Medical water purifier auditing and tracking method and system

Abstract

The invention discloses a medical water purifier auditing and tracking method, and particularly relates to the technical field of auditing and tracking, and the method comprises the steps: when an abnormal parameter of a medical water purifier is identified, calling a user who modifies the abnormal parameter from a previous period to a current detection period, and carrying out the clustering analysis of the time point of modifying the water purifier by the modification user, the historical operation information of the modification users is determined by comparing the similarity of the operation characteristics of the modification users, the historical habit information of the modification users is determined according to the time sequence analysis of the modification abnormal parameters of the modification users, the historical operation information and the historical habit information of the modification users are comprehensively analyzed, and suspected users are determined; the association information and importance information of the medical water purification machine modified by the suspected user every time are extracted, the potential abnormal modification record of the suspected user is determined, the operation safety of the medical water purification machine can be improved, potential abnormal operation can be found in time, and the auditing and tracking efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of audit tracking technology, and more specifically, to a medical water purifier audit tracking method and system. Background Art

[0002] In order to ensure the quality of water used in medical pure water machines, the equipment parameters of medical pure water machines need to be adjusted and maintained regularly. However, with the increase in frequency of use, various parameter abnormalities may occur in medical pure water machines, leading to water quality problems, which not only affects the normal operation of the equipment, but may also affect the safety of patients. In actual operations, the maintenance and operation records of medical pure water machines are usually managed and operated by multiple staff members. However, due to the high mobility of staff and the complexity of equipment operation, some operating users may have abnormal adjustments to the equipment that cannot be discovered and corrected in time, thereby affecting the reliability of the equipment and water quality. In addition, there is often a lack of in-depth analysis of the modification records of medical pure water machine parameters, making it difficult to identify potential abnormal modification behaviors in the operation history.

[0003] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a medical water purifier audit tracking method and system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A medical water purifier audit tracking method specifically includes the following steps:

[0007] S1: Periodically identify abnormal parameters of the medical pure water machine. When abnormal parameters of the medical pure water machine are identified, retrieve users who have modified the abnormal parameters from the previous cycle to the current detection cycle;

[0008] S2: By clustering the time points at which the modifying user modified the water purifier, and by comparing the similarity of the operation features between the modifying users, the historical operation information of the modifying user is determined, and the historical habit information of the modifying user is determined based on the time series analysis of the modifying user's modification of abnormal parameters;

[0009] S3: Determine the suspicious user through comprehensive analysis of the historical operation information and historical habit information of the modifying user, and extract the associated information and importance information of each modification of the medical pure water machine by the suspicious user;

[0010] S4: Determine the potential abnormal modification records of the suspicious user through the associated information and importance information of the suspicious user's modifications to the medical pure water machine.

[0011] In a preferred embodiment, periodically identifying abnormal parameters of a medical water purifier includes:

[0012] Equipment parameters are set by using a card reader in combination with read-write cards of different authority levels, and the safety of the medical pure water machine is ensured by periodic quality inspection of the medical pure water machine. When problems occur in the quality inspection of the medical pure water machine, further inspection is performed to determine the parameter settings of the medical pure water machine, and the abnormal parameter settings of the medical pure water machine are obtained. Based on the abnormal parameter settings of the medical pure water machine, users who have modified the abnormal parameters from the previous cycle to the current inspection cycle are retrieved.

[0013] In a preferred embodiment, determining to modify the historical operation information of the user includes:

[0014] Modify the historical operation information of the user through the cluster center time distance difference coefficient and operation feature similarity coefficient;

[0015] The logic for obtaining the time distance difference coefficient of the cluster center is as follows: obtaining the historical operation records of the users who modified the abnormal parameters from the previous cycle to the current detection cycle, marking the users who modified the abnormal parameters from the previous cycle to the current detection cycle as modifying users, determining the time points at which each modifying user modified the abnormal parameters of the medical pure water machine parameters, using the DBSCAN clustering algorithm to divide the operation time points of different modifying users, setting the neighborhood radius to define the maximum distance between time points, setting the minimum number of points, determining the minimum number of time points required for a cluster, and the area below the minimum number of points is regarded as noise;

[0016] Perform DBSCAN clustering on the modification time points of each user in a day to obtain clusters of abnormal parameters of the medical water purifier, where each cluster represents the regular time period of the modification user's operation, and the noise represents some abnormal operation time points;

[0017] The cluster center of the cluster is approximated by calculating the mean of all time points in the cluster, and the cluster center of each cluster is marked as: SJ n , where n is the number of the cluster after DBSCAN clustering;

[0018] According to the operation time point of the modified user, it is determined whether there is a noise point in the modified user, and the noise point in the modified user is marked as: Wherein, i=1, 2, 3, ..., I, I is a positive integer, I is the number of the user who modified the abnormal parameters from the previous cycle to the current detection cycle, m=1, 2, 3, ..., M, M is a positive integer, m is the number of the noise point where the i-th modifying user exists;

[0019] According to the noise point of the modified user, the minimum time difference between the noise point of the modified user and the cluster center of each cluster is determined, and the minimum time difference between the noise point of the modified user and the cluster center of each cluster is marked as: in, If there is no noise point for the modified user, the minimum time difference between the noise point of the modified user and the cluster center of each cluster is set to 0;

[0020] Eliminate the noise points of user modification, and mark the user modification time points according to the user modification time points: Wherein, j=1, 2, 3, ..., J, J is a positive integer, j is the number of the operation time point of the i-th user modification, the minimum time difference between the operation time point of the user modification and the cluster center of each cluster is determined, and the minimum time difference between the operation time point of the user modification and the cluster center of each cluster is marked as: in,

[0021] Calculate the cluster center time distance difference coefficient, the calculation formula is: Among them, cy i is the cluster center time distance difference coefficient of the i-th modified user.

