Medical equipment management system based on artificial intelligence

Abstract

The invention relates to the technical field of medical equipment management, in particular to a medical equipment management system based on artificial intelligence, which comprises a calling acquisition module, an information acquisition module, a medical equipment management module, a medical equipment management module and a medical equipment management module, and is characterized in that the calling acquisition module is used for acquiring calling application information, warehouse position coordinates and equipment inventory information of medical equipment warehouses in hospitals; the information acquisition module is used for acquiring regional information and equipment calling records of a hospital and also used for periodically acquiring equipment information after medical equipment is called out of a warehouse, and the position analysis module is used for analyzing equipment use departments and equipment position parameters; the operation analysis module is used for analyzing the equipment operation parameters; the historical analysis module is used for analyzing the historical operation parameters; the score analysis module is used for analyzing the score of the user; and the equipment management module is used for managing the calling of the medical equipment according to the calling application information, the equipment inventory information and the user score. According to the invention, accurate management of medical equipment calling is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of medical device management, and in particular to a medical equipment management system based on artificial intelligence. Background Art

[0002] With the continuous advancement of medical technology and the expansion of hospital scale, medical equipment call management has become a key link in hospital operations. The traditional manual management model can no longer meet the needs of efficiency and accuracy. In this context, relying on advanced technologies such as the Internet of Things, big data, and cloud computing, the medical equipment call management system came into being.

[0003] Chinese Patent Publication No.: CN113574606A discloses a medical device and its data management method, including receiving instructions for selecting locking rules to determine target locking rules; the locking rules are used to screen monitoring data for locking; under the triggering of a first trigger condition, the monitoring data is screened according to the target locking rules, and the screened monitoring data is locked; under the triggering of a second trigger condition, the monitoring data that is not locked is selectively deleted. This invention realizes the screening management of medical device usage data, but does not realize the management of medical device calls combined with user data and medical device usage data. There are problems such as low efficiency in analyzing medical device operation data and inaccurate management of medical device calls. Summary of the invention

[0004] To this end, the present invention provides a medical equipment management system based on artificial intelligence to overcome the problems of low efficiency in analyzing medical equipment operation data and inaccurate management of medical equipment calls in the prior art.

[0005] To achieve the above object, the present invention provides a medical equipment management system based on artificial intelligence, comprising:

[0006] The call acquisition module is used to obtain the call application information, warehouse location coordinates and equipment inventory information of the medical equipment warehouse in the hospital;

[0007] The information acquisition module is used to obtain the hospital's regional information and equipment call records, and is also used to periodically obtain equipment information after the medical equipment is called out of the warehouse;

[0008] Location analysis module, used to analyze equipment use department and equipment location parameters based on equipment information, warehouse location coordinates and regional information;

[0009] An operation analysis module is used to analyze the analysis cycle type according to the device location parameter, and is also used to analyze the device operation parameter according to the device information, the device location parameter and the analysis cycle type;

[0010] The historical analysis module is used to count the number of historical operations of users according to the device call records, and to analyze the historical operation parameters according to the device call records and the number of historical operations of users;

[0011] The scoring analysis module is used to analyze user scores based on device call records, device operating parameters, historical operating parameters and call application information;

[0012] The equipment management module is used to manage the call of medical equipment based on the call application information, equipment inventory information and user ratings.

[0013] Furthermore, the location analysis module analyzes the equipment use department according to the equipment location information and area information, wherein:

[0014] When the device location information is the device location coordinates and (x(i,j),y(i,j))∈U(k), the location analysis module determines that the device use department is the department corresponding to the current analysis department number k;

[0015] When the device location information is the device location distance, the location analysis module determines that the device use department is no department;

[0016] Among them, (x(i,j), y(i,j)) represents the device location coordinates, x(i,j) represents the horizontal coordinate of the device location coordinates, y(i,j) represents the vertical coordinate of the device location coordinates, i represents the device number, i∈N + , j represents the analysis period number, j∈N + , U(k) represents the regional coordinate set, k represents the department number, k∈N + , U(k)={(x,y)|X1(k)≤x≤X2(k),Y1(k)≤y≤Y2(k)}, X1(k) represents the horizontal coordinate range parameter of the first region, X2(k) represents the horizontal coordinate range parameter of the second region, Y1(k) represents the vertical coordinate range parameter of the first region, and Y2(k) represents the vertical coordinate range parameter of the second region.

[0017] Furthermore, the location analysis module analyzes the device location parameters according to the device location information and the warehouse location coordinates, wherein:

[0018] When the device location information is the device location coordinates, the location analysis module analyzes the device location parameters according to the device location coordinates and the warehouse location coordinates, sets the device location parameters to S(i, j), and sets

[0019] When the device location information is the device location distance, the location analysis module analyzes the device location parameter according to the device location distance, sets the device location parameter to S(i, j), and sets S(i, j)=s(i, j);

[0020] Among them, S(i,j) represents the equipment location parameters, (x1,y1) represents the warehouse location coordinates, ω represents the unit length of the hospital plane coordinate system, and s(i,j) represents the equipment location distance.

