Clinical nursing monitoring information input method, system and equipment

Abstract

The invention relates to the technical field of medical care information, in particular to a clinical nursing monitoring information input method, system and equipment, and the method comprises the steps: obtaining to-be-input clinical nursing monitoring information and historical clinical nursing monitoring information, and carrying out the word segmentation extraction of the monitoring information, the real-time segmented words of the to-be-input clinical nursing monitoring information and the historical segmented words of the historical clinical nursing monitoring information are obtained; the method comprises the following steps: analyzing real-time segmented words of to-be-input clinical nursing monitoring information and historical segmented words of historical clinical nursing monitoring information, screening out high-frequency segmented words in all real-time segmented words of descriptive information in the to-be-input clinical nursing monitoring information, and determining the to-be-encrypted degree of each high-frequency segmented word; determining an optimal encryption round number group of each high-frequency segmented word; and based on the optimal encryption round number group, encrypting all real-time segmented words in the to-be-input clinical nursing monitoring information, and inputting encrypted data. The encryption input efficiency of the clinical nursing monitoring information is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of healthcare information technology, and particularly to a method, system, and device for entering clinical nursing monitoring information. Background Art

[0002] The clinical nursing monitoring information of patients provides an important basis for medical decision-making. The clinical nursing monitoring information is often entered into the CIS clinical information system. The detailed and accurate entry of nursing monitoring information can reflect the changes in the patient's condition, treatment response, and the effect of nursing measures in real time. By consulting the records of nursing monitoring information, doctors can quickly understand the patient's nursing situation and thus formulate a more accurate treatment plan.

[0003] To ensure the privacy and security of patients' medical data, information encryption processing is usually required during the entry of clinical nursing monitoring information. The traditional AES encryption algorithm is often used to encrypt the content of clinical nursing monitoring information, and a fixed number of encryption rounds are usually used to encrypt all nursing monitoring information during the encryption process, so that all information is equally encrypted within a safe range. However, since the clinical nursing monitoring information usually includes multi-dimensional different types of information, and there are differences in the encryption requirements between different types of information, setting the same encryption level for all monitoring information by using a fixed number of encryption rounds will cause the consumption of redundant encryption computing resources and reduce the encryption entry efficiency of nursing monitoring information. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, system, and device for entering clinical nursing monitoring information, which is used to solve the problem that setting the same encryption level for all monitoring information by using a fixed number of encryption rounds in the prior art will reduce the encryption entry efficiency of nursing monitoring information.

[0005] To solve the above technical problems, in a first aspect, the present invention provides a method for entering clinical nursing monitoring information, including the following steps: Obtain the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information, perform word segmentation extraction on the clinical nursing monitoring information, so as to obtain the real-time word segmentation of the clinical nursing monitoring information to be entered and the historical word segmentation of the historical clinical nursing monitoring information; According to the occurrence frequency of each real-time word segmentation of the descriptive information in the clinical nursing monitoring information to be entered in all the historical word segmentations of the historical clinical nursing monitoring information, screen out the high-frequency word segmentations among all the real-time word segmentations of the descriptive information in the clinical nursing monitoring information to be entered; According to the similarity between the descriptive sentence segments where each high-frequency word segmentation is located in the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information, determine the word segmentation sensitivity of each high-frequency word segmentation; Determine the degree of encryption to be applied to each high-frequency word segment based on the sensitivity of each high-frequency word segment to word segmentation and the difference in the frequencies of occurrence of each high-frequency word segment among all word segments under the nursing information dimension to which the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information belong; Determine the optimal encryption round number array for each high-frequency word segment based on the degree of encryption to be applied to each high-frequency word segment and the frequency of occurrence of each high-frequency word segment among all word segments of the clinical nursing monitoring information to be encrypted; Encrypt all real-time word segments in the clinical nursing monitoring information to be entered, and during the encryption process, the encryption round of each high-frequency word segment is determined by its optimal encryption round number array, so as to obtain encrypted data and enter the encrypted data.

[0006] Combined with the first aspect above, in some possible implementation manners, screen out the high-frequency word segments among all real-time word segments, including: Determine the maximum frequency of occurrence of each real-time word segment of the descriptive information in the clinical nursing monitoring information to be entered among all historical word segments of the historical clinical nursing monitoring information to obtain the maximum frequency of occurrence; Determine the ratio of the frequency of occurrence of each real-time word segment of the descriptive information in the clinical nursing monitoring information to be entered among all historical word segments of the historical clinical nursing monitoring information to the maximum frequency of occurrence to obtain the high-frequency performance degree; Determine the real-time word segments of the descriptive information in the clinical nursing monitoring information to be entered with a high-frequency performance degree greater than the set high-frequency performance degree threshold as high-frequency word segments.

[0007] Combined with the first aspect above, in some possible implementation manners, determine the sensitivity of each high-frequency word segment to word segmentation, including: Determine the description consistency between each high-frequency word segment and each of its identical historical word segments according to the similarity between the description sentence segment where each high-frequency word segment is located in the clinical nursing monitoring information to be entered and the description sentence segment where each identical historical word segment is located in the historical clinical nursing monitoring information; Determine the confidence index of the description consistency between each high-frequency word segment and each of its identical historical word segments according to the similarity between the description sentence segment where each identical historical word segment of each high-frequency word segment is located in the historical clinical nursing monitoring information and the description sentence segments where other identical historical word segments are located; Use the confidence index to correct the description consistency between each high-frequency word segment and each of its identical historical word segments to determine the true description consistency between each high-frequency word segment and each of its identical historical word segments; Determine the sensitivity of each high-frequency word segment to word segmentation according to the average distribution level of the true description consistency between each high-frequency word segment and each of its identical historical word segments.

[0008] Combined with the above first aspect, in some possible implementation manners, determining the description consistency between each high-frequency word segment and each of its same historical word segments includes: According to the encoding values of each word segment in the description sentence segment where each high-frequency word segment is located in the clinical care monitoring information to be entered, constructing a first sentence segment word segment encoding value sequence, and according to the encoding values of each word segment in the description sentence segment where each same historical word segment of each high-frequency word segment is located in the historical clinical care monitoring information, constructing a second sentence segment word segment encoding value sequence; Determining the number of interval encoding values between the encoding value corresponding to each high-frequency word segment in the first sentence segment word segment encoding value sequence and the midpoint encoding value to obtain a first interval encoding value number; and determining the number of interval encoding values between the encoding value of each same historical word segment in the second sentence segment word sequence and the midpoint encoding value to obtain a second interval encoding value number; Determining the difference between the encoding value of each high-frequency word segment and the average value of the encoding values of all word segments in the first sentence segment word segment sequence to obtain a first encoding value difference, and determining the difference between the encoding value of each same historical word segment of each high-frequency word segment in the historical clinical care monitoring information and the average value of the encoding values of all word segments in the second sentence segment word segment sequence to obtain a second encoding value difference; According to the difference magnitude between the first interval encoding value number and the second interval encoding value number, and the difference magnitude between the first encoding value difference and the second encoding value difference, determining the description consistency between each high-frequency word segment and each of its same historical word segments.

