CN115101187B - Anesthesia machine operation fault prediction system based on big data - Google Patents
Anesthesia machine operation fault prediction system based on big data Download PDFInfo
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Abstract
The invention discloses an anesthesia machine operation fault prediction system based on big data, which relates to the technical field of anesthesia machine fault prediction and solves the technical problem that accurate fault prediction cannot be carried out through part types in the prior art, frequent fault parts of an analysis object are analyzed, the real-time state of the frequent fault parts of the analysis object is judged, whether the frequent fault parts of the analysis object can be maintained or not is accurately judged, the work efficiency of maintenance of the analysis object is improved, and equipment fault prediction is carried out according to the frequent fault parts of the analysis object; the real-time state of the occasionally failed part of the analysis object is judged, and the avoidability of the corresponding occasionally failed part is analyzed, so that the failure prevention efficiency of the analysis object in the operation process is improved; and predicting equipment faults of the corresponding analysis objects so as to reduce the influence caused by the faults of the analysis objects, improve the timeliness of fault maintenance of the analysis objects and ensure the operating efficiency of the analysis objects.
Description
Technical Field
The invention relates to the technical field of anesthesia machine fault prediction, in particular to an anesthesia machine operation fault prediction system based on big data.
Background
The anesthesia machine sends the anesthetic into the alveolus of the patient through a mechanical loop to form the gas partial pressure of the anesthetic, and after the gas partial pressure is dispersed into blood, the gas partial pressure directly inhibits the central nervous system, thereby generating the effect of general anesthesia. An anesthesia machine belongs to a semi-open type anesthesia device. It is mainly composed of anesthesia evaporation tank, flowmeter, folding bellows respirator, respiration loop (containing inhalation and exhalation one-way valve and manual air bag), corrugated pipeline and other parts.
However, in the prior art, the failure cause cannot be accurately analyzed in the operation process of the anesthesia machine, so that the accuracy of failure maintenance of the anesthesia machine is reduced, and the maintenance difficulty of the anesthesia machine is increased; meanwhile, the faults of the anesthesia machine cannot be accurately analyzed and predicted according to the type of the part, and the running state of the anesthesia machine cannot be analyzed in time in the running process, so that the real-time fault influence of the anesthesia machine cannot be reduced to the minimum.
In view of the above technical drawbacks, a solution is proposed.
Disclosure of Invention
The invention aims to solve the problems, and provides an anesthesia machine operation fault prediction system based on big data, which analyzes and judges the fault reason of anesthesia machine equipment through the historical operation process of the anesthesia machine equipment, thereby improving the maintenance pertinence of the anesthesia machine equipment, ensuring the working efficiency of the anesthesia machine equipment, and improving the prediction accuracy of the anesthesia machine equipment according to the fault reason of the anesthesia machine equipment; the equipment fault time period is obtained according to the fault time point of the selected equipment, the fault part of the analysis object is analyzed in the equipment fault time period, the fault prediction of the analysis object is carried out through the collection of the fault part, and the fault prediction accuracy of the equipment for running the analysis object is improved.
The purpose of the invention can be realized by the following technical scheme:
the utility model provides an anesthesia machine operation fault prediction system based on big data, includes the server, and the server communication is connected with:
the device historical operation analysis unit is used for analyzing and judging the failure reason of the anesthesia machine equipment through the historical operation process of the anesthesia machine equipment, marking the anesthesia machine equipment as an analysis object, acquiring the historical operation time period of the analysis object, dividing the historical operation time period into o sub-time points, dividing the sub-time points into a preselected device failure time point and a preselected artificial failure time point through analysis, then acquiring a selected device failure time point, a non-device failure time point, a selected artificial failure time point and a non-selected artificial failure time point through analysis, and sending the selected device failure time point, the non-device failure time point, the selected artificial failure time point and the non-selected artificial failure time point to the server;
the part type dividing unit is used for acquiring an equipment fault time period according to the selected equipment fault time point, analyzing fault parts of an analysis object in the equipment fault time period, dividing the analysis object into k parts, dividing the parts of the analysis object into frequent fault parts and occasional fault parts through analysis, and sending corresponding numbers of the frequent fault parts and the occasional fault parts to the server;
a frequent fault part analysis unit for analyzing a frequent fault part of an analysis object, dividing the frequent fault part of the analysis object into a high maintainability part and a low maintainability part by analysis, generating a high maintainability signal and a low maintainability signal, and transmitting the signals to a server;
the occasional fault part analysis unit is used for analyzing the occasional fault part of the analysis object, dividing the occasional fault part of the analysis object into a low-avoidance part and a high-avoidance part through analysis, and sending the corresponding serial number of the occasional fault part to the server;
and the equipment fault prediction unit is used for predicting equipment faults of the corresponding analysis object, acquiring an equipment fault prediction coefficient of the analysis object through analysis, comparing the equipment fault prediction coefficient of the analysis object to generate a high-risk fault signal and a low-risk fault signal, and sending the high-risk fault signal and the low-risk fault signal to the server.