[0021] Furthermore, the operation analysis module is provided with a cycle classification unit, which is used to analyze the analysis cycle type according to the equipment location parameters, wherein:

[0022] When |S(i,j)-S(i,j-1)|≤α, the period classification unit determines that the analysis period type of the current analysis period is one type;

[0023] When |S(i,j)-S(i,j-1)|>α, the period classification unit determines that the analysis period type of the current analysis period is the second type;

[0024] Where S(i,j-1) represents the device location parameter of the previous analysis period, and α represents the location difference threshold.

[0025] Furthermore, the operation analysis module is further provided with an operation analysis unit, which is used to analyze the equipment operation parameters according to the analysis cycle type and the equipment operation information, wherein:

[0026] If the analysis cycle types in the n analysis cycles before the current analysis cycle are all of the same type, the operation analysis unit analyzes the equipment operation parameters and sets the equipment operation parameters to A(i, j).

[0027] If there are two types of analysis cycles in the n analysis cycles before the current analysis cycle, the operation analysis unit does not analyze the equipment operation parameters;

[0028] Where A(i,j) represents the equipment operating parameters, P(i,j) represents the equipment operating power, and p(i) represents the equipment rated power.

[0029] Furthermore, the history analysis module is provided with a history statistics unit, which is used to respectively count the number of analysis cycles in which the historical operation power of each device corresponding to the user name called by each device satisfies P(i,j,r)>0 as the user's historical operation number;

[0030] The historical analysis module is further provided with a historical analysis unit, which is used to calculate the historical operation parameters through a historical analysis formula according to the user's historical operation quantity, historical operation power and the device calling user name. The historical analysis unit is provided with a historical analysis formula as follows:

[0031]

[0032] Among them, Q(r) represents the historical operation parameters, r represents the device calling user name, P(i,j,r) represents the historical operation power, and N2(r) represents the user's historical operation quantity.

[0033] Further, the score analysis module is provided with a score analysis unit, which is used to take the time interval between the device call time and the scheduled return time as the first interval time, and the time interval between the scheduled return time and the actual return time as the second interval time. When the scheduled return time is earlier than the actual return time, the second interval time takes a negative value, and when the actual return time is earlier than the scheduled return time, the second interval time takes a positive value, and calculates the user score according to the device call user name, the first interval time and the second interval time through a score analysis formula. The score analysis unit is provided with a score analysis formula as follows:

[0034] W(r)=100+F(r),

[0035]

[0036]

[0037] Wherein, W(r) represents the user score, F(r) represents the score adjustment parameter, f(r,i,z) represents the time score parameter, o1 represents the first score threshold, o2 represents the second score threshold, t1(r,i,z) represents the first interval duration, t2(r,i,z) represents the second interval duration, and γ represents the interval comparison threshold.

[0038] Furthermore, the score analysis module is further provided with a score adjustment unit, which is used to make adjustments according to the analysis process of user scores of device operating parameters and historical operating parameters, wherein:

[0039] When A(i,j) / Q(r)≥η, the score adjustment unit determines that the device operating parameters and the historical operating parameters meet the threshold value, and does not adjust the analysis process of the user score;

[0040] When A(i,j) / Q(r)<η, the score adjustment unit determines that the equipment operating parameters and the historical operating parameters do not meet the threshold value, and adjusts the analysis process of the score adjustment parameter, and the adjusted score adjustment parameter is F1(r);

[0041] Wherein, η represents the running comparison threshold.

[0042] Furthermore, the score analysis module is further provided with a score optimization unit, which is used to optimize the adjustment process of user scores according to the equipment use department and user category, wherein:

[0043] When the user category is individual, the score optimization unit does not optimize the adjustment process of the user score;

[0044] When the user category is a department doctor, the score optimization unit counts the number of analysis cycles of the same department as the department operation number, and optimizes the adjustment process of the user score according to the department operation number. If [N(k)+N(k) max ] / Nj>λ, the score optimization unit does not optimize the adjustment process of user scores. If [N(k)+N(k) max ] / Nj≤λ, the scoring optimization unit optimizes the adjustment process of the score adjustment parameter, and the optimized score adjustment parameter is F2(r);

[0045] Among them, N(k) represents the number of department operations, N(k) max It represents the maximum value of the number of department operations, Nj represents the number of equipment operation cycles, and λ represents the optimization comparison threshold.

[0046] Furthermore, the device management module manages the calling of medical devices according to the calling application information, the device inventory information and the user rating, wherein:

[0047] When W(r=R)≥w, the equipment management module allows the user to call the medical equipment in the medical equipment warehouse whose equipment operation status is normal;

[0048] When W(r=R)<w, the equipment management module does not allow the user to call the medical equipment in the medical equipment warehouse;

[0049] Where R represents the user name and w represents the call comparison threshold.