[0009] Combined with the above first aspect, in some possible implementation manners, determining the confidence index of the description consistency between each high-frequency word segment and each same historical high-frequency word segment includes: Determining the distance value between the second sentence segment word segment encoding value sequence corresponding to each same historical high-frequency word segment of each high-frequency word segment and each other same historical high-frequency word segment; According to the average distribution level of the distance values corresponding to each same historical high-frequency word segment and all other same historical high-frequency word segments, determining the confidence index of the description consistency between each high-frequency word segment and each same historical high-frequency word segment.

[0010] Combined with the above first aspect, in some possible implementation manners, determining the degree of encryption to be applied to each high-frequency word segment includes: Determining the sum value of the frequency of occurrence of each high-frequency word segment among all word segments in the nursing information dimension to which it belongs in the historical clinical care monitoring information and a set frequency parameter; Determining the ratio of the frequency of occurrence of each high-frequency word segment among all word segments in the nursing information dimension to which it belongs in the clinical care monitoring information to be entered to the corresponding sum value to obtain the frequency ratio of each high-frequency word segment; Determine the product of the frequency ratio and the word segmentation sensitivity of each high-frequency word segment, so as to obtain the degree of encryption to be performed on each high-frequency word segment.

[0011] Combined with the above first aspect, in some possible implementation manners, determine the optimal encryption round number array for each high-frequency word segment, including: Divide the encryption round number range into several encryption round number intervals, each encryption round number interval corresponds to an encryption degree interval, and the larger the encryption round number in the encryption round number interval, the greater the encryption degree in the corresponding encryption degree interval; Determine the encryption degree interval to which the encryption degree to be performed on each high-frequency word segment belongs, and use the encryption round number interval corresponding to the belonging encryption degree interval as the target encryption round number interval; Determine several encryption round number arrays in the target encryption round number interval, and the number of encryption round numbers in the encryption round number array is equal to the frequency of each high-frequency word segment in all word segments of the clinical care monitoring information to be encrypted; According to the dispersion degree of all encryption round numbers in each encryption round number array, screen out the optimal encryption round number array from all encryption round number arrays.

[0012] Combined with the above first aspect, in some possible implementation manners, screen out the optimal encryption round number array from all encryption round number arrays, including: Determine the variance of all encryption round numbers in each encryption round number array; Determine the maximum variance among the variances corresponding to all encryption round number arrays; Use the encryption round number array corresponding to the maximum variance as the optimal encryption round number array.

[0013] To solve the above technical problems, in a second aspect, the present invention further provides a clinical care monitoring information input system, including a memory and a processor. The memory is used to store executable program codes, and the processor is used to call and run the executable program codes from the memory, so that the system executes the method in the above first aspect or any one of the possible implementation manners of the first aspect.

[0014] To solve the above technical problems, in a third aspect, the present invention further provides a clinical care monitoring information input device, and the device includes: A data acquisition module, configured to acquire the clinical care monitoring information to be input and the historical clinical care monitoring information, perform word segmentation extraction on the clinical care monitoring information, so as to obtain the real-time word segments of the clinical care monitoring information to be input and the historical word segments of the historical clinical care monitoring information; A high-frequency word segmentation acquisition module, which is used to screen out high-frequency word segments among all real-time word segments of the descriptive information in the clinical nursing monitoring information to be input according to the occurrence frequencies of each real-time word segment of the descriptive information in the clinical nursing monitoring information to be input in all historical word segments of the historical clinical nursing monitoring information; A word segmentation sensitivity acquisition module, which is used to determine the word segmentation sensitivity of each high-frequency word segment according to the similarity between the descriptive sentence segments where each high-frequency word segment is located in the clinical nursing monitoring information to be input and the historical clinical nursing monitoring information; An encryption degree to be determined acquisition module, which is used to determine the encryption degree to be determined for each high-frequency word segment according to the word segmentation sensitivity of each high-frequency word segment and the difference in the occurrence frequencies of each high-frequency word segment among all word segments under the nursing information dimension in the clinical nursing monitoring information to be input and the historical clinical nursing monitoring information; An optimal encryption round number array acquisition module, which is used to determine the optimal encryption round number array for each high-frequency word segment according to the encryption degree to be determined for each high-frequency word segment and the occurrence frequency of each high-frequency word segment among all word segments of the clinical nursing monitoring information to be encrypted; An encrypted input module, which is used to encrypt all real-time word segments in the clinical nursing monitoring information to be input, and the encryption round of each high-frequency word segment during the encryption process is determined by its optimal encryption round number array, so as to obtain encrypted data and input the encrypted data.

[0015] To solve the above technical problems, in a fourth aspect, the present invention further provides a computer program product, which includes: computer program code, when the computer program code runs on a computer, it causes the computer to execute the method in the first aspect or any possible implementation manner of the first aspect above.

[0016] To solve the above technical problems, in a fifth aspect, the present invention further provides a computer-readable storage medium, which stores computer program code, when the computer program code runs on a computer, it causes the computer to execute the method in the first aspect or any possible implementation manner of the first aspect above.

[0017] The present invention has the following beneficial effects: By obtaining the real-time word segmentation of the clinical nursing monitoring information to be entered and the historical word segmentation of the historical clinical nursing monitoring information, and based on the occurrence frequency of each real-time word segmentation of the descriptive information in the clinical nursing monitoring information to be entered among all the historical word segmentations of the historical clinical nursing monitoring information, high-frequency word segmentations are selected from all the real-time word segmentations of the descriptive information in the clinical nursing monitoring information to be entered; According to the similarity between the descriptive sentence segments where each high-frequency word segmentation is located in the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information, the word segmentation sensitivity degree of each high-frequency word segmentation is determined; According to the word segmentation sensitivity degree of each high-frequency word segmentation and the difference in the occurrence frequency of each high-frequency word segmentation among all the word segmentations under the nursing information dimension to which the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information belong, the degree of encryption to be performed on each high-frequency word segmentation is determined; According to the degree of encryption to be performed on each high-frequency word segmentation and the occurrence frequency of each high-frequency word segmentation among all the word segmentations of the clinical nursing monitoring information to be encrypted, the optimal encryption round number array of each high-frequency word segmentation is determined; Encrypt all the real-time word segmentations in the clinical nursing monitoring information to be entered, and during the encryption process, the encryption round of each high-frequency word segmentation is determined by its optimal encryption round number array, thereby obtaining encrypted data, and the encrypted data is entered. The present invention determines the degree of encryption to be performed on each high-frequency word segmentation according to the importance performance of each high-frequency word segmentation of the descriptive information in the clinical nursing monitoring information to be entered, and adaptively determines the encryption round of each high-frequency word segmentation according to the degree of encryption to be performed. Compared with the traditional encryption algorithm that uses a consistent encryption round for all nursing monitoring information, the encryption and entry efficiency of the clinical nursing monitoring information is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a step flow chart of a method for entering clinical nursing monitoring information according to an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a system for entering clinical nursing monitoring information according to an embodiment of the present invention; Figure 3 It is a structural schematic diagram of a device for entering clinical nursing monitoring information according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to clearly illustrate the technical features of the present solution, the present invention will be elaborated in detail below through specific embodiments in combination with the drawings.