[0022] In a preferred embodiment, the logic for obtaining the operation feature similarity coefficient is:

[0023] Determine the operation characteristics of the modifying user. The operation characteristics of the modifying user include modification frequency, modification amplitude, and operation duration. The modification frequency is determined by determining the number of times the abnormal parameters are modified from the previous cycle to the current detection cycle. The modification amplitude is determined by recording the change amplitude of each modification of the abnormal parameters by the modifying user, and the maximum amplitude of the modification of the abnormal parameters by the modifying user is determined. The maximum amplitude of the modification of the abnormal parameters by the modifying user is used as the modification amplitude in the operation characteristics. The operation duration is determined by recording the average duration of each modification by the modifying user. The operation characteristics of the i-th modifying user are represented by a vector as: X i =(pl i ,fd i ,sc i );

[0024] The similarity between the modifying user and other modifying user operation feature vectors is calculated by cosine similarity. The calculation formula is: Among them, XS ik is the similarity between the operation features of the i-th modifying user and the k-th other modifying user, X k is the operation feature vector of other modifying users except the i-th modifying user, k=1, 2, 3, ..., K, K is a positive integer, and k is the serial number of other modifying users except the i-th modifying user;

[0025] Calculate the operation feature similarity coefficient, the calculation formula is: Among them, TZXS i is the operation feature similarity coefficient.

[0026] In a preferred embodiment, determining to modify the historical habit information of the user includes:

[0027] The logic for obtaining the stability coefficient of the operation characteristic is: according to the operation of modifying the abnormal parameters of the medical pure water machine by the user in the previous cycle, the difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle is obtained, and the difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle is marked as: CZ r , where r = 1, 2, 3, ..., R, R is a positive integer, and r is the number of each time the user modifies the medical pure water machine in the previous cycle;

[0028] According to the difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle, the time series of the modification difference of the abnormal parameters of the user in the previous cycle is determined, and the ARMA model is trained according to the time series data to determine the orders P and Q of the ARMA model. The difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle is predicted from the previous cycle to the current cycle.

[0029] The ARMA model expression obtained by training is: Among them, α is a constant term, which represents the offset of the time series, p = 1, 2, 3, ..., P, p is the number of the order P of the ARMA model, q = 1, 2, 3, ..., Q, q is the number of the order Q of the ARMA model, φ p is the coefficient of the MA part in the ARMA model, which represents the relationship with the residuals of the past p moments, θ q is the coefficient of the AR part in the ARMA model, which represents the relationship with the value of the past q moments, ε r is the white noise term;

[0030] The ARMA model obtained through training predicts the difference between the user's modification of abnormal parameters of the medical pure water machine from the previous cycle to the current cycle. The expression is: Among them, d R+t To predict the difference of the abnormal parameter modification of the medical pure water machine by the user for the tth time, d R+t-p is the known observation value of the time series data, ε R+t-q is the prediction error of the time series data, t = 1, 2, 3, ..., T, T is a positive integer, and t is the number of modified differences of the required prediction anomaly parameters;

[0031] Obtain the difference in the abnormal parameters of the medical pure water machine actually modified by the modifying user from the previous cycle to the current detection cycle, and mark the difference in the abnormal parameters of the medical pure water machine actually modified by the modifying user from the previous cycle to the current detection cycle as: DQ t ;

[0032] By calculating the variance of the residual error of the abnormal parameter difference of the modified user, the operation characteristic stability coefficient is determined, and the operation characteristic stability coefficient of the i-th modified user is marked as: WD i ,in,

[0033] In a preferred embodiment, determining a suspicious user includes:

[0034] By modifying the user's historical operation information and historical habit information through comprehensive analysis, the cluster center time distance difference coefficient, operation feature similarity coefficient and operation feature stability coefficient are weighted and calculated to build a user evaluation model and generate a user evaluation coefficient. The calculation formula of the user evaluation coefficient is: Among them, pg i is the user evaluation coefficient of the i-th modifying user, β 1 , β 2 , β 3 are the proportional coefficients of the time distance difference coefficient of the cluster center, the similarity coefficient of the operating characteristics, and the stability coefficient of the operating characteristics, respectively. 1 , β 2 , β 3 They are both greater than 0;

[0035] Set a user evaluation coefficient threshold, compare the user evaluation coefficients of all modified users with the user evaluation coefficient threshold, mark the modified users whose user evaluation coefficients are greater than the user evaluation coefficient threshold as suspicious users, and do not mark the modified users if the user evaluation coefficient of the modified users is less than the user evaluation coefficient threshold.

[0036] In a preferred embodiment, the associated information of each modification of the medical pure water machine by the suspicious user includes:

[0037] The associated information of the suspected user's modification of the medical pure water machine is represented by the associated closeness coefficient;

[0038] The logic for obtaining the correlation closeness coefficient is as follows: construct a knowledge graph of medical pure water machine parameters, wherein the basic unit in the knowledge graph of medical pure water machine parameters is a node, a node represents an entity, and indicates the parameters of the medical pure water machine. The nodes are connected by edges, indicating the relationship between the parameters of the medical pure water machine. The edges between the nodes have a weight value, indicating the strength of the relationship between the nodes.

[0039] Get the parameters of the medical pure water machine modified by the suspicious user each time, and represent the parameters of the medical pure water machine modified by the suspicious user each time through a set as: XG g = {GB 1 , GB 2 , GB 3 ,……,GB H}, where g = 1, 2, 3, ..., G, G is a positive integer, g is the number of times the suspected user has modified the medical pure water machine from the last cycle to the current detection cycle, GB 1 , GB 2 , GB 3 ,......,GB H For each parameter modified by the suspicious user, the distance between the abnormal parameters of the medical pure water machine and the parameters modified by the suspicious user each time is determined through the shortest path analysis in the graph algorithm, and the distance between the abnormal parameters of the medical pure water machine and the parameters modified by the suspicious user each time is marked as: JL h ;

[0040] Calculate the correlation closeness coefficient, the calculation formula is: Among them, JM g It is the correlation closeness coefficient of the g-th modification of the medical pure water machine by the suspicious user, h=1, 2, 3, ..., H, H is a positive integer, and h is the number of the medical pure water machine parameter modified for the g-th time.