As a preferred embodiment of the present invention, the operation process of the device historical operation analysis unit is as follows:
acquiring a historical operation time period of an analysis object, acquiring a sub-time point of the analysis object with a fault, marking the sub-time point as a fault time point, and marking the corresponding fault time point as a fault time point of the pre-selection equipment if the fault time point corresponds to the operator of the analysis object and is inconsistent; if the corresponding fault time points are consistent with the operators of the analysis objects, marking the corresponding fault time points as pre-selected artificial fault time points;
the shortest interval duration of the preselected equipment fault time point and the continuous frequency of the corresponding preselected equipment fault time point are collected and are respectively compared with the shortest interval duration threshold value and the continuous frequency threshold value:
if the shortest interval duration of the fault time point of the pre-selection equipment does not exceed the shortest interval duration threshold value or the continuous frequency of the fault time point of the corresponding pre-selection equipment exceeds the continuous frequency threshold value, marking the fault time point of the corresponding pre-selection equipment as the fault time point of the selected equipment; and if the shortest interval duration of the preselected device fault time point exceeds the shortest interval duration threshold and the continuous frequency of the corresponding preselected device fault time point does not exceed the continuous frequency threshold, marking the corresponding preselected device fault time point as a non-device fault time point.
As a preferred embodiment of the present invention, the coincidence frequency of a preselected artificial fault time point with an operator of an analysis object and the fault frequency of a corresponding operator operating a different analysis object are collected and compared with a coincidence frequency threshold and a fault frequency threshold, respectively:
if the consistent frequency of the preselected artificial fault time point and an operator of the analysis object exceeds the consistent frequency threshold, or the fault frequency of different analysis objects operated by corresponding operators exceeds the fault frequency threshold, marking the corresponding preselected artificial fault time point as a selected artificial fault time point; and if the consistent frequency of the preselected artificial fault time point and the operator of the analysis object does not exceed the consistent frequency threshold, and the fault frequency of the corresponding operator operating different analysis objects does not exceed the fault frequency threshold, marking the corresponding preselected artificial fault time point as a non-selected artificial fault time point.
As a preferred embodiment of the present invention, the operation process of the part classification unit is as follows:
acquiring the failure times of the corresponding part of the analysis object and the shortest interval duration of the failure of the corresponding part of the analysis object in the time period of the failure equipment, and respectively comparing the failure times with a failure time threshold and an interval duration threshold:
if the failure times of the corresponding parts of the analysis objects in the failure equipment time period exceed the failure time threshold, or the shortest interval duration of the failures of the corresponding parts of the analysis objects does not exceed the interval duration threshold, marking the corresponding failure parts of the analysis objects as frequent failure parts;
and if the failure times of the corresponding parts of the analysis object in the failure equipment time period do not exceed the failure time threshold, and the shortest interval duration of the failure of the corresponding parts of the analysis object exceeds the interval duration threshold, marking the corresponding failure parts of the analysis object as occasional failure parts.
As a preferred embodiment of the present invention, the frequent fault location analyzing unit operates as follows:
acquiring interval duration of a fault moment corresponding to the frequent fault part and a recent historical maintenance finishing moment and the increase speed of the maintenance time of the corresponding frequent fault part, and comparing the interval duration with an interval duration threshold and a time increase speed threshold respectively:
if the interval duration of the fault time corresponding to the frequent fault part and the latest historical maintenance finishing time exceeds an interval duration threshold value and the increase speed of the maintenance time consumption corresponding to the frequent fault part does not exceed a time consumption increase speed threshold value, marking the corresponding frequent fault part as a high maintainability part, generating a high maintainability signal and sending the high maintainability signal to a server; if the interval duration of the fault time corresponding to the frequent fault part and the latest historical maintenance ending time does not exceed the interval duration threshold, or the time consumption increasing speed of the maintenance of the corresponding frequent fault part exceeds the time consumption increasing speed threshold, marking the corresponding frequent fault part as a low-maintainability part, generating a low-maintainability signal and sending the low-maintainability signal to the server.