[0021] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Instead, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0022] It should be understood that the various steps recited in the method embodiments of the present invention can be executed in a different order and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0023] As used herein, the term "comprising" and its variations are open-ended, i.e., "including but not limited to". The term "based on" is "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0024] It should be noted that the concepts such as "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order of the functions performed by these devices, modules or units or their interdependent relationships.

[0025] In the embodiments of the present invention, although the operations or steps are described in a specific order in the drawings, it should not be understood that these operations or steps are required to be executed in the specific order shown or in a serial order, or that all the operations or steps shown are required to be executed to obtain the desired result. In the embodiments of the present invention, these operations or steps can be executed serially; they can also be executed in parallel; or a part of these operations or steps can be executed.

[0026] At the same time, it can be understood that the data involved in the technical solution of the present invention (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of the corresponding laws, regulations and related provisions. Unless otherwise defined, all the technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs, and all the parameters or indicators in the formulas involved in the present invention are numerical values after normalization that eliminate the influence of dimensions.

[0027] To solve the problem that setting the same encryption level for all monitoring information with a fixed number of encryption rounds in the prior art will reduce the encryption input efficiency of nursing monitoring information, the embodiments of the present invention provide a method, a system and a device for inputting clinical nursing monitoring information. By identifying the high-frequency words in the clinical nursing monitoring information and adaptively determining the encryption rounds of different high-frequency words, the encryption input efficiency of the clinical nursing monitoring information is effectively improved.

[0028] Next, with reference to the accompanying drawings, a method, a system and a device for inputting clinical nursing monitoring information provided by the embodiments of the present invention will be introduced in detail.

[0029] Figure 1 The basic flowchart of a method for inputting clinical nursing monitoring information provided by the embodiments of the present invention is shown, as Figure 1 shown, the method specifically includes the following steps: Step S100: Obtain the clinical nursing monitoring information to be input and the historical clinical nursing monitoring information, perform word segmentation extraction on the clinical nursing monitoring information, so as to obtain the real-time word segmentation of the clinical nursing monitoring information to be input and the historical word segmentation of the historical clinical nursing monitoring information.

[0030] Specifically, the clinical nursing monitoring information of all patients to be input in each nursing department of the monitoring hospital is collected in real time, and these clinical nursing monitoring information to be input are preprocessed, so as to obtain the real-time word segmentation sequences of all patients in different dimensions. Among them, the preprocessing process includes: for the clinical nursing monitoring information of a single patient, since it includes nursing information in multiple dimensions such as vital signs, patient identity, diagnosis information, medication records, etc., the nursing information dimensions of the clinical nursing monitoring information of the single patient are distinguished; then for the clinical nursing monitoring information of the single patient in each dimension, Jieba word segmentation is used to perform word segmentation extraction on the information, and the extracted words are recorded as real-time word segmentation, and all the real-time word segmentation of each patient in each dimension are arranged in the order of speech, so as to obtain the real-time word segmentation sequence of each patient in each dimension.

[0031] At the same time, obtain the clinical nursing monitoring information of all patients in the monitoring hospital in the past period of time, and call these clinical nursing monitoring information historical clinical nursing monitoring information. According to the same preprocessing method as above, these historical clinical nursing monitoring information are preprocessed, and the words identified from these historical clinical nursing monitoring information during the preprocessing process are recorded as historical word segmentation, and then the historical word segmentation sequences of each patient in each dimension are obtained. The length of the past period of time can be reasonably set as needed. In this embodiment, the past period of time refers to the past three months.

[0032] Upload all the real-time word segmentation sequences of all patients corresponding to the to-be-entered clinical nursing monitoring information obtained above in each dimension, as well as the historical word segmentation sequences of all patients corresponding to the historical clinical nursing monitoring information in each dimension to the data acquisition system for subsequent processing.

[0033] Step S200: Screen out the high-frequency word segments among all the real-time word segments of the descriptive information in the to-be-entered clinical nursing monitoring information according to the occurrence frequencies of each real-time word segment of the descriptive information in the to-be-entered clinical nursing monitoring information among all the historical word segments of the historical clinical nursing monitoring information.

[0034] Specifically, in this embodiment, the encryption rounds are divided into three preset levels. The highest level is 50 times, the lowest level is 10 times, and the middle level in the range of (10, 50) times is the adaptive level. Since the clinical nursing monitoring information includes both digital-form information and descriptive information, where the digital-form information may be the patient's ID card information or transaction information, the highest encryption level should be set. And among the descriptive information, the high-frequency word segments are more important than the non-high-frequency word segments because the word segments with higher occurrence frequencies are more likely to be the symptoms repeatedly presented by the patient or the nursing measures repeatedly emphasized by the doctor. Therefore, the digital-form information is placed in the highest level, the non-high-frequency word segments in the descriptive information are placed in the lowest level, and the high-frequency word segments are placed in the middle adaptive level.

[0035] Therefore, determine the descriptive information in the to-be-entered clinical nursing monitoring information, which refers to the non-digital-form information, and obtain each real-time word segment of this descriptive information. According to the occurrence frequencies of each real-time word segment among all the historical word segments of the historical clinical nursing monitoring information, judge the high-frequency performance degree of each real-time word segment, so as to extract the high-frequency word segments.

[0036] Further, the implementation steps of screening out the high-frequency word segments among all the real-time word segments of the descriptive information in the to-be-entered clinical nursing monitoring information according to the occurrence frequencies of each real-time word segment of the descriptive information in the to-be-entered clinical nursing monitoring information among all the historical word segments of the historical clinical nursing monitoring information include: Determine the maximum value of the occurrence frequencies of each real-time word segment of the descriptive information in the to-be-entered clinical nursing monitoring information among all the historical word segments of the historical clinical nursing monitoring information to obtain the maximum occurrence frequency; Determine the ratio of the occurrence frequency of each real-time word segment of the descriptive information in the to-be-entered clinical nursing monitoring information among all the historical word segments of the historical clinical nursing monitoring information to the maximum occurrence frequency to obtain the high-frequency performance degree; Determine the real-time word segments of the descriptive information in the to-be-entered clinical nursing monitoring information with a high-frequency performance degree greater than the set high-frequency performance degree threshold as high-frequency word segments.