[0041] In a preferred embodiment, the importance information of each modification made by the suspected user to the medical pure water machine includes:

[0042] The importance information of the suspected user's modification of the medical water purifier is expressed by the risk important regression coefficient;

[0043] The logic for obtaining the risk important regression coefficient is as follows: determine the parameters of the medical pure water machine, collect the parameter historical data of the medical pure water machine, and obtain the water quality data corresponding to the parameters of each medical pure water machine, and construct a Logistic risk important regression model through a Logistic regression model to generate a risk important regression coefficient, and quantify the importance of each parameter through the risk important regression coefficient;

[0044] The expression of the logistic risk important regression model is: Among them, HG is the risk important regression coefficient of the Logistic risk important regression model, CS 1 , CS 2 , CS 3 ,......,CS B is the value of all parameters of the medical water purifier, λ 1 , 2 , 3 ,......,λB is the regression coefficient, indicating the influence of each medical water purifier parameter on water quality risk;

[0045] Obtain the parameters of the medical pure water machine after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle, and bring the parameters of the medical pure water machine after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle into the Logistic risk important regression model to obtain the risk important regression coefficient after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle, and mark the risk important regression coefficient after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle as: HG g .

[0046] In a preferred embodiment, determining the potential abnormal modification record of the suspicious user includes:

[0047] Through comprehensive analysis of the relevance information and importance information of the suspicious users' modifications to the medical pure water machine, weighted calculation of the correlation closeness coefficient and the risk important regression coefficient is performed, a modification evaluation model is constructed, and a modification evaluation coefficient is generated. The calculation formula of the modification evaluation coefficient is: Among them, pg g is the modification evaluation coefficient of the g-th modification by the suspicious user, γ 1 , γ 2 are the proportional coefficients of the correlation coefficient and the risk important regression coefficient, γ 1 , γ 2 They are both greater than 0;

[0048] Set a modification evaluation coefficient threshold, and compare the modification evaluation coefficient of each modification made by the suspicious user from the previous cycle to the current detection cycle with the modification evaluation coefficient threshold. If the modification evaluation coefficient is greater than the modification evaluation coefficient threshold, the modification is marked; if the modification evaluation coefficient is less than the modification evaluation coefficient threshold, the modification is not marked.

[0049] In a preferred embodiment, a medical water purifier audit tracking system includes an abnormality identification module, a data acquisition module, a user evaluation module, and a record audit evaluation module, and the modules are signal-connected;

[0050] The abnormality identification module is used to periodically monitor the operating status of the medical pure water machine and identify abnormal parameters that occur in the current cycle;

[0051] The data collection module is used to collect and modify the historical operation information and historical habit information of the user, and collect the associated information and importance information of the medical pure water machine modification;

[0052] A user evaluation module is used to build a user evaluation model, generate a user evaluation coefficient, and compare the user evaluation coefficient with a user evaluation coefficient threshold to determine suspicious users;

[0053] The record audit assessment module is used to build a modification assessment model, generate a modification assessment coefficient, and compare the modification assessment coefficient of the suspicious user with the modification assessment coefficient threshold to determine the potential abnormal modification records of the suspicious user.

[0054] Technical effects and advantages of the present invention:

[0055] The present invention periodically identifies abnormal parameters of medical pure water machines, conducts in-depth analysis based on the user's modification behavior, and then identifies potential abnormal modification records and ultimately determines suspicious users, which helps to improve the operating safety of medical pure water machines, promptly discover potential abnormal operations, and improve the efficiency of audit tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0057] Figure 1 A schematic diagram of a process of an audit tracking method for a medical water purifier according to the present invention;

[0058] Figure 2 The present invention is a schematic structural diagram of an audit tracking system for a medical water purifier. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Example 1

[0061] like Figure 1 A schematic flow chart of a method for auditing and tracking a medical water purifier of the present invention is provided, which specifically comprises the following steps:

[0062] S1: Periodically identify abnormal parameters of the medical pure water machine. When abnormal parameters of the medical pure water machine are identified, retrieve users who have modified the abnormal parameters from the previous cycle to the current detection cycle;

[0063] S2: By clustering the time points at which the modifying user modified the water purifier, and by comparing the similarity of the operation features between the modifying users, the historical operation information of the modifying user is determined, and the historical habit information of the modifying user is determined based on the time series analysis of the modifying user's modification of abnormal parameters;

[0064] S3: Determine the suspicious user through comprehensive analysis of the historical operation information and historical habit information of the modifying user, and extract the associated information and importance information of each modification of the medical pure water machine by the suspicious user;

[0065] S4: Determine the potential abnormal modification records of the suspicious user through the associated information and importance information of the suspicious user's modifications to the medical pure water machine.

[0066] In order to ensure the safety of water quality, compliance of equipment and traceability of operation, and to protect the hygiene and safety of the medical environment, the safety of medical pure water machines is improved by using card readers combined with read-write cards of different authority levels to set equipment parameters, including:

[0067] Users with different permissions use different reader / writer cards. Different reader / writer cards have the ability to modify all parameters and system settings under that permission, and restrict the use of parameters and system settings outside of the permission.

[0068] When modifying the parameters of the medical pure water machine, the user is authenticated, the card reader reads the card information of the permission card and determines all the parameters and system settings under the user's permission according to the user's permission level;

[0069] After each card swiping operation, the system will record the timestamp, user ID, permission level, and operation type (read, modify, etc.) of the relevant operation for subsequent query and audit, and generate historical operation records of the medical pure water machine.

[0070] The safety of medical pure water machines is ensured by periodically conducting quality inspections on them. When problems occur during quality inspections of medical pure water machines, further inspections are conducted to determine the parameter settings of the medical pure water machines and obtain abnormal parameter settings of the medical pure water machines. The contents of quality inspections of medical pure water machines include:

[0071] Water quality testing: including water purity (such as conductivity, TDS, etc.), content of bacteria, viruses and other microorganisms, pH value, etc.;

[0072] Equipment performance testing: whether the equipment's operating pressure, flow, temperature, etc. meet the design requirements;

[0073] Equipment health status detection: including whether the filter element contamination, membrane aging, etc. meet the expected service life.