[0050] Compared with the prior art, the beneficial effect of the present invention lies in that, by acquiring the call application information, warehouse location coordinates, equipment inventory information, regional information, equipment call records and equipment information through the call acquisition module and the information acquisition module, and comprehensive analysis of user data, equipment call records and equipment operation data by other modules, the user score and the operation status of the medical equipment in the medical equipment warehouse are analyzed, and the judgment of the user's application for calling the medical equipment and whether the medical equipment in the warehouse can work stably are realized, thereby improving the system's analysis efficiency of the medical equipment operation data and improving the accuracy of the medical equipment call management. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a schematic diagram of the structure of the medical equipment management system based on artificial intelligence in this embodiment.

[0052] Figure 2This is a schematic diagram of the structure of the operation analysis module of this embodiment.

[0053] Figure 3 Schematic diagram of the structure of the history analysis module of this embodiment.

[0054] Figure 4 Schematic diagram of the structure of the scoring analysis module in this embodiment. DETAILED DESCRIPTION

[0055] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0057] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] See also Figure 1 As shown, it is a medical equipment management system based on artificial intelligence in this embodiment, including:

[0059] A call acquisition module is used to acquire the call application information, warehouse location coordinates and equipment inventory information of the medical equipment warehouse in the hospital, wherein the call application information includes the user name, user category and application usage duration, wherein the user category includes individuals and department doctors, wherein the department doctors include dentists, surgeons and internal medicine doctors, etc., wherein the warehouse location coordinates are the coordinates of the location of the medical equipment warehouse in the hospital plane coordinate system, wherein the equipment inventory information includes the equipment inventory quantity and the equipment operation status, wherein the equipment operation status includes normal operation and abnormal operation, wherein the call application information and the equipment inventory information are acquired by importing the medical equipment warehouse management cloud data, and wherein the warehouse location coordinates are acquired by user interactive input;

[0060] The information acquisition module is used to obtain the regional information and equipment call records of the hospital, and is also used to periodically obtain equipment information after the medical equipment is called out of the warehouse. The equipment call record includes the equipment call user name, equipment call time, scheduled return time, actual return time and historical equipment operating power. The equipment call time is the time when the medical equipment is called and shipped out of the medical equipment warehouse, the scheduled return time is the scheduled return time of the called medical equipment, and the actual return time is the time when the medical equipment is actually returned to the medical equipment warehouse. The equipment information includes equipment operation information and equipment location information. The equipment operation information includes equipment operation power and equipment rated power. The equipment location information includes equipment location coordinates and equipment location distance. When the medical equipment is located in the hospital, the equipment location information is the equipment location coordinates, which are the plane coordinates of the equipment in each area of ​​the hospital. When the medical equipment is located outside the hospital, the equipment location information is the equipment location distance, which is the straight-line distance between the medical equipment and the hospital, and its unit is m. The regional information includes regional ownership and regional coordinate set. The regional ownership is the corresponding unit of each area in the hospital. Department, the regional coordinate set represents the set of coordinate points corresponding to each area in the hospital, the coordinate points are the coordinates in the hospital plane coordinate system, the hospital plane coordinate system is a coordinate system constructed according to the hospital plan, the construction of the coordinate system is not specifically limited in this embodiment, and technicians in this field can freely set it, such as taking the lower left corner of the circumscribed rectangle of the hospital plan as the coordinate origin, and the two sides adjacent to the coordinate origin as the x-axis and the y-axis respectively, the x-axis increases from left to right, and the y-axis increases from bottom to top, the unit length is 0.001 meters, and a plane rectangular coordinate system is established, and the coordinates corresponding to each position in the hospital are represented by plane coordinates. The acquisition frequency of the equipment information in this embodiment is once per preset analysis cycle, and the duration of the preset analysis cycle in this embodiment is set to 15 minutes. The setting of the preset analysis cycle is not specifically limited in this embodiment, and technicians in this field can freely set it, such as 3 minutes, 5 minutes and 20 minutes, etc. The acquisition method of the equipment call record is to import the cloud data through the medical equipment warehouse management, and the acquisition method of the equipment information is to import and obtain through the medical equipment operation data management platform;

[0061] A location analysis module, used to analyze the equipment use department and equipment location parameters according to the equipment information, warehouse location coordinates and area information, the location analysis module is connected to the call acquisition module and the information acquisition module;

[0062] An operation analysis module, used for analyzing the analysis cycle type according to the device location parameter, and also for analyzing the device operation parameter according to the device information, the device location parameter and the analysis cycle type, the operation analysis module being connected to the location analysis module;

[0063] A history analysis module, used to count the number of historical operations of the user according to the device call record, and to analyze the historical operation parameters according to the device call record and the number of historical operations of the user, the history analysis module is connected to the information acquisition module;

[0064] A scoring analysis module, used to analyze user scores based on device call records, device operating parameters, historical operating parameters and call application information, and the scoring analysis module is connected to the historical analysis module and the operating analysis module;

[0065] The equipment management module is used to manage the calling of medical equipment according to the calling application information, equipment inventory information and user scores, and the equipment management module is connected to the score analysis module.

[0066] Specifically, this embodiment is applied to the medical equipment warehouse management cloud in the hospital. By acquiring and analyzing the call information and operation information of the medical equipment, the operation status of each medical equipment and the score of the user who applied for the call are determined, and the call management of the medical equipment in the medical equipment warehouse is realized.