[0037] For the above steps, the real-time word segmentation sequence corresponding to the clinical nursing monitoring information to be entered and the historical word segmentation sequence corresponding to the historical clinical nursing monitoring information word segmentation are read through the data acquisition system. In the real-time word segmentation sequence corresponding to the clinical nursing monitoring information to be entered, for a single real-time word segmentation corresponding to the descriptive information, obtain the frequency of occurrence of this real-time word segmentation in all historical word segmentations of the historical clinical nursing monitoring information, and determine the maximum value of the frequencies of occurrence of all real-time word segmentations corresponding to the descriptive information in the historical word segmentations. Since the frequency level of a real-time word segmentation in the historical word segmentations can reflect the high-frequency performance of this word segmentation, the ratio of the frequency of occurrence of a single real-time word segmentation to the maximum value of the frequencies of occurrence of all real-time word segmentations in the historical word segmentations is calculated, and this ratio is used as the high-frequency performance degree of a single real-time word segmentation. Denote the frequency of occurrence of the th real-time word segmentation in the historical word segmentations as , denote the maximum value of the frequencies of occurrence of all real-time word segmentations in the historical word segmentations as , then the high-frequency performance degree of this th real-time word segmentation.

[0038] A high-frequency performance degree threshold is preset, and the high-frequency performance degrees of each real-time word segmentation corresponding to the descriptive information in the clinical nursing monitoring information to be entered are compared with this preset high-frequency performance degree threshold. The real-time word segmentations corresponding to the descriptive information in the clinical nursing monitoring information to be entered with a high-frequency performance degree greater than the preset high-frequency performance degree threshold are determined as high-frequency word segmentations, and thus the high-frequency word segmentations among all real-time word segmentations can be screened out. Among them, the specific value of the preset high-frequency performance degree threshold can be reasonably set according to needs. In this embodiment, the value of the preset high-frequency performance degree threshold is set to 0.63.

[0039] Step S300: Determine the word segmentation sensitivity of each high-frequency word segmentation according to the similarity between the descriptive sentence segments where each high-frequency word segmentation is located in the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information.

[0040] Specifically, in order to further determine the specific encryption round range of each high-frequency word segmentation in the adaptive file, considering that in addition to the symptoms that may repeatedly appear in the clinical nursing monitoring information and need to be encrypted with emphasis or the nursing measures repeatedly emphasized by doctors, high-frequency word segmentations may also be well-known words commonly used in some medical fields. The former is related to patient privacy and requires more encryption rounds, while the latter is common knowledge and should be encrypted with relatively fewer rounds than the former. And the high-frequency word segmentations representing common knowledge will have similar sentence segment description performances in the historical clinical nursing monitoring information. Therefore, the word segmentation sensitivity can be obtained according to the comparison characteristics between the high-frequency word segmentations and the historical clinical nursing monitoring information, and then the encryption round array of the current high-frequency word segmentation can be obtained by combining the importance differences between the information dimensions to which the high-frequency word segmentations belong.

[0041] Further, the step of determining the sensitivity degree of each high-frequency word segment according to the similarity between the description sentence segments where each high-frequency word segment is located in the clinical nursing monitoring information to be input and the historical clinical nursing monitoring information includes: Determine the description consistency between each high-frequency word segment and each of its same historical word segments according to the similarity between the description sentence segment where each high-frequency word segment is located in the clinical nursing monitoring information to be input and the description sentence segment where each same historical word segment is located in the historical clinical nursing monitoring information; Determine the confidence index of the description consistency between each high-frequency word segment and each of its same historical word segments according to the similarity between each same historical word segment of each high-frequency word segment in the historical clinical nursing monitoring information and the description sentence segments where other same historical word segments are located; Use the confidence index to correct the description consistency between each high-frequency word segment and each of its same historical word segments, and determine the true description consistency between each high-frequency word segment and each of its same historical word segments; Determine the sensitivity degree of each high-frequency word segment according to the average distribution level of the true description consistency between each high-frequency word segment and each of its same historical word segments.

[0042] For the above steps, considering that the encryption degree of the word segments corresponding to the well-known medical knowledge among all high-frequency word segments should be lower than that of other high-frequency word segments, and due to the rigor of medical terms, the description usage of the word segments corresponding to well-known knowledge often shows strong consistency in sentence segments, so calculate the nursing description consistency between the high-frequency word segments and the same historical word segments in the historical clinical nursing monitoring information to distinguish the high-frequency word segments corresponding to well-known knowledge.

[0043] Taking the th high-frequency word segment of the descriptive information in the clinical nursing monitoring information to be input as an example, extract the sentence segment where the th high-frequency word segment is located, and use this sentence segment as the description sentence segment where the th high-frequency word segment is located. At the same time, extract each same historical word segment of the th high-frequency word segment in the historical clinical nursing monitoring information. Each same historical word segment refers to the word segment with the same coding value as the th high-frequency word segment, and extract the sentence segment where each same historical word segment is located in the historical clinical nursing monitoring information, and use this sentence segment as the description sentence segment where each same historical word segment is located. When the similarity between the description sentence segment where the th high-frequency word segment is located in the clinical nursing monitoring information to be input and the description sentence segments where each same historical word segment is located in the historical clinical nursing monitoring information is higher, it indicates that the th high-frequency word segment is more likely to be a word segment corresponding to well-known knowledge.

[0044] Further, the description consistency between each high-frequency word segment and its corresponding identical historical word segment is determined based on the similarity between the description sentence segments where each high-frequency word segment is located in the clinical care monitoring information to be entered and the description sentence segments where each identical historical word segment is located in the historical clinical care monitoring information, including: constructing a first sentence segment word segment coding value sequence according to the coding values of each word segment in the description sentence segment where each high-frequency word segment is located in the clinical care monitoring information to be entered, and constructing a second sentence segment word segment coding value sequence according to the coding values of each word segment in the description sentence segment where each identical historical word segment of each high-frequency word segment is located in the historical clinical care monitoring information; determining the number of interval coding values between the coding value corresponding to each high-frequency word segment in the first sentence segment word segment coding value sequence and the midpoint coding value to obtain a first number of interval coding values; and determining the number of interval coding values between the coding value of each identical historical word segment in the second sentence segment word sequence and the midpoint coding value to obtain a second number of interval coding values; determining the difference between the coding value of each high-frequency word segment and the average value of the coding values of all word segments in the first sentence segment word sequence to obtain a first coding value difference, and determining the difference between the coding value of each identical historical word segment of each high-frequency word segment in the historical clinical care monitoring information and the average value of the coding values of all word segments in the second sentence segment word sequence to obtain a second coding value difference; determining the description consistency between each high-frequency word segment and its corresponding identical historical word segment according to the difference magnitude between the first number of interval coding values and the second number of interval coding values, and the difference magnitude between the first coding value difference and the second coding value difference.