[0074] The detection of medical pure water machine parameter settings includes water flow, pressure, temperature, pH value, conductivity and other parameters, which need to be monitored in real time, and thresholds must be set to determine whether there are any abnormalities.

[0075] It should be noted that periodic quality inspections focus on the overall operating status of the equipment and water quality indicators, and parameter abnormalities may not directly lead to quality inspection abnormalities, but the occurrence of quality inspection abnormalities must be related to certain parameter abnormalities. Therefore, the quality inspection results can be used as a trigger point to further trigger in-depth analysis of parameter abnormalities.

[0076] According to the abnormal parameter settings of the medical pure water machine, the users who modified the abnormal parameters from the previous cycle to the current detection cycle are retrieved, the operation characteristics of the modified users in the historical operation records of the medical pure water machine are obtained, the historical operation records of the modified users are collected, and according to the usage habits of the modified users, the historical habit information of the modified users is collected, the historical operation information of the modified users is expressed by the cluster center time distance difference coefficient and the operation feature similarity coefficient, and the historical habit information of the modified users is expressed by the operation feature stability coefficient.

[0077] The logic for obtaining the time distance difference coefficient of the cluster center is as follows: obtaining the historical operation records of the users who modified the abnormal parameters from the previous cycle to the current detection cycle, marking the users who modified the abnormal parameters from the previous cycle to the current detection cycle as modifying users, determining the time points at which each modifying user modified the abnormal parameters of the medical pure water machine parameters, using the DBSCAN clustering algorithm to divide the operation time points of different modifying users, setting the neighborhood radius to define the maximum distance between time points, setting the minimum number of points, determining the minimum number of time points required for a cluster, and the area below the minimum number of points is regarded as noise;

[0078] It should be noted that the modification of medical pure water machine parameters is usually regular, and the parameters of the medical pure water machine often need to be modified or checked during a specific time period of the day. Therefore, the clustering algorithm can be used to identify users who frequently modify or check parameters within a day.

[0079] Perform DBSCAN clustering on the modification time points of each user in a day to obtain clusters of abnormal parameters of the medical water purifier, where each cluster represents the regular time period of the modification user's operation, and the noise represents some abnormal operation time points;

[0080] The cluster center of the cluster is approximated by calculating the mean of all time points in the cluster, and the cluster center of each cluster is marked as: SJ n , where n is the number of the cluster after DBSCAN clustering;

[0081] According to the operation time point of the modified user, it is determined whether there is a noise point in the modified user, and the noise point in the modified user is marked as: Wherein, i=1, 2, 3, ..., I, I is a positive integer, I is the number of the user who modified the abnormal parameters from the previous cycle to the current detection cycle, m=1, 2, 3, ..., M, M is a positive integer, m is the number of the noise point where the i-th modifying user exists;

[0082] According to the noise point of the modified user, the minimum time difference between the noise point of the modified user and the cluster center of each cluster is determined, and the minimum time difference between the noise point of the modified user and the cluster center of each cluster is marked as: in, If there is no noise point for the modified user, the minimum time difference between the noise point of the modified user and the cluster center of each cluster is set to 0;

[0083] Eliminate the noise points of user modification, and mark the user modification time points according to the user modification time points: Wherein, j=1, 2, 3, ..., J, J is a positive integer, j is the number of the operation time point of the i-th user modification, the minimum time difference between the operation time point of the user modification and the cluster center of each cluster is determined, and the minimum time difference between the operation time point of the user modification and the cluster center of each cluster is marked as: in,

[0084] Calculate the cluster center time distance difference coefficient, the calculation formula is: Among them, cy i is the cluster center time distance difference coefficient of the i-th modified user.

[0085] It can be seen from the formula that the larger the time distance difference coefficient of the cluster center of the modified user is, the more irregular the time of the modified user's operation of the water purifier may be, which means that the operations performed by the user at different time points are significantly different from the operation mode of most users, thereby increasing the possibility of the user causing abnormal equipment parameters.

[0086] The acquisition logic of the operation feature similarity coefficient is as follows: determine the operation feature of the modifying user, the operation feature of the modifying user includes modification frequency, modification amplitude, and operation duration, the modification frequency is determined by determining the number of times the abnormal parameters are modified from the previous cycle to the current detection cycle, the modification amplitude is determined by recording the change amplitude of each modification of the abnormal parameters by the modifying user, the maximum amplitude of the modification of the abnormal parameters by the modifying user is determined, and the maximum amplitude of the modification of the abnormal parameters by the modifying user is used as the modification amplitude in the operation feature, the operation duration is determined by recording the average duration of each modification by the modifying user, and the operation feature of the i-th modifying user is represented by a vector as: X i =(pli ,fd i ,sc i );

[0087] The similarity between the modifying user and other modifying user operation feature vectors is calculated by cosine similarity. The calculation formula is: Among them, XS ik is the similarity between the operation features of the i-th modifying user and the k-th other modifying user, X k is the operation feature vector of other modifying users except the i-th modifying user, k=1, 2, 3, ..., K, K is a positive integer, and k is the serial number of other modifying users except the i-th modifying user;

[0088] Calculate the operation feature similarity coefficient, the calculation formula is: Among them, TZXS i is the operation feature similarity coefficient.

[0089] It can be seen from the formula that the larger the operation feature similarity coefficient is, the more similar the operation behavior of the modified user for the medical pure water machine from the previous cycle to the current cycle is to that of similar users, indicating that the modified user may operate according to the regular operation process expected by the system, and the operation habits are relatively stable and regular, which means that the user's operation has not deviated from normal behavior and is in line with normal behavior.

[0090] The logic for obtaining the stability coefficient of the operation characteristic is: according to the operation of modifying the abnormal parameters of the medical pure water machine by the user in the previous cycle, the difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle is obtained, and the difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle is marked as: CZ r , where r = 1, 2, 3, ..., R, R is a positive integer, and r is the number of each time the user modifies the medical pure water machine in the previous cycle;

[0091] According to the difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle, the time series of the modification difference of the abnormal parameters of the user in the previous cycle is determined, and the ARMA model is trained according to the time series data to determine the orders P and Q of the ARMA model. The difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle is predicted from the previous cycle to the current cycle.