As a preferred embodiment of the present invention, the operation of the occasional failure analysis unit is as follows:
acquiring peripheral environment parameters of an analysis object at the current moment according to the occurrence moment of an occasional fault part of the analysis object, wherein the environment parameters are represented by temperature and humidity, acquiring the floating type quantity of the peripheral environment parameters when the analysis object is corresponding to the occasional fault part and the lowest floating value difference value of the same peripheral environment parameter when the analysis object is corresponding to the adjacent fault of the occasional fault part, and respectively comparing the floating type quantity threshold value and the floating value difference value threshold value:
if the type quantity of the peripheral environment parameters floating when the analysis object corresponding to the occasional fault part fails exceeds a floating type quantity threshold value, or the lowest floating value difference value of the same peripheral environment parameters when the analysis object corresponding to the occasional fault part adjacent fails exceeds a floating value difference value threshold value, marking the corresponding occasional fault part as a low-avoidance part; if the type quantity of the peripheral environment parameters floating when the analysis object corresponds to the occasional fault part and has faults does not exceed the threshold value of the floating type quantity, and the lowest floating value difference value of the same peripheral environment parameters when the analysis object corresponds to the occasional fault part and has adjacent faults does not exceed the threshold value of the floating value difference value, the corresponding occasional fault part is marked as a high-avoidance part.
As a preferred embodiment of the present invention, the operation process of the equipment failure prediction unit is as follows:
acquiring cycle shortening speed of a high maintainability part to a low maintainability part corresponding to an analysis object, and acquiring accident frequency of the high avoidability part corresponding to the analysis object and real-time qualified running time of the low avoidability part corresponding to the analysis object; acquiring an equipment fault prediction coefficient of an analysis object through analysis;
comparing the equipment failure prediction coefficient of the analysis object with an equipment failure prediction coefficient threshold value:
if the equipment failure prediction coefficient of the analysis object exceeds the equipment failure prediction coefficient threshold, judging that the equipment failure prediction of the corresponding analysis object is high in abnormal failure risk, generating a high-risk failure signal and sending the high-risk failure signal to a server; and if the equipment failure prediction coefficient of the analysis object does not exceed the equipment failure prediction coefficient threshold, judging that the equipment failure prediction of the corresponding analysis object is low in abnormal failure risk, generating a low-risk failure signal and sending the low-risk failure signal to the server.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the fault reason of the anesthesia machine equipment is analyzed and judged through the historical operation process of the anesthesia machine equipment, so that the maintenance pertinence of the anesthesia machine equipment is improved, the working efficiency of the anesthesia machine equipment is ensured, and the prediction accuracy of the anesthesia machine equipment is improved according to the fault reason of the anesthesia machine equipment; acquiring an equipment fault time period according to the selected equipment fault time point, analyzing the fault part of the analysis object in the equipment fault time period, and predicting the fault of the analysis object by acquiring the fault part, so that the fault prediction accuracy of the equipment for operating the analysis object is improved;
2. in the invention, the frequent fault part of the analysis object is analyzed, the real-time state of the frequent fault part of the analysis object is judged, and whether the frequent fault part of the analysis object can be maintained or not is accurately judged, so that the working efficiency of maintenance of the analysis object is improved, and meanwhile, equipment fault prediction is carried out according to the frequent fault part of the analysis object, thereby being beneficial to improving the accuracy of equipment fault prediction; the real-time state of the occasionally failed part of the analysis object is judged, and the avoidability of the corresponding occasionally failed part is analyzed, so that the failure prevention efficiency of the analysis object in the operation process is improved, the accuracy of failure prediction of the analysis object can be improved, and the risk of failure prediction result deviation is reduced; and predicting equipment faults of the corresponding analysis objects so as to reduce the influence caused by the faults of the analysis objects, improve the timeliness of fault maintenance of the analysis objects and ensure the operating