[0045] For the above steps, taking the th high-frequency word segment in the descriptive information of the clinical care monitoring information to be entered as an example, identifying each word segment in the description sentence segment where the th high-frequency word segment is located in the clinical care monitoring information to be entered, determining the coding values of each word segment, and arranging the coding values of each word segment in the order of the word segments in the sentence segment, so as to obtain a first sentence segment word segment coding value sequence. In this embodiment, the word vectors of each word segment are obtained through the word2vec algorithm, and one-dimensional numerical processing is performed on the word vectors, that is, the word vectors are normalized, so as to obtain the coding values of each word segment. At the same time, in the same way, identify each word segment in the description sentence segment where each identical historical word segment of the th high-frequency word segment is located in the historical clinical care monitoring information, and obtain a second sentence segment word segment coding value sequence corresponding to each identical historical word segment.

[0046] Determine the th in the first sentence segment word segment coding value sequence corresponding to the The number of interval coding values between the coding value corresponding to a high-frequency word segment and the midpoint coding value in the first sentence segment word segment coding value sequence is called the first interval coding value quantity, denoted as ; Determine the average value of all coding values in the first sentence segment word segment coding value sequence to obtain the coding value mean, and extract the difference between the coding value corresponding to the high-frequency word segment and the coding value mean, and use this difference as the first coding value difference, denoted as .

[0047] In the same way, for each second sentence segment word segment coding value sequence corresponding to the same historical word segments of this th high-frequency word segment, taking the second sentence segment word segment coding value sequence corresponding to the th same historical word segment as an example, determine the th number of interval coding values between the coding value corresponding to the th same historical word segment in the second sentence segment word segment coding value sequence and the midpoint coding value in the second sentence segment word segment coding value sequence, and call this number of interval coding values the second interval coding value quantity, denoted as ; Determine the average value of all coding values in the second sentence segment word segment coding value sequence to obtain the coding value mean, and extract the difference between the coding value corresponding to the same historical word segment and the coding value mean, and use this difference as the second coding value difference, denoted as .

[0048] If the differences between the th high-frequency word segment and its th same historical word segment in the sentence segment word segment coding value sequence and , and and are all small, it means that the descriptions of the th high-frequency word segment and its th same historical word segment in the sentence segment are more similar, indicating that the description usage is more consistent. Thus, the description consistency between the th high-frequency word segment and its th same historical word segment can be obtained.

[0049] In this embodiment, the description consistency between the th high-frequency word segment and its th same historical word segment ; In the formula: represents the The number of first interval coding values corresponding to a high-frequency word segment; Indicates the th The number of second interval coding values corresponding to the same historical word segments of the high-frequency word segment; Indicates the First coding value difference corresponding to the high-frequency word segment; Indicates the th Second coding value difference corresponding to the same historical word segments of the high-frequency word segment; And Both represent the denominator correction parameter, used to prevent the denominator from being taken as 0. In this embodiment, is set, .

[0050] In the same way as above, the description consistency between each high-frequency word segment and each of its same historical word segments can be determined.

[0051] Meanwhile, for the set of same historical word segments corresponding to a single high-frequency word segment, if the performance of the sentence segment to which a certain same historical word segment belongs is more similar to the average level of the performance of the sentence segments to which other same historical word segments in the set of same historical word segments belong, it indicates that the universality of this same historical word segment is higher, and the confidence level of the nursing description consistency obtained from this same historical word segment is higher. At this time, combining the sentence segment similarity between each high-frequency word segment and each of its same historical word segments, the word segment sensitivity of each high-frequency word segment can be obtained.

[0052] Furthermore, the confidence index for determining the description consistency between each high-frequency word segment and each of its same historical word segments according to the similarity between each same historical word segment of each high-frequency word segment in the historical clinical nursing monitoring information and the description sentence segments where other same historical word segments are located includes: determining the distance value between the second sentence segment word segment coding value sequences corresponding to each same historical high-frequency word segment of each high-frequency word segment and each other same historical high-frequency word segment; according to the average distribution level of the distance values corresponding to each same historical high-frequency word segment and all other same historical high-frequency word segments, determining the confidence index for the description consistency between each high-frequency word segment and each same historical high-frequency word segment.

[0053] For the above steps, taking the th same historical word segment corresponding to the th high-frequency word segment as an example, calculate the average value of the distance values between the second sentence segment word segment coding value sequence of the th same historical word segment and the second sentence segment word segment coding value sequences of all other same historical word segments of the The mean of the distance values is denoted as . If the mean is smaller, it indicates that the universality of the th identical historical word segment is higher. Therefore, a higher confidence can be given to the description consistency obtained from the th identical historical word segment and the th high-frequency word segment, that is, the larger the value of the confidence index for the description consistency between the th high-frequency word segment and its th identical historical word segment. In this embodiment, the sum value of the mean and the correction value is determined, and the reciprocal of this sum value is used as the confidence index for the description consistency between the th high-frequency word segment and its th identical historical word segment.

[0054] In the above manner, the confidence index for the description consistency between each high-frequency word segment and each of its identical historical word segments can be determined. The description consistency between each high-frequency word segment and each of its identical historical word segments is corrected using the confidence index, that is, the product of the confidence index and the corresponding description consistency is calculated, and this product is used as the true description consistency. Thus, the true description consistency between each high-frequency word segment and each of its identical historical word segments can be determined.

[0055] The average value of the true description consistency between each high-frequency word segment and each of its identical historical word segments is determined to obtain the true description consistency mean. Since the lower the true nursing description consistency between a high-frequency word segment and all of its identical historical word segments, the lower the possibility that this high-frequency word segment is common medical knowledge, and the higher the sensitivity of this high-frequency word segment. Therefore, the word segmentation sensitivity of each high-frequency word segment can be determined based on the true description consistency mean between each high-frequency word segment and each of its identical historical word segments.

[0056] In this embodiment, taking the th high-frequency word segment as an example, the word segmentation sensitivity of this th high-frequency word segment corresponds to the following calculation formula: ; In the formula: represents the average value of the true description consistency between the th high-frequency word segment and each of its identical historical word segments, that is, the true description consistency mean; represents the standard normalization function, which is used to limit the value range of to the range of (0, 1); represents the denominator correction parameter, which is used to prevent the denominator from taking the value of 0, .

[0057] In the above manner, the word segmentation sensitivity of all high-frequency words can be determined.

[0058] Step S400: Determine the degree of encryption to be applied to each high-frequency word based on the word segmentation sensitivity of each high-frequency word and the difference in the frequencies of occurrence of each high-frequency word among all the words in the dimension of nursing information to which they belong in the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information.

