze whether there are differences between the same-numbered header tables based on text recognition technology; if yes, mark the corresponding header table as a variable header table; if not, mark the corresponding header table as the same header table; wherein the header table number is manually set.

[0054] It should be noted that the present invention has numbered each inspection data during analysis, for example: the heart rate inspection data is numbered 1, and the blood sugar inspection data is numbered 2; because each head table represents the summary information of the current indicator inspection data, therefore under the restriction of such numbering, the head table number corresponding to the head table is consistent with the number of the inspection data, such as the heart rate head table is numbered 1, and the blood sugar head table is numbered 2.

[0055] See also Figure 3 , in this application, target data is dynamically acquired based on geographic location, including:

[0056] Extract the geographical location of the current terminal, extract the historical frequency of the terminal acquiring the target data in the vicinity of the current geographical location, and summarize the historical frequencies into a historical frequency group; wherein the range size of the vicinity is obtained by manual setting;

[0057] Obtain the variance of the historical frequency group and determine whether the variance is less than the frequency variance threshold; if yes, retain the data of the historical frequency group; if no, remove the value with the largest difference from the mode in the historical frequency group and re-judge the variance until the variance of the historical frequency group is less than the frequency variance threshold, and retain the remaining data of the historical frequency group; the frequency variance threshold is obtained through experience;

[0058] Mark the historical frequency with the largest value in the data retained by the historical frequency group as the maximum frequency DPL, mark the historical frequency with the smallest value in the data retained by the historical frequency group as the minimum frequency XPL, obtain the average value PJP of the data retained by the historical frequency group, and obtain the frequency PL of the target data based on the formula PL = (α1×DPL+α2×XPL+PJP) / 2, and obtain the target data based on the frequency PL; wherein α1 and α2 are proportional adjustment coefficients, and α1+α2=1, α1<α2.

[0059] It should be noted that the present invention uses a variance judgment method to remove fluctuation values ​​from the data in the historical frequency group. After removing these fluctuation values ​​that affect the analysis, the data obtained can effectively represent the frequency of obtaining the target data, providing data support for subsequent analysis.

[0060] It should be noted that the frequency of acquiring target data is every n seconds.

[0061] In this application, operation instructions are issued based on the current network, including:

[0062] Determine whether the current network is normal; if yes, issue command one; if no, issue command two.

[0063] In this application, corresponding operations are performed based on the operation instructions, including:

[0064] When instruction 1 is received, the target data is uploaded to the cloud, and the reference data in the cloud is updated based on the uploaded target data;

[0065] When instruction 2 is received, the target data is saved in the database, the uploaded data is retrieved, the current network is judged, and the operation instruction is reissued.

[0066] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0067] Working principle of the present invention:

[0068] The user logs in. When the network is normal during login, reference data and data to be inspected are downloaded from the cloud, and the reference data are stored in the terminal according to the header table and the detail table, and the terminal enables maintenance time. When the network is abnormal during login, the user waits for the network to be normal to download the reference data and data to be inspected, store the reference data, and enable maintenance time; the examination data obtained by the user based on the data to be inspected is stored in the database, and the examination data is compared with the reference data to obtain a comparison result. When there is a change in the comparison result, the header table is obtained; the target data is dynamically acquired based on the geographic location, and an operation instruction is issued based on the current network, and a corresponding operation is performed based on the operation instruction; when there is no change in the comparison result, a reminder to check the consistency of the data is issued.

[0069] 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 mobile nurse station offline data processing method, characterized in that: S1: The user logs in and determines whether the network is normal when logging into the terminal; if yes, download reference data and data to be inspected from the cloud, store the reference data in the terminal according to the header table and detail table, and enable the terminal maintenance time; No, wait until the network is normal to download the reference data and the data to be inspected, store the reference data, and enable the maintenance time; the reference data is the inspection data last updated on the cloud before this analysis, and the data to be inspected is the data that needs to be inspected set on the cloud; S2: storing the examination data obtained by the user according to the data to be examined into the database, comparing the examination data with the reference data to obtain the comparison result, and judging whether there is a change head table in the comparison result; if yes, jumping to S3; if no, issuing a reminder to check the consistency of the data; wherein the comparison result includes the change head table corresponding to the different data and the same head table corresponding to the same data; the examination data includes the patient's physiological indicators or body part examination data; S3: dynamically acquire target data based on geographic location, issue operation instructions based on the current network, and perform corresponding operations based on the operation instructions; wherein the target data is the inspection data corresponding to the change header table; the operation instructions include instruction one and instruction two.

2. A mobile nurse station offline data processing method according to claim 1, characterized in that: The terminal activation maintenance time includes: Obtain the network time on the cloud, use the network time as the start time of the terminal timing, trigger the timer timing, and add the start time and the timing time of the timer to obtain the maintenance time; Maintenance time is used as the recording time of terminal operations; terminal operations include operation execution, data entry, data review and data deletion.

3. A mobile nurse station offline data processing method according to claim 1, characterized in that: The step of comparing the inspection data with the reference data to obtain a comparison result includes: Get the header table number corresponding to each header table of the inspection data, and mark the header tables with the same number in the inspection data and the reference data as the same-numbered header tables; analyze whether there are differences between the same-numbered header tables based on text recognition technology; if yes, mark the corresponding header table as a variable header table; if not, mark the corresponding header table as the same header table; wherein the header table number is manually set.

4. A mobile nurse station offline data processing method according to claim 1, characterized in that: The dynamically acquiring target data based on the geographic location includes: Extracting the geographical location of the current terminal, extracting the historical frequency of the terminal acquiring the target data in the vicinity of the current geographical location, and summarizing the historical frequency into a historical frequency group; Obtain the variance of the historical frequency group, and determine whether the variance is less than the frequency variance threshold; if yes, retain the data of the historical frequency group; if no, remove the value with the largest difference from the mode in the historical frequency group, and re-determine the variance until the variance of the historical frequency group is less than the frequency variance threshold, and retain the remaining data of the historical frequency group; Mark the historical frequency with the largest value in the data retained by the historical frequency group as the maximum frequency DPL, mark the historical frequency with the smallest value in the data retained by the historical frequency group as the minimum frequency XPL, obtain the average value PJP of the data retained by the historical frequency group, and obtain the frequency PL of the target data based on the formula PL = (α1×DPL+α2×XPL+PJP) / 2, and obtain the target data based on the frequency PL; wherein α1 and α2 are proportional adjustment coefficients, and α1+α2=1, α1<α2.

5. A mobile nurse station offline data processing method according to claim 1, characterized in that: The issuing of an operation instruction based on the current network includes: Determine whether the current network is normal; if yes, issue command one; if no, issue command two.

6. A mobile nurse station offline data processing method according to claim 5, characterized in that: The performing corresponding operations based on the operation instructions includes: When instruction 1 is received, the target data is uploaded to the cloud, and the reference data in the cloud is updated based on the uploaded target data; When instruction 2 is received, the target data is saved in the database, the uploaded data is retrieved, the current network is judged, and the operation instruction is reissued.

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