[0092] It should be noted that the orders p and q of the ARMA model are set by professionals in the field and are usually determined through ACF (autocorrelation function) and PACF (partial autocorrelation function) graphs combined with experience.

[0093] The ARMA model expression obtained by training is: Among them, α is a constant term, which represents the offset of the time series, p = 1, 2, 3, ..., P, p is the number of the order P of the ARMA model, q = 1, 2, 3, ..., Q, q is the number of the order Q of the ARMA model, φ p is the coefficient of the MA part in the ARMA model, which represents the relationship with the residuals of the past p moments, θ q is the coefficient of the AR part in the ARMA model, which represents the relationship with the value of the past q moments, ε r is the white noise term;

[0094] The ARMA model obtained through training predicts the difference between the user's modification of abnormal parameters of the medical pure water machine from the previous cycle to the current cycle. The expression is: Among them, d R+t To predict the difference of the abnormal parameter modification of the medical pure water machine by the user for the tth time, d R+t-p is the known observation value of the time series data, ε R+t-q is the prediction error of the time series data, t=1, 2, 3, ..., T, T is a positive integer, t is the number of the difference values ​​of the required prediction abnormal parameter modification, and usually the number of the difference values ​​of the required prediction abnormal parameter modification is the same as the number of differences of the abnormal parameters of the medical pure water machine actually modified by the modifying user from the previous cycle to the current detection cycle;

[0095] Obtain the difference in the abnormal parameters of the medical pure water machine actually modified by the modifying user from the previous cycle to the current detection cycle, and mark the difference in the abnormal parameters of the medical pure water machine actually modified by the modifying user from the previous cycle to the current detection cycle as: DQ t ;

[0096] By calculating the variance of the residual error of the abnormal parameter difference of the modified user, the operation characteristic stability coefficient is determined, and the operation characteristic stability coefficient of the i-th modified user is marked as: WD i ,in,

[0097] It can be seen from the formula that the larger the stability coefficient of the user's operation characteristics, the larger the error predicted by the ARMA model, which means that the modification habits and rules of the user may have changed.

[0098] By modifying the user's historical operation information and historical habit information through comprehensive analysis, the cluster center time distance difference coefficient, operation feature similarity coefficient and operation feature stability coefficient are weighted and calculated to build a user evaluation model and generate a user evaluation coefficient. The calculation formula of the user evaluation coefficient is: Among them, pg i is the user evaluation coefficient of the i-th modifying user, β 1 , β2 , β 3 are the proportional coefficients of the time distance difference coefficient of the cluster center, the similarity coefficient of the operating characteristics, and the stability coefficient of the operating characteristics, respectively. 1 , β 2 , β 3 Both are greater than 0.

[0099] Set a user evaluation coefficient threshold, compare the user evaluation coefficients of all modified users with the user evaluation coefficient threshold, and mark the modified users whose user evaluation coefficients are greater than the user evaluation coefficient threshold as suspicious users, indicating that the modified users are more likely to violate the regulations on the medical pure water machine. If the user evaluation coefficient of the modified user is less than the user evaluation coefficient threshold, no mark will be made.

[0100] According to the abnormal parameters of the medical pure water machine from the previous cycle to the current detection cycle, by constructing a knowledge graph of the medical pure water machine parameters, the associated information of the modifications made to the medical pure water machine by the suspicious users is obtained, and according to the historical data of the medical pure water machine parameters, the degree of influence of each parameter on the water quality is analyzed, and the importance information of the modifications made to the medical pure water machine by the suspicious users is obtained. The associated information of the modifications made to the medical pure water machine by the suspicious users is represented by the correlation closeness coefficient, and the importance information of the modifications made to the medical pure water machine by the suspicious users is represented by the risk importance regression coefficient.

[0101] It should be noted that after finding the suspicious user and abnormal parameters, it is necessary to further search for other modification operations that may be abnormal based on the abnormal parameters of the suspicious user, identify other potential abnormal modifications, and reduce misjudgments or omissions caused by a single parameter abnormality, including:

[0102] A single parameter anomaly may be a mistake or temporary adjustment made by the user at a certain moment during the operation, but it may also affect other related parameters. If we only rely on the anomaly of a single parameter as the identification standard, we may easily miss some systematic anomalies caused by the combined effect of multiple parameters. Through multi-dimensional analysis, we can systematically capture the correlation between multiple parameters and identify potential abnormal behaviors.

[0103] Abnormal modifications in a single parameter do not necessarily directly cause a malfunction or water quality problem, but are part of a systemic problem, and examining the interrelationships between multiple parameters can help us identify these potential systemic problems;

[0104] By comprehensively analyzing the operating behaviors of suspicious users, potential equipment failures and abnormalities can be predicted in advance, so that maintenance or adjustments can be performed in advance. This proactive fault prevention strategy is more cost-effective than post-processing, and can reduce equipment downtime and improve equipment availability and stability.

[0105] The logic for obtaining the correlation closeness coefficient is as follows: construct a knowledge graph of medical pure water machine parameters, wherein the basic unit in the knowledge graph of medical pure water machine parameters is a node, a node represents an entity, and indicates the parameters of the medical pure water machine. The nodes are connected by edges, indicating the relationship between the parameters of the medical pure water machine. The edges between the nodes have a weight value, indicating the strength of the relationship between the nodes.

[0106] Get the parameters of the medical pure water machine modified by the suspicious user each time, and represent the parameters of the medical pure water machine modified by the suspicious user each time through a set as: XG g = {GB 1 , GB 2 , GB 3 ,......,GB H}, where g = 1, 2, 3, ..., G, G is a positive integer, g is the number of times the suspected user has modified the medical pure water machine from the last cycle to the current detection cycle, GB 1 , GB 2 , GB 3 ,……,GB H For each parameter modified by the suspicious user, the distance between the abnormal parameters of the medical pure water machine and the parameters modified by the suspicious user each time is determined through the shortest path analysis in the graph algorithm, and the distance between the abnormal parameters of the medical pure water machine and the parameters modified by the suspicious user each time is marked as: JL h ;

[0107] Calculate the correlation closeness coefficient, the calculation formula is: Among them, JM g It is the correlation closeness coefficient of the g-th modification of the medical pure water machine by the suspicious user, h=1, 2, 3, ..., H, H is a positive integer, and h is the number of the medical pure water machine parameter modified for the g-th time.