[0059] Specifically, there are differences in the encryption corresponding to high-frequency words in clinical nursing monitoring information of different dimensions. For example, the encryption for the dimension representing patient personal identity information should be higher than that for the diagnosis information dimension. Moreover, compared with high-frequency words in other dimensions, the high-frequency characteristics of high-frequency words in the dimension representing identity information are mainly manifested in the real-time clinical nursing monitoring information and less in the historical clinical nursing monitoring information. Therefore, based on the word segmentation sensitivity of each high-frequency word, the degree of encryption to be applied to the high-frequency word can be further obtained by combining the dimensional differences of the high-frequency words.

[0060] Furthermore, the above step of determining the degree of encryption to be applied to each high-frequency word based on the word segmentation sensitivity of each high-frequency word and the difference in the frequencies of occurrence of each high-frequency word among all the words in the dimension of nursing information to which they belong in the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information, the implementation steps include: Determine the sum of the frequency of occurrence of each high-frequency word among all the words in the dimension of nursing information to which it belongs in the historical clinical nursing monitoring information and the set frequency parameter; Determine the ratio of the frequency of occurrence of each high-frequency word among all the words in the dimension of nursing information to which it belongs in the clinical nursing monitoring information to be entered to the corresponding sum value, to obtain the frequency ratio of each high-frequency word; Determine the product of the frequency ratio of each high-frequency word and the word segmentation sensitivity, so as to obtain the degree of encryption to be applied to each high-frequency word.

[0061] Regarding the above steps, taking the th high-frequency word as an example, extract the frequency of occurrence of this th high-frequency word among all the words in the dimension of nursing information to which it belongs in the clinical nursing monitoring information to be entered , and extract the frequency of occurrence of this th high-frequency word among all the words in the dimension of nursing information to which it belongs in the historical clinical nursing monitoring information . If the frequency of occurrence of this th high-frequency word in the dimension of nursing information to which it belongs in the clinical nursing monitoring information to be entered is more than the frequency of occurrence in the historical clinical nursing monitoring information , it indicates that this The more likely a high-frequency word segment is personal identity information of a patient, the higher the degree of encryption required for it.

[0062] In this embodiment, taking the th high-frequency word segment as an example, the degree of encryption required for the th high-frequency word segment corresponds to the following calculation formula: ; In the formula: represents the frequency of occurrence of the th high-frequency word segment among all word segments in the nursing information dimension to which the clinical nursing monitoring information to be entered belongs; represents the frequency of occurrence of the th high-frequency word segment among all word segments in the nursing information dimension to which the historical clinical nursing monitoring information belongs; represents a set frequency parameter used to prevent the denominator from being 0. In this embodiment, is set; represents a standard normalization function used to limit the value range to the range of (0, 1); represents the sensitivity degree of the th high-frequency word segment.

[0063] In the above manner, the degree of encryption required for all high-frequency word segments can be determined.

[0064] Step S500: Determine the optimal encryption round number array for each high-frequency word segment according to the degree of encryption required for each high-frequency word segment and the frequency of occurrence of each high-frequency word segment among all word segments of the clinical nursing monitoring information to be encrypted.

[0065] For each high-frequency word segment to be encrypted, the higher its degree of encryption required, the larger the corresponding encryption round number should be. At the same time, since high-frequency word segments are repeated word segments, in order to prevent the situation where a single high-frequency word segment is decrypted and exposes all high-frequency word segments of this type, the encryption round numbers between high-frequency word segments should be made as different as possible. At this time, each high-frequency word segment will correspond to an optimal encryption round number array composed of multiple encryption round numbers.

[0066] Furthermore, the above-mentioned step of determining the optimal encryption round number array for each high-frequency word segment according to the degree of encryption required for each high-frequency word segment and the frequency of occurrence of each high-frequency word segment among all word segments of the clinical nursing monitoring information to be encrypted includes the following implementation steps: Divide the encryption round number range into several encryption round number intervals. Each encryption round number interval corresponds to an encryption degree interval. The larger the encryption round number in the encryption round number interval, the higher the encryption degree in the corresponding encryption degree interval; Determine the encryption degree interval to which the encryption degree to be encrypted of each high-frequency word segment belongs, and use the corresponding encryption round interval of the encryption degree interval as the target encryption round interval; Determine several encryption round arrays within the target encryption round interval. The number of encryption rounds in the encryption round array is equal to the frequency of occurrence of each high-frequency word segment among all the word segments in the clinical nursing monitoring information to be encrypted; According to the degree of dispersion of all the encryption round numbers in each encryption round array, select the optimal encryption round array from all the encryption round arrays.

[0067] For the above steps, in this embodiment, the total encryption degree interval (0, 1) is divided into four encryption degree intervals: (0, 0.25), [0.25, 0.5), [0.5, 0.75), [0.75, 1). At the same time, the encryption round range (10, 50) of the adaptive gear is also evenly divided into four encryption round intervals, namely (10, 20), [20, 30), [30, 40), [40, 50). At this time, each encryption round interval corresponds to an encryption degree interval. The larger the encryption round in the encryption round interval, the greater the encryption degree in the corresponding encryption degree interval. For example, the encryption degree interval [0.75, 1) corresponds to the encryption round interval [40, 50).

[0068] Taking the th high-frequency word segment as an example, determine the encryption degree interval to which the encryption degree to be encrypted of the th high-frequency word segment belongs, and use the corresponding encryption round interval of the encryption degree interval as the target encryption round interval. Extract the frequency of occurrence of the th high-frequency word segment among all the word segments in the clinical nursing monitoring information to be encrypted , taking the target encryption round interval corresponding to the th high-frequency word segment as [0.75, 1) as an example, generate random numbers at one time within the encryption round interval [40, 50) through the random number generation algorithm. Take all the random numbers generated at one time for the th high-frequency word segment as an encryption round array. In this embodiment, the number of groups of the encryption round array for a single high-frequency word segment is the preset value 8.

[0069] In the above manner, multiple encryption round arrays for each high-frequency word segment can be determined. Since the high-frequency word segments are repeated word segments, in order to improve the encryption effect, the encryption rounds at different positions of the high-frequency word segment should be as discretely distributed as possible. Therefore, it is necessary to select the optimal encryption round array according to the degree of dispersion of multiple groups of encryption round arrays of the high-frequency word segment.

[0070] Further, screening out the optimal encryption round number group from all encryption round number groups according to the dispersion degree of all encryption round numbers in each encryption round number group includes: determining the variance of all encryption round numbers in each encryption round number group; determining the maximum variance among the variances corresponding to all encryption round number groups; and taking the encryption round number group corresponding to the maximum variance as the optimal encryption round number group.

[0071] For the sake of easy understanding, taking the th high-frequency word segmentation's th encryption round number group as an example, calculate the mean value of each encryption round data , and denote the value of the th high-frequency word segmentation's th encryption round number group at the cth encryption round number as , and there are a total of values inside the array. If the dispersion degree of each encryption round number value in a single encryption round number group of high-frequency word segmentation is greater, then the matching degree of this group is higher. Therefore, calculate the encryption matching degree of the th encryption round number group of the th high-frequency word segmentation. For all encryption round number groups of the th high-frequency word segmentation, select the group with the highest encryption matching degree as the optimal encryption round number group.