[0067] Specifically, the location analysis module in this embodiment analyzes the equipment use department according to the equipment location information and area information, wherein:

[0068] When the device location information is the device location coordinates and (x(i,j),y(i,j))∈U(k), the location analysis module determines that the device use department is the department corresponding to the current analysis department number k;

[0069] When the device location information is the device location distance, the location analysis module determines that the device use department is no department;

[0070] Among them, (x(i,j), y(i,j)) represents the device location coordinates, x(i,j) represents the horizontal coordinate of the device location coordinates, y(i,j) represents the vertical coordinate of the device location coordinates, i represents the device number, i∈N + , j represents the analysis period number, j∈N + , U(k) represents the regional coordinate set, k represents the department number, k∈N + , U(k)={(x,y)|X1(k)≤x≤X2(k),Y1(k)≤y≤Y2(k)}, X1(k) represents the horizontal coordinate range parameter of the first region, X2(k) represents the horizontal coordinate range parameter of the second region, Y1(k) represents the vertical coordinate range parameter of the first region, and Y2(k) represents the vertical coordinate range parameter of the second region.

[0071] Specifically, the equipment number described in this embodiment is a number for distinguishing different medical equipment, the analysis cycle number is a number for distinguishing different analysis cycles, the analysis cycle number is a number set in chronological order every preset analysis cycle duration, and the department number is a number for distinguishing different departments in the hospital, such as k=1 indicates that the department currently being analyzed is the Department of Dentistry, k=2 indicates that the department currently being analyzed is the Department of Internal Medicine, etc.

[0072] Specifically, the location analysis module in this embodiment analyzes the device location parameters according to the device location information and the warehouse location coordinates, wherein:

[0073] When the device location information is the device location coordinates, the location analysis module analyzes the device location parameters according to the device location coordinates and the warehouse location coordinates, sets the device location parameters to S(i, j), and sets

[0074] When the device location information is the device location distance, the location analysis module analyzes the device location parameter according to the device location distance, sets the device location parameter to S(i, j), and sets S(i, j)=s(i, j);

[0075] Among them, S(i,j) represents the equipment location parameters, (x1,y1) represents the warehouse location coordinates, ω represents the unit length of the hospital plane coordinate system, and s(i,j) represents the equipment location distance.

[0076] Specifically, in this embodiment, the location analysis module analyzes the equipment information, warehouse location coordinates and area information to analyze the equipment usage department and equipment location parameters. The equipment usage department is used to represent the area in the hospital corresponding to the location of the medical equipment, and the equipment location parameters are used to represent the distance relationship between the medical equipment and the medical equipment warehouse, thereby improving the system's analysis efficiency of medical equipment operation data and improving the accuracy of medical equipment call management.

[0077] See also Figure 2 As shown, the operation analysis module includes:

[0078] A cycle classification unit for analyzing the analysis cycle type according to the equipment location parameters;

[0079] An operation analysis unit, used for analyzing the equipment operation parameters according to the analysis cycle type and the equipment operation information, the operation analysis unit being connected to the cycle classification unit;

[0080] A distance analysis unit, used to analyze the periodic movement distance according to the device position parameter, and also used to analyze the distance fluctuation parameter according to the analysis period type and the device position parameter, the distance analysis unit is connected to the operation analysis unit;

[0081] An operation adjustment unit, used for adjusting the analysis process of the equipment operation parameters according to the periodic movement distance, the operation adjustment unit being connected to the distance analysis unit;

[0082] The operation optimization unit is used to optimize the adjustment process of the equipment operation parameters according to the distance fluctuation parameters, and the operation optimization unit is connected to the operation adjustment unit.

[0083] Specifically, the cycle classification unit in this embodiment analyzes the analysis cycle type according to the device location parameter, wherein:

[0084] When |S(i,j)-S(i,j-1)|≤α, the period classification unit determines that the analysis period type of the current analysis period is one type;

[0085] When |S(i,j)-S(i,j-1)|>α, the period classification unit determines that the analysis period type of the current analysis period is the second type;

[0086] Wherein, S(i,j-1) represents the device position parameter of the previous analysis cycle, α represents the position difference threshold, and 0.3≤α<1. It can be understood that the value of the position difference threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it meets the analysis of the analysis cycle type. The optimal value of the position difference threshold is: α=0.5.

[0087] Specifically, in this embodiment, the device position parameters are analyzed by the cycle classification unit to analyze the analysis cycle type. Class one indicates that the medical device does not move significantly in adjacent cycles, and class two indicates that the medical device moves significantly in adjacent cycles. The analysis cycles are divided into two categories based on the positional relationship of the medical equipment, thereby improving the system's analysis efficiency of medical device operation data and improving the accuracy of medical equipment call management.

[0088] Specifically, the operation analysis unit in this embodiment analyzes the equipment operation parameters according to the analysis cycle type and the equipment operation information, wherein:

[0089] If the analysis cycle types in the n analysis cycles before the current analysis cycle are all of the same type, the operation analysis unit analyzes the equipment operation parameters and sets the equipment operation parameters to A(i, j).