[0108] It can be seen from the formula that the smaller the correlation closeness coefficient is, the more likely it is that there is a problem with the operation, which means that the suspicious user may not only modify abnormal parameters, but may also modify parameters in violation of regulations during the operation.

[0109] The logic for obtaining the risk important regression coefficient is as follows: determine the parameters of the medical pure water machine, collect the parameter historical data of the medical pure water machine, and obtain the water quality data corresponding to the parameters of each medical pure water machine, and construct a Logistic risk important regression model through a Logistic regression model to generate a risk important regression coefficient, and quantify the importance of each parameter through the risk important regression coefficient;

[0110] It should be noted that the parameters of the medical pure water machine include flow rate, temperature, pressure, conductivity and pH value, etc. By setting the parameters of the medical pure water machine, the water quality of the medical pure water machine can be affected.

[0111] The expression of the logistic risk important regression model is: Among them, HG is the risk important regression coefficient of the Logistic risk important regression model, CS 1 , CS 2 , CS 3 ,……,CS B is the value of all parameters of the medical water purifier, λ 1 , 2 , 3 , ..., λ B is the regression coefficient, indicating the influence of each medical water purifier parameter on water quality risk;

[0112] Obtain the parameters of the medical pure water machine after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle, and bring the parameters of the medical pure water machine after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle into the Logistic risk important regression model to obtain the risk important regression coefficient after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle, and mark the risk important regression coefficient after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle as: HG g .

[0113] It can be seen from the formula that the larger the risk important regression coefficient of each operation of the suspicious user, the greater the impact of the operation on the water quality risk of the medical pure water machine.

[0114] Through comprehensive analysis of the relevance information and importance information of the suspicious users' modifications to the medical pure water machine, weighted calculation of the correlation closeness coefficient and the risk important regression coefficient is performed, a modification evaluation model is constructed, and a modification evaluation coefficient is generated. The calculation formula of the modification evaluation coefficient is: Among them, pg g is the modification evaluation coefficient of the g-th modification by the suspicious user, γ 1 , γ 2 are the proportional coefficients of the correlation coefficient and the risk important regression coefficient, γ 1 , γ 2 Both are greater than 0.

[0115] It can be seen from the formula that the smaller the correlation closeness coefficient and the larger the risk important regression coefficient, the larger the modification evaluation coefficient, which means that there may be problems with the modification operation of the medical pure water machine by the suspicious user this time. Conversely, the larger the correlation closeness coefficient and the smaller the risk important regression coefficient, the smaller the modification evaluation coefficient, which means that there may be no problems with the modification operation of the medical pure water machine by the suspicious user this time.

[0116] Set a modification evaluation coefficient threshold, and compare the modification evaluation coefficient of each modification made by the suspicious user from the previous cycle to the current detection cycle with the modification evaluation coefficient threshold. If the modification evaluation coefficient is greater than the modification evaluation coefficient threshold, the modification is marked, indicating that there is a problem with this modification. This is used by professional staff to track potential parameter modification issues. If the modification evaluation coefficient is less than the modification evaluation coefficient threshold, the modification is not marked, indicating that this modification is normal.

[0117] The present invention periodically identifies abnormal parameters of medical pure water machines, conducts in-depth analysis based on the user's modification behavior, and then identifies potential abnormal modification records and ultimately determines suspicious users, which helps to improve the operating safety of medical pure water machines, promptly discover potential abnormal operations, and improve the efficiency of audit tracking.

[0118] Example 2

[0119] like Figure 2 A schematic diagram of the structure of an audit tracking system for a medical water purifier of the present invention is given, which specifically includes an abnormality identification module, a data acquisition module, a user evaluation module and a record audit evaluation module, and the modules are signal-connected;

[0120] The abnormality identification module is used to periodically monitor the operating status of the medical pure water machine and identify abnormal parameters that occur in the current cycle;

[0121] The data collection module is used to collect and modify the historical operation information and historical habit information of the user, and collect the associated information and importance information of the medical pure water machine modification;

[0122] A user evaluation module is used to build a user evaluation model, generate a user evaluation coefficient, and compare the user evaluation coefficient with a user evaluation coefficient threshold to determine suspicious users;

[0123] The record audit assessment module is used to build a modification assessment model, generate a modification assessment coefficient, and compare the modification assessment coefficient of the suspicious user with the modification assessment coefficient threshold to determine the potential abnormal modification records of the suspicious user.

[0124] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0125] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0126] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0127] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0128] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0129] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0130] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0131] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A medical water purifier audit tracking method, characterized in that: Specifically include the following steps: S1: Periodically identify abnormal parameters of the medical pure water machine. When abnormal parameters of the medical pure water machine are identified, retrieve users who have modified the abnormal parameters from the previous cycle to the current detection cycle; S2: By clustering the time points at which the modifying user modified the water purifier, and by comparing the similarity of the operation features between the modifying users, the historical operation information of the modifying user is determined, and the historical habit information of the modifying user is determined based on the time series analysis of the modifying user's modification of abnormal parameters; S3: Determine the suspicious user through comprehensive analysis of the historical operation information and historical habit information of the modifying user, and extract the associated information and importance information of each modification of the medical pure water machine by the suspicious user; S4: Determine the potential abnormal modification records of the suspicious user through the associated information and importance information of the suspicious user's modifications to the medical pure water machine.

2. A medical water purifier audit tracking method according to claim 1, characterized in that ,Periodically identify abnormal parameters of medical water purifiers, including: Equipment parameters are set by using a card reader in combination with read-write cards of different authority levels, and the safety of the medical pure water machine is ensured by periodic quality inspection of the medical pure water machine. When problems occur in the quality inspection of the medical pure water machine, further inspection is performed to determine the parameter settings of the medical pure water machine, and the abnormal parameter settings of the medical pure water machine are obtained. Based on the abnormal parameter settings of the medical pure water machine, users who have modified the abnormal parameters from the previous cycle to the current inspection cycle are retrieved.