[0072] In the above manner, the optimal encryption round number group of each high-frequency word segmentation can be determined.

[0073] Step S600: Encrypt all real-time word segmentations in the clinical nursing monitoring information to be entered, and during the encryption process, the encryption rounds of each high-frequency word segmentation are determined by its optimal encryption round number group, so as to obtain encrypted data, and enter the encrypted data.

[0074] Specifically, for all the word segments in the real-time word segmentation sequences of each patient in each dimension of the clinical nursing monitoring information to be entered, AES encryption processing is performed according to the allocated number of encryption rounds to obtain the ciphertext of the clinical nursing information. Among them, during the AES encryption processing, for multiple word segments at different positions in each high-frequency word segment in the real-time word segmentation sequence, the encryption rounds in the optimal encryption round array are randomly and non-repeatedly mapped, that is, each word segment randomly determines an encryption round from its optimal encryption round array, and the encryption rounds determined by different word segments are different, thereby realizing the adaptability of the encryption rounds of each high-frequency word segment in the clinical nursing monitoring information to be entered. The ciphertext of the clinical nursing information is compressed and stored in the hospital CIS clinical information system. After the encryption entry of the current batch of clinical nursing monitoring information to be entered in the hospital is completed, the same encryption entry process is performed on the latest batch of clinical nursing monitoring information to be entered that is updated in real time, so as to complete the complete entry process of the clinical nursing monitoring information.

[0075] A method for entering clinical nursing monitoring information provided in this embodiment can adaptively determine the encryption rounds for each high-frequency word segment in the clinical nursing monitoring information to be entered by determining the matching encryption round array. Compared with the traditional encryption algorithm that uses a consistent encryption round for all nursing monitoring information, it can determine the degree of encryption to be performed on each high-frequency word segment in combination with the importance performance of the high-frequency word segments in the patient's clinical nursing monitoring information, and adaptively determine the encryption rounds for each high-frequency word segment according to the degree of encryption to be performed, effectively improving the encryption entry efficiency of the clinical nursing monitoring information.

[0076] Based on the same inventive concept, an embodiment of the present invention also provides a system for entering clinical nursing monitoring information, as Figure 2 shown. The system includes: a memory 201, a processor 202, and a computer program 203 stored in the memory 201 and running on the processor 202. Among them, when the processor 202 executes the computer program 203, the system can execute any one of the clinical nursing monitoring information entry methods described above.

[0077] Embodiments of the present invention can divide the functions of the system according to the above method examples. For example, corresponding to each function module, or two or more functions can be integrated into one processing module, and the above integrated modules can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is illustrative, only a logical function division, and there may be other division methods in actual implementation.

[0078] Based on the same inventive concept, an embodiment of the present invention also provides a device for entering clinical nursing monitoring information, as Figure 3 shown. The device includes: A data acquisition module, configured to acquire the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information, perform word segmentation extraction on the clinical nursing monitoring information, so as to obtain the real-time word segmentation of the clinical nursing monitoring information to be entered and the historical word segmentation of the historical clinical nursing monitoring information; A high-frequency word segmentation acquisition module, configured to screen out the high-frequency word segmentation in all the real-time word segmentation of the descriptive information in the clinical nursing monitoring information to be entered according to the occurrence frequency of each real-time word segmentation of the descriptive information in the clinical nursing monitoring information to be entered in all the historical word segmentation of the historical clinical nursing monitoring information; A word segmentation sensitivity acquisition module, configured to determine the word segmentation sensitivity of each high-frequency word segmentation according to the similarity between the descriptive sentence segments where each high-frequency word segmentation is located in the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information; An encryption degree to be determined module, configured to determine the encryption degree to be determined for each high-frequency word segmentation according to the word segmentation sensitivity of each high-frequency word segmentation and the difference in the occurrence frequency of each high-frequency word segmentation among all the word segmentation under the nursing information dimension in the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information; An optimal encryption round number array acquisition module, configured to determine the optimal encryption round number array for each high-frequency word segmentation according to the encryption degree to be determined for each high-frequency word segmentation and the occurrence frequency of each high-frequency word segmentation among all the word segmentation of the clinical nursing monitoring information to be encrypted; An encrypted entry module, configured to encrypt all the real-time word segmentation in the clinical nursing monitoring information to be entered, and the encryption round of each high-frequency word segmentation during the encryption process is determined by its optimal encryption round number array, so as to obtain encrypted data and enter the encrypted data.

[0079] It should be noted that: the device provided in the above embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above.

[0080] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product, which includes: computer program code, when the computer program code runs on a computer, the computer is caused to execute any one of the clinical nursing monitoring information entry methods introduced above.

[0081] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer program code, when the computer program code runs on a computer, the computer is caused to execute any one of the clinical nursing monitoring information entry methods introduced above.

[0082] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for entering clinical nursing monitoring information, characterized in that: The following steps are involved: Acquire the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information, perform word segmentation extraction on the clinical nursing monitoring information, thereby obtaining the real-time word segmentation of the clinical nursing monitoring information to be entered and the historical word segmentation of the historical clinical nursing monitoring information; According to the occurrence frequency of each real-time segmentation of the descriptive information in the clinical nursing monitoring information to be entered in all historical segmentations of the historical clinical nursing monitoring information, high-frequency segmentations in all real-time segmentations of the descriptive information in the clinical nursing monitoring information to be entered are screened out; Determine the sensitivity of each high-frequency participle according to the similarity between the description sentences of each high-frequency participle in the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information; Determine the degree of encryption of each high-frequency segmentation according to the segmentation sensitivity of each high-frequency segmentation and the difference between the frequencies of occurrence of each high-frequency segmentation in all segmentations under the nursing information dimension in the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information; Determine the optimal encryption round array of each high-frequency segmentation word according to the degree of encryption to be performed for each high-frequency segmentation word and the frequency of occurrence of each high-frequency segmentation word in all segmentations of the clinical nursing monitoring information to be encrypted; All real-time word segments in the clinical nursing monitoring information to be entered are encrypted, and during the encryption process, the encryption round of each high-frequency word segment is determined by its optimal encryption round array, thereby obtaining encrypted data, and the encrypted data is entered.

2. A clinical nursing monitoring information input method according to claim 1, characterized in that: Filter out high-frequency words in all real-time word segmentations, including: Determine the maximum value of the occurrence frequency of each real-time segmentation word of the descriptive information to be entered in the clinical nursing monitoring information among all historical segmentations of the historical clinical nursing monitoring information to obtain the maximum occurrence frequency; Determine the ratio of the occurrence frequency of each real-time segmentation of the descriptive information to be entered in the clinical nursing monitoring information to the maximum occurrence frequency among all historical segmentations in the historical clinical nursing monitoring information to obtain the high-frequency expression degree; The real-time word segmentation of the descriptive information in the clinical nursing monitoring information to be entered whose high-frequency expression degree is greater than the set high-frequency expression degree threshold is determined as the high-frequency word segmentation.