[0090] If there are two types of analysis cycles in the n analysis cycles before the current analysis cycle, the operation analysis unit does not analyze the equipment operation parameters;

[0091] Wherein, A(i,j) represents the equipment operating parameters, P(i,j) represents the equipment operating power, p(i) represents the equipment rated power, n represents the cycle range threshold, 1≤n≤5, and v represents the cycle range parameter, v∈N, v≤n. It can be understood that in this embodiment, the value of the cycle range threshold is not specifically limited, and those skilled in the art can freely set it as long as it meets the analysis of the equipment operating parameters. The optimal value of the cycle range threshold is: n=3.

[0092] Specifically, in this embodiment, the operation analysis unit analyzes the analysis cycle type and the equipment operation information to analyze the equipment operation parameters, and uses the equipment operation parameters to represent the equipment operation status of the medical equipment when it is not moving continuously and significantly, thereby improving the system's analysis efficiency of the medical equipment operation data and improving the accuracy of medical equipment call management.

[0093] Specifically, the distance analysis unit in this embodiment calculates the periodic movement distance according to the device position parameter by using a first distance analysis formula. The distance analysis unit is provided with a first distance analysis formula as follows:

[0094]

[0095] Wherein, L1(i,j) represents the periodic moving distance.

[0096] Specifically, the distance analysis unit in this embodiment takes the analysis cycle number of the first analysis cycle type of the second category before the current analysis cycle as the continuous termination number, and takes the difference between the analysis cycle number of the current analysis cycle and the continuous termination number as the number of continuous cycles, and calculates the distance fluctuation parameter through the second distance analysis formula according to the number of continuous cycles and the device location parameter. The distance analysis unit is provided with a second distance analysis formula as follows:

[0097]

[0098]

[0099] Among them, L2(i,j) represents the distance fluctuation parameter, m represents the continuous period parameter, m∈N, m≤N1(i,j), N1(i,j) represents the number of continuous periods, and s1(i,j) represents the continuous mean parameter.

[0100] Specifically, in this embodiment, the distance analysis unit analyzes the device location parameters and the analysis cycle type to analyze the periodic movement distance and the distance fluctuation parameters. The periodic movement distance is used to represent the average movement distance of the medical device in the most recent analysis cycles, and the distance fluctuation parameter is used to represent the distance fluctuation of the device in a continuous type of analysis cycles, thereby improving the system's analysis efficiency of medical device operation data and improving the accuracy of medical device call management.

[0101] Specifically, the operation adjustment unit in this embodiment adjusts the analysis process of the equipment operation parameters according to the periodic movement distance, wherein:

[0102] When L1(i,j)≤α, the operation adjustment unit determines that the periodic movement distance does not meet the threshold value and does not adjust the analysis process of the equipment operation parameters;

[0103] When α<L1(i,j)≤1, the operation adjustment unit determines that the periodic movement distance meets the threshold value, and adjusts the analysis process of the equipment operation parameters. The adjusted equipment operation parameters are A1(i,j), and A1(i,j)=A(i,j)×e 1 -L1(i,j) ;

[0104] When L1(i,j)>1, the operation adjustment unit determines that the periodic movement distance does not meet the threshold value and does not adjust the analysis process of the equipment operation parameters.

[0105] Specifically, in this embodiment, the operation adjustment unit analyzes the periodic movement distance to adjust the analysis process of the equipment operation parameters, so that the adjusted equipment operation parameters are related to the movement distance of the medical equipment in the most recent analysis cycles, thereby reducing the impact of external manipulation on the fluctuation of the medical equipment operation data analysis, thereby improving the system's analysis efficiency of the medical equipment operation data and improving the accuracy of medical equipment call management.

[0106] Specifically, the operation optimization unit in this embodiment optimizes the adjustment process of the equipment operation parameters according to the distance fluctuation parameters, wherein:

[0107] When L2(i,j)≤β, the operation optimization unit determines that the distance fluctuation parameter meets the threshold value, optimizes the adjustment process of the equipment operation parameter, and the optimized equipment operation parameter is A2(i,j), and A2(i,j)=A1(i,j)×L2(i,j) / L1(i,j);

[0108] When L2(i,j)>β, the operation optimization unit determines that the distance fluctuation parameter does not meet the threshold value and does not optimize the adjustment process of the equipment operation parameter;

[0109] Wherein, β represents the fluctuation comparison threshold, 0.6≤β≤1. It can be understood that the value of the fluctuation comparison threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it, as long as the optimization of the equipment operation parameter adjustment process is satisfied. The optimal value of the fluctuation comparison threshold is: β=0.8.

[0110] Specifically, in this embodiment, the operation optimization unit analyzes the distance fluctuation parameters to optimize the adjustment process of the equipment operation parameters, so that the optimized equipment operation parameters are related to the fluctuation of the equipment distance change, thereby improving the system's analysis efficiency of medical equipment operation data and improving the accuracy of medical equipment call management.

[0111] See also Figure 3 As shown, the historical analysis module includes:

[0112] The historical statistics unit is used to analyze the historical operation quantity of the user according to the historical operation power of the equipment;

[0113] The history analysis unit is used to analyze the historical operation parameters according to the historical operation power of the equipment and the user name of the equipment calling, and the history analysis unit is connected to the history statistics unit.

[0114] Specifically, the historical statistics unit in this embodiment respectively counts the historical operating power of each device corresponding to the user name called by each device to satisfy P(i,j,r)>0,i∈N + ,j∈N + , the number of analysis cycles is taken as the user's historical running number.

[0115] Specifically, the historical analysis unit in this embodiment calculates the historical operation parameters according to the user's historical operation quantity, historical operation power and the device calling user name through the historical analysis formula. The historical analysis unit is provided with the following historical analysis formula:

[0116]

[0117] Among them, Q(r) represents the historical operation parameters, r represents the device calling user name, P(i,j,r) represents the historical operation power, and N2(r) represents the user's historical operation quantity.

[0118] Specifically, in this embodiment, the historical analysis unit analyzes the user's historical operation quantity, historical operation power and device calling user name to analyze the historical operation parameters, and uses the historical operation parameters to represent the operation status of the medical device when the user historically calls and uses the medical device, thereby improving the system's analysis efficiency of the medical device operation data and improving the accuracy of medical device call management.

[0119] See also Figure 4As shown, the scoring analysis module includes:

[0120] A rating analysis unit, used to analyze user ratings based on the device calling user name, device calling time, scheduled return time, and actual return time;

[0121] A score adjustment unit, used to adjust the analysis process of user scores according to the device operating parameters and historical operating parameters, and the score adjustment unit is connected to the score analysis unit;

[0122] The scoring optimization unit is used to optimize the adjustment process of the user scoring according to the equipment use department and user category, and the scoring optimization unit is connected to the scoring adjustment unit.

[0123] Specifically, the score analysis unit in this embodiment uses the time interval between the device call time and the scheduled return time as the first interval time, and uses the time interval between the scheduled return time and the actual return time as the second interval time. When the scheduled return time is earlier than the actual return time, the second interval time takes a negative value, and when the actual return time is earlier than the scheduled return time, the second interval time takes a positive value, and calculates the user score according to the device call user name, the first interval time and the second interval time through the score analysis formula. The score analysis unit is provided with a score analysis formula as follows:

[0124] W(r)=100+F(r),

[0125]

[0126]

[0127] Wherein, W(r) represents the user score, F(r) represents the adjustment parameter, f(r,i,z) represents the time score parameter, o1 represents the first score threshold, -5≤o1≤-1, o2 represents the second score threshold, 0<o2≤2, t1(r,i,z) represents the first interval duration, t2(r,i,z) represents the second interval duration, γ represents the interval comparison threshold, -180≤γ≤-60. It can be understood that in this embodiment, the values ​​of the score threshold and the interval comparison threshold are not specifically limited, and those skilled in the art can freely set them as long as they meet the analysis of the user score. The optimal values ​​of the score threshold and the interval comparison threshold are: o1=-5, o2=1, γ=-120.

[0128] Specifically, in this embodiment, the scoring analysis unit analyzes the device call time, scheduled return time and actual return time to analyze the user score, and uses the user score to represent the scoring relationship between the user's agreed usage time and the actual usage time in the historical call data, thereby achieving the scoring of the user's usage reputation based on the user's historical call data, thereby improving the system's analysis efficiency of medical device operation data and improving the accuracy of medical device call management.

[0129] Specifically, the score adjustment unit in this embodiment makes adjustments according to the analysis process of the user scores of the device operating parameters and the historical operating parameters, wherein:

[0130] When A(i,j) / Q(r)≥η, the score adjustment unit determines that the device operating parameters and the historical operating parameters meet the threshold value, and does not adjust the analysis process of the user score;

[0131] When A(i,j) / Q(r)<η, the score adjustment unit determines that the equipment operating parameters and historical operating parameters do not meet the threshold, and adjusts the analysis process of the adjustment parameters. The adjusted adjustment parameters are F1(r). Set

[0132] Wherein, η represents the operation comparison threshold, 0.8≤η<1. It can be understood that the value of the operation comparison threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it, as long as the adjustment of the adjustment parameters is satisfied. The optimal value of the operation comparison threshold is: η=0.85.

[0133] Specifically, in this embodiment, the scoring adjustment unit analyzes the device operating parameters and historical operating parameters to adjust the analysis process of the user score, so that the adjusted user score is related to the standard situation of the user's use of the medical device, thereby improving the system's analysis efficiency of the medical device operating data and improving the accuracy of the medical device call management.