3. A medical water purifier audit tracking method according to claim 2, characterized in that: Confirm and modify the user's historical operation information, including: Modify the historical operation information of the user through the cluster center time distance difference coefficient and operation feature similarity coefficient; The logic for obtaining the time distance difference coefficient of the cluster center is as follows: obtaining the historical operation records of the users who modified the abnormal parameters from the previous cycle to the current detection cycle, marking the users who modified the abnormal parameters from the previous cycle to the current detection cycle as modification users, determining the time points at which each modification user modified the abnormal parameters of the medical pure water machine parameters, using the DBSCAN clustering algorithm to divide the operation time points of different modification users, setting the neighborhood radius to define the maximum distance between time points, setting the minimum number of points, determining the minimum number of time points required for a cluster, and the area below the minimum number of points is regarded as noise; Perform DBSCAN clustering on the modification time points of each user in a day to obtain clusters of abnormal parameters of the medical water purifier, where each cluster represents the regular time period of the modification user's operation, and the noise represents some abnormal operation time points; The cluster center of the cluster is approximated by calculating the mean of all time points in the cluster, and the cluster center of each cluster is marked as: SJ n , where n is the number of the cluster after DBSCAN clustering; According to the operation time point of the modified user, it is determined whether there is a noise point in the modified user, and the noise point in the modified user is marked as: Wherein, i=1, 2, 3, ..., I, I is a positive integer, I is the number of the user who modified the abnormal parameters from the previous cycle to the current detection cycle, m=1, 2, 3, ..., M, M is a positive integer, m is the number of the noise point where the i-th modifying user exists; According to the noise point of the modified user, the minimum time difference between the noise point of the modified user and the cluster center of each cluster is determined, and the minimum time difference between the noise point of the modified user and the cluster center of each cluster is marked as: in, If there is no noise point for the modified user, the minimum time difference between the noise point of the modified user and the cluster center of each cluster is set to 0; Eliminate the noise points of user modification, and mark the user modification time points according to the user modification time points: Wherein, j=1, 2, 3, ..., J, J is a positive integer, j is the number of the operation time point of the i-th user modification, the minimum time difference between the operation time point of the user modification and the cluster center of each cluster is determined, and the minimum time difference between the operation time point of the user modification and the cluster center of each cluster is marked as: in, Calculate the cluster center time distance difference coefficient, the calculation formula is: Among them, cy i is the cluster center time distance difference coefficient of the i-th modified user.

4. A medical water purifier audit tracking method according to claim 3, characterized in that: The logic for obtaining the similarity coefficient of the operation feature is: Determine the operation characteristics of the modifying user. The operation characteristics of the modifying user include modification frequency, modification amplitude, and operation duration. The modification frequency is determined by determining the number of times the abnormal parameters are modified from the previous cycle to the current detection cycle. The modification amplitude is determined by recording the change amplitude of each modification of the abnormal parameters by the modifying user, and the maximum amplitude of the modification of the abnormal parameters by the modifying user is determined. The maximum amplitude of the modification of the abnormal parameters by the modifying user is used as the modification amplitude in the operation characteristics. The operation duration is determined by recording the average duration of each modification by the modifying user. The operation characteristics of the i-th modifying user are represented by a vector as: X i =(pl i ,fd i ,sc i ); The similarity between the modifying user and other modifying user operation feature vectors is calculated by cosine similarity. The calculation formula is: Among them, XS ik is the similarity between the operation features of the i-th modifying user and the k-th other modifying user, X k is the operation feature vector of other modifying users except the i-th modifying user, k=1, 2, 3, ..., K, K is a positive integer, and k is the serial number of other modifying users except the i-th modifying user; Calculate the operation feature similarity coefficient, the calculation formula is: Among them, TZXS i is the operation feature similarity coefficient.

5. A medical water purifier audit tracking method according to claim 4, characterized in that: Determine and modify the user's historical habit information, including: The logic for obtaining the stability coefficient of the operation characteristic is: according to the operation of modifying the abnormal parameters of the medical pure water machine by the user in the previous cycle, the difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle is obtained, and the difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle is marked as: CZ r , where r = 1, 2, 3, ..., R, R is a positive integer, and r is the number of each time the user modifies the medical pure water machine in the previous cycle; According to the difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle, the time series of the modification difference of the abnormal parameters of the user in the previous cycle is determined, and the ARMA model is trained according to the time series data to determine the orders P and Q of the ARMA model, and the difference of each modification of the abnormal parameters of the medical pure water machine by the user in the previous cycle to predict the difference of each modification of the abnormal parameters of the medical pure water machine by the user from the previous cycle to the current cycle is predicted; The ARMA model expression obtained by training is: Among them, α is a constant term, which represents the offset of the time series, p = 1, 2, 3, ..., P, p is the number of the order P of the ARMA model, q = 1, 2, 3, ..., Q, q is the number of the order Q of the ARMA model, φ p is the coefficient of the MA part in the ARMA model, which represents the relationship with the residuals of the past p moments, θ q is the coefficient of the AR part in the ARMA model, which represents the relationship with the value of the past q moments, ε r is the white noise term; The ARMA model obtained through training predicts the difference between the user's modification of abnormal parameters of the medical pure water machine from the previous cycle to the current cycle. The expression is: Among them, d R+t To predict the difference of the abnormal parameter modification of the medical pure water machine by the user for the tth time, d R+t-p is the known observation value of the time series data, ε R+t-q is the prediction error of the time series data, t = 1, 2, 3, ..., T, T is a positive integer, and t is the number of modified differences of the required prediction anomaly parameters; Obtain the difference in the abnormal parameters of the medical pure water machine actually modified by the modifying user from the previous cycle to the current detection cycle, and mark the difference in the abnormal parameters of the medical pure water machine actually modified by the modifying user from the previous cycle to the current detection cycle as: DQ t ; By calculating the variance of the residual of the abnormal parameter difference of the modified user, the operation characteristic stability coefficient is determined, and the operation characteristic stability coefficient of the i-th modified user is marked as: WD i ,in, 6. A medical water purifier audit tracking method according to claim 5, characterized in that: Identify suspicious users, including: By modifying the user's historical operation information and historical habit information through comprehensive analysis, the cluster center time distance difference coefficient, operation feature similarity coefficient and operation feature stability coefficient are weighted and calculated to build a user evaluation model and generate a user evaluation coefficient. The calculation formula of the user evaluation coefficient is: Among them, pg i is the user evaluation coefficient of the i-th modifying user, β1, β2, and β3 are respectively the proportional coefficients of the cluster center time distance difference coefficient, the operation feature similarity coefficient, and the operation feature stability coefficient, and β1, β2, and β3 are all greater than 0; Set a user evaluation coefficient threshold, compare the user evaluation coefficients of all modified users with the user evaluation coefficient threshold, mark the modified users whose user evaluation coefficients are greater than the user evaluation coefficient threshold as suspicious users, and do not mark the modified users if the user evaluation coefficient of the modified users is less than the user evaluation coefficient threshold.