3. A clinical nursing monitoring information input method according to claim 1, characterized in that: Determine the sensitivity of each high-frequency word segmentation, including: Determine the description consistency between each high-frequency participle and each of its identical historical participles according to the similarity between the description segment where each high-frequency participle is located in the clinical nursing monitoring information to be entered and the description segment where each identical historical participle is located in the historical clinical nursing monitoring information; According to the similarity between each identical historical participle of each high-frequency participle and the description sentence segments where other identical historical participles are located in the historical clinical nursing monitoring information, a confidence index of the description consistency between each high-frequency participle and each identical historical participle is determined; The confidence index is used to correct the description consistency between each high-frequency participle and its respective identical historical participles, and the true description consistency between each high-frequency participle and its respective identical historical participles is determined; The segmentation sensitivity of each high-frequency segmentation is determined according to the average distribution level of the true description consistency between each high-frequency segmentation and its respective identical historical segmentations.

4. A clinical nursing monitoring information entry method according to claim 3, characterized in that: Determine the descriptive consistency between each high-frequency participle and each of its identical historical participles, including: According to the coding values ​​of each high-frequency segmentation in the description segment in the clinical nursing monitoring information to be entered, a first segment segmentation coding value sequence is constructed, and according to the coding values ​​of each high-frequency segmentation in the description segment where each same historical segmentation in the historical clinical nursing monitoring information is located, a second segment segmentation coding value sequence is constructed; Determine the number of interval code values ​​between the code value corresponding to each high-frequency word segment and the midpoint code value in the first sentence segment code value sequence to obtain the first number of interval code values; and determine the number of interval code values ​​between the code value of each identical historical word segment and the midpoint code value in the second sentence segment code value sequence to obtain the second number of interval code values; Determine the difference between the coding value of each high-frequency segment and the mean coding value of all segmentations in the first segment segment sequence to obtain a first coding value difference, and determine the difference between the coding value of each same historical segmentation in the historical clinical nursing monitoring information of each high-frequency segment and the mean coding value of all segmentations in the second segment segment sequence to obtain a second coding value difference; The descriptive consistency between each high-frequency participle and each of its identical historical participles is determined based on the difference between the number of first interval code values ​​and the number of second interval code values, and the difference between the first code value difference and the second code value difference.

5. A clinical nursing monitoring information input method according to claim 4, characterized in that: Confidence indicators for determining the descriptive consistency between each high-frequency word and each of the same historical high-frequency words, including: Determine the sequence of segmentation code values ​​between each identical historical high-frequency segmentation of each high-frequency segmentation and each other identical historical high-frequency segmentation Distance value; According to the correspondence between each high-frequency participle with the same history and all other high-frequency participles with the same history The average distribution level of the distance values ​​determines the confidence indicator of the description consistency between each high-frequency word and each of the same historical high-frequency words.

6. A clinical nursing monitoring information input method according to claim 1, characterized in that: Determine the degree of encryption for each high-frequency word, including: Determine the frequency of each high-frequency segmentation word in all the segmentations under the nursing information dimension to which it belongs in the historical clinical nursing monitoring information and the sum of the set frequency parameters; Determine the ratio of the frequency of each high-frequency participle in all the participles under the nursing information dimension to which it belongs in the clinical nursing monitoring information to be entered and the corresponding added value, and obtain the frequency ratio of each high-frequency participle; The product of the frequency ratio of each high-frequency word segmentation and the word segmentation sensitivity is determined to obtain the degree of encryption to be obtained for each high-frequency word segmentation.

7. A clinical nursing monitoring information input method according to claim 1, characterized in that: Determine the optimal encryption round array for each high-frequency word, including: Divide the encryption round range into a number of encryption round intervals, each encryption round interval corresponds to an encryption degree interval, and the larger the encryption round in the encryption round interval, the larger the encryption degree in the corresponding encryption degree interval; Determine the encryption level interval to which the encryption level of each high-frequency word belongs, and use the encryption round interval corresponding to the encryption level interval as the target encryption round interval; Determine a plurality of encryption round arrays in the target encryption round interval, wherein the number of encryption rounds in the encryption round arrays is equal to the frequency of each high-frequency word appearing in all the words of the clinical nursing monitoring information to be encrypted; According to the discrete degree of all encryption round numbers in each encryption round array, the optimal encryption round array is screened out from all encryption round arrays.

8. A method for entering clinical nursing monitoring information according to claim 7, characterized in that: Filter out the best encryption round array from all encryption round arrays, including: Determine the variance of all encryption round numbers in each encryption round array; Determine the maximum variance among the variances corresponding to all encryption round arrays; The encryption round array corresponding to the maximum variance is taken as the optimal encryption round array.

9. A clinical nursing monitoring information entry system, characterized in that: It comprises a memory, a processor and an executable computer program stored in the memory and runnable on the processor, wherein when the processor executes the computer program, a clinical nursing monitoring information entry method as described in any one of claims 1 to 8 is executed.

10. A clinical nursing monitoring information input device, characterized in that: The device comprises: The data acquisition module is used to obtain the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information, and perform word segmentation extraction on the clinical nursing monitoring information, so as to obtain the real-time word segmentation of the clinical nursing monitoring information to be entered and the historical word segmentation of the historical clinical nursing monitoring information; A high-frequency word segmentation acquisition module is used to screen out high-frequency word segmentations in all real-time word segmentations of the descriptive information in the clinical nursing monitoring information to be entered according to the frequency of occurrence of each real-time word segmentation of the descriptive information in the clinical nursing monitoring information to be entered in all historical word segmentations of the historical clinical nursing monitoring information; A segmentation sensitivity acquisition module is used to determine the segmentation sensitivity of each high-frequency segmentation word according to the similarity between the description sentences of each high-frequency segmentation word in the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information; The module for obtaining the degree of encryption to be obtained is used to determine the degree of encryption to be obtained for each high-frequency segmentation according to the segmentation sensitivity of each high-frequency segmentation and the difference between the frequencies of occurrence of each high-frequency segmentation in all the segmentations under the nursing information dimension in the clinical nursing monitoring information to be entered and the historical clinical nursing monitoring information; The optimal encryption round array acquisition module is used to determine the optimal encryption round array of each high-frequency segmentation word according to the degree of encryption of each high-frequency segmentation word and the frequency of each high-frequency segmentation word appearing in all the segmentations of the clinical nursing monitoring information to be encrypted; The encryption entry module is used to encrypt all real-time word segments in the clinical nursing monitoring information to be entered, and during the encryption process, the encryption rounds of each high-frequency word segment are determined by its optimal encryption round array, thereby obtaining encrypted data and entering the encrypted data.

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