[0134] Specifically, the score optimization unit in this embodiment performs optimization according to the adjustment process of the user scores of the equipment use departments and user categories, wherein:

[0135] When the user category is individual, the score optimization unit does not optimize the adjustment process of the user score;

[0136] When the user category is a department doctor, the score optimization unit counts the number of analysis cycles of the same department as the department operation number, and optimizes the adjustment process of the user score according to the department operation number. If [N(k)+N(k) max ] / Nj>λ, the score optimization unit does not optimize the adjustment process of user scores. If [N(k)+N(k)max ] / Nj≤λ, the score optimization unit optimizes the adjustment process of the score adjustment parameter, and the optimized score adjustment parameter is F2(r), and F2(r)=F1(r)×e N(k) / Nj ;

[0137] Among them, N(k) represents the number of department operations, N(k) max Represents the maximum value of the number of department operations, Nj represents the number of equipment operation cycles, λ represents the optimization comparison threshold, 0.7≤λ≤0.9. The number of equipment operation cycles is defined as the number of analysis cycles that have passed between the current time and the equipment call time. It can be understood that in this embodiment, no specific limitation is made on the value of the optimization comparison threshold, and those skilled in the art can freely set it as long as the optimization of the adjustment process of the adjustment parameters is satisfied. The best value of the optimization comparison threshold is: λ=0.8.

[0138] Specifically, in this embodiment, the scoring optimization unit analyzes the equipment-using departments and user categories to optimize the user scoring adjustment process, so that the optimized user scoring is related to the location where the medical equipment stays in the hospital, reducing the impact of possible emergency usage on the user scoring, thereby improving the system's analysis efficiency of medical equipment operation data and improving the accuracy of medical equipment call management.

[0139] Specifically, the device management module in this embodiment analyzes the device operation status according to historical operation parameters, wherein:

[0140] When Q(r)≤q, the device management module determines that the device operation state is abnormal;

[0141] When Q(r)>q, the device management module determines that the device operation status is normal;

[0142] Wherein, q represents the state comparison threshold, 0.7≤q≤0.8. It can be understood that the value of the state comparison threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it satisfies the analysis of the equipment operation state. The optimal value of the state comparison threshold is: q=0.75.

[0143] Specifically, the device management module in this embodiment manages the calling of medical devices according to the calling application information, the device inventory information and the user rating, wherein:

[0144] When W(r=R)≥w, the equipment management module allows the user to call the medical equipment in the medical equipment warehouse whose equipment operation status is normal;

[0145] When W(r=R)<w, the equipment management module does not allow the user to call the medical equipment in the medical equipment warehouse;

[0146] Wherein, R represents the user name, w represents the call comparison threshold, and 70≤w≤90. It can be understood that the value of the call comparison threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it satisfies the call of the medical device. The optimal value of the call comparison threshold is: w=80.

[0147] Specifically, when the device management module in this embodiment compares the user scores, if there is no device calling the same user name as the user name, the user score is set to W(R)=100.

[0148] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A medical equipment management system based on artificial intelligence, characterized in that: include: The call acquisition module is used to obtain the call application information, warehouse location coordinates and equipment inventory information of the medical equipment warehouse in the hospital; The information acquisition module is used to obtain the hospital's regional information and equipment call records, and is also used to periodically obtain equipment information after the medical equipment is called out of the warehouse; Location analysis module, used to analyze equipment use department and equipment location parameters based on equipment information, warehouse location coordinates and regional information; An operation analysis module is used to analyze the analysis cycle type according to the device location parameter, and is also used to analyze the device operation parameter according to the device information, the device location parameter and the analysis cycle type; The historical analysis module is used to count the number of historical operations of users according to the device call records, and to analyze the historical operation parameters according to the device call records and the number of historical operations of users; The scoring analysis module is used to analyze user scores based on device call records, device operating parameters, historical operating parameters and call application information; The equipment management module is used to manage the call of medical equipment based on the call application information, equipment inventory information and user ratings.

2. The medical equipment management system based on artificial intelligence according to claim 1 is characterized in that: The location analysis module analyzes the equipment use department based on the equipment location information and area information, wherein: When the device location information is the device location coordinates and (x(i,j),y(i,j))∈U(k), the location analysis module determines that the device use department is the department corresponding to the current analysis department number k; When the device location information is the device location distance, the location analysis module determines that the device use department is no department; Among them, (x(i,j), y(i,j)) represents the device location coordinates, x(i,j) represents the horizontal coordinate of the device location coordinates, y(i,j) represents the vertical coordinate of the device location coordinates, i represents the device number, i∈N + , j represents the analysis period number, j∈N + , U(k) represents the regional coordinate set, k represents the department number, k∈N + , U(k)={(x,y)|X1(k)≤x≤X2(k),Y1(k)≤y≤Y2(k)}, X1(k) represents the horizontal coordinate range parameter of the first region, X2(k) represents the horizontal coordinate range parameter of the second region, Y1(k) represents the vertical coordinate range parameter of the first region, and Y2(k) represents the vertical coordinate range parameter of the second region.

3. The medical equipment management system based on artificial intelligence according to claim 2 is characterized in that: The location analysis module analyzes the device location parameters according to the device location information and the warehouse location coordinates, wherein: When the device location information is the device location coordinates, the location analysis module analyzes the device location parameters according to the device location coordinates and the warehouse location coordinates, sets the device location parameters to S(i, j), and sets When the device location information is the device location distance, the location analysis module analyzes the device location parameter according to the device location distance, sets the device location parameter to S(i, j), and sets S(i, j)=s(i, j); Among them, S(i,j) represents the equipment location parameters, (x1,y1) represents the warehouse location coordinates, ω represents the unit length of the hospital plane coordinate system, and s(i,j) represents the equipment location distance.

4. The medical equipment management system based on artificial intelligence according to claim 3 is characterized in that: The operation analysis module is provided with a cycle classification unit, which is used to analyze the analysis cycle type according to the equipment location parameters, wherein: When |S(i,j)-S(i,j-1)|≤α, the period classification unit determines that the analysis period type of the current analysis period is one type; When |S(i,j)-S(i,j-1)|>α, the period classification unit determines that the analysis period type of the current analysis period is the second type; Where S(i,j-1) represents the device location parameter of the previous analysis period, and α represents the location difference threshold.

5. The artificial intelligence-based medical equipment management system according to claim 4, characterized in that: The operation analysis module is further provided with an operation analysis unit, which is used to analyze the equipment operation parameters according to the analysis cycle type and the equipment operation information, wherein: If the analysis cycle types in the n analysis cycles before the current analysis cycle are all of the same type, the operation analysis unit analyzes the equipment operation parameters and sets the equipment operation parameters to A(i, j). If there are two types of analysis cycles in the n analysis cycles before the current analysis cycle, the operation analysis unit does not analyze the equipment operation parameters; Where A(i,j) represents the equipment operating parameters, P(i,j) represents the equipment operating power, and p(i) represents the equipment rated power.

6. The artificial intelligence-based medical equipment management system according to claim 5, characterized in that: The history analysis module is provided with a history statistics unit, which is used to count the number of analysis cycles in which the historical operation power of each device corresponding to the user name called by each device satisfies P(i,j,r)>0 as the user's historical operation number; The historical analysis module is further provided with a historical analysis unit, which is used to calculate the historical operation parameters through a historical analysis formula according to the user's historical operation quantity, historical operation power and the device calling user name. The historical analysis unit is provided with a historical analysis formula as follows: Among them, Q(r) represents the historical operation parameters, r represents the device calling user name, P(i,j,r) represents the historical operation power, and N2(r) represents the user's historical operation quantity.

7. The artificial intelligence-based medical equipment management system according to claim 6, characterized in that: The scoring analysis module is provided with a scoring analysis unit, which is used to use the time interval between the device call time and the scheduled return time as the first interval time, and the time interval between the scheduled return time and the actual return time as the second interval time. When the scheduled return time is earlier than the actual return time, the second interval time takes a negative value, and when the actual return time is earlier than the scheduled return time, the second interval time takes a positive value, and calculates the user score according to the device call user name, the first interval time and the second interval time through a scoring analysis formula. The scoring analysis unit is provided with a scoring analysis formula as follows: W(r)=100+F(r), Wherein, W(r) represents the user score, F(r) represents the score adjustment parameter, f(r,i,z) represents the time score parameter, o1 represents the first score threshold, o2 represents the second score threshold, t1(r,i,z) represents the first interval duration, t2(r,i,z) represents the second interval duration, and γ represents the interval comparison threshold.

8. The artificial intelligence-based medical equipment management system according to claim 7, characterized in that: The score analysis module is further provided with a score adjustment unit, which is used to make adjustments according to the analysis process of user scores of device operating parameters and historical operating parameters, wherein: When A(i,j) / Q(r)≥η, the score adjustment unit determines that the device operating parameters and the historical operating parameters meet the threshold value, and does not adjust the analysis process of the user score; When A(i,j) / Q(r)<η, the score adjustment unit determines that the equipment operating parameters and the historical operating parameters do not meet the threshold value, and adjusts the analysis process of the score adjustment parameter, and the adjusted score adjustment parameter is F1(r); Wherein, η represents the running comparison threshold.

9. The artificial intelligence-based medical equipment management system according to claim 8, characterized in that: The score analysis module is also provided with a score optimization unit, which is used to optimize the adjustment process of user scores according to the equipment use department and user category, wherein: When the user category is individual, the score optimization unit does not optimize the adjustment process of the user score; When the user category is a department doctor, the score optimization unit counts the number of analysis cycles of the same department as the department operation number, and optimizes the adjustment process of the user score according to the department operation number. If [N(k)+N(k) max ] / Nj>λ, the score optimization unit does not optimize the adjustment process of user scores. If [N(k)+N(k) max ] / Nj≤λ, the scoring optimization unit optimizes the adjustment process of the score adjustment parameter, and the optimized score adjustment parameter is F2(r); Among them, N(k) represents the number of department operations, N(k) max It represents the maximum value of the number of department operations, Nj represents the number of equipment operation cycles, and λ represents the optimization comparison threshold.

10. The medical equipment management system based on artificial intelligence according to claim 9, characterized in that: The equipment management module manages the calling of medical equipment according to the calling application information, equipment inventory information and user ratings, wherein: When W(r=R)≥w, the equipment management module allows the user to call the medical equipment in the medical equipment warehouse whose equipment operation status is normal; When W(r=R)<w, the equipment management module does not allow the user to call the medical equipment in the medical equipment warehouse; Where R represents the user name and w represents the call comparison threshold.

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