7. A medical water purifier audit tracking method according to claim 6, characterized in that: The relevant information of each modification made by the suspicious user to the medical pure water machine includes: The associated information of the suspected user's modification of the medical pure water machine is represented by the associated closeness coefficient; The logic for obtaining the correlation closeness coefficient is as follows: construct a knowledge graph of medical pure water machine parameters, wherein the basic unit in the knowledge graph of medical pure water machine parameters is a node, a node represents an entity, and indicates the parameters of the medical pure water machine. The nodes are connected by edges, indicating the relationship between the parameters of the medical pure water machine. The edges between the nodes have a weight value, indicating the strength of the relationship between the nodes. Get the parameters of the medical pure water machine modified by the suspicious user each time, and represent the parameters of the medical pure water machine modified by the suspicious user each time through a set as: XG g ={GB1, GB2, GB3,..., GB H }, where g = 1, 2, 3, ..., G, G is a positive integer, g is the number of times the suspected user has modified the medical pure water machine from the previous cycle to the current detection cycle, GB1, GB2, GB3, ..., GB H For each parameter modified by the suspicious user, the distance between the abnormal parameters of the medical pure water machine and the parameters modified by the suspicious user each time is determined through the shortest path analysis in the graph algorithm, and the distance between the abnormal parameters of the medical pure water machine and the parameters modified by the suspicious user each time is marked as: JL h ; Calculate the correlation closeness coefficient, the calculation formula is: Among them, JM g It is the correlation closeness coefficient of the g-th modification of the medical pure water machine by the suspicious user, h=1, 2, 3, ..., H, H is a positive integer, and h is the number of the medical pure water machine parameter modified for the g-th time.

8. A medical water purifier audit tracking method according to claim 7, characterized in that: The importance of each modification of the medical water purifier by the suspected user includes: The importance information of the suspected user's modification of the medical water purifier is expressed by the risk important regression coefficient; The logic for obtaining the risk important regression coefficient is as follows: determine the parameters of the medical pure water machine, collect the parameter historical data of the medical pure water machine, and obtain the water quality data corresponding to the parameters of each medical pure water machine, and construct a Logistic risk important regression model through a Logistic regression model to generate a risk important regression coefficient, and quantify the importance of each parameter through the risk important regression coefficient; The expression of the logistic risk important regression model is: Among them, HG is the risk important regression coefficient of the Logistic risk important regression model, CS1, CS2, CS3, ..., CS B are the values ​​of all parameters of the medical pure water machine, λ1, λ2, λ3, ..., λ B is the regression coefficient, indicating the influence of each medical water purifier parameter on water quality risk; Obtain the parameters of the medical pure water machine after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle, and bring the parameters of the medical pure water machine after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle into the Logistic risk important regression model to obtain the risk important regression coefficient after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle, and mark the risk important regression coefficient after each operation of the medical pure water machine by the suspicious user from the previous cycle to the current detection cycle as: HG g .

9. A medical water purifier audit tracking method according to claim 8, characterized in that: Identify potential abnormal modification records of suspicious users, including: Through comprehensive analysis of the relevance information and importance information of the suspicious users' modifications to the medical pure water machine, weighted calculation of the correlation closeness coefficient and the risk important regression coefficient is performed, a modification evaluation model is constructed, and a modification evaluation coefficient is generated. The calculation formula of the modification evaluation coefficient is: Among them, pg g is the modification evaluation coefficient of the g-th modification by the suspicious user, γ1 and γ2 are the proportional coefficients of the correlation closeness coefficient and the risk important regression coefficient, respectively, and γ1 and γ2 are both greater than 0; Set a modification evaluation coefficient threshold, and compare the modification evaluation coefficient of each modification made by the suspicious user from the previous cycle to the current detection cycle with the modification evaluation coefficient threshold. If the modification evaluation coefficient is greater than the modification evaluation coefficient threshold, the modification is marked; if the modification evaluation coefficient is less than the modification evaluation coefficient threshold, the modification is not marked.

10. A medical water purifier audit tracking system, used to implement a medical water purifier audit tracking method according to any one of claims 1 to 9, characterized in that: It includes an abnormality identification module, a data collection module, a user evaluation module and a record audit evaluation module, and the modules are connected by signals; The abnormality identification module is used to periodically monitor the operating status of the medical pure water machine and identify abnormal parameters that occur in the current cycle; The data collection module is used to collect and modify the historical operation information and historical habit information of the user, and collect the associated information and importance information of the medical pure water machine modification; A user evaluation module is used to build a user evaluation model, generate a user evaluation coefficient, and compare the user evaluation coefficient with a user evaluation coefficient threshold to determine suspicious users; The record audit assessment module is used to build a modification assessment model, generate a modification assessment coefficient, and compare the modification assessment coefficient of the suspicious user with the modification assessment coefficient threshold to determine the potential abnormal modification records of the suspicious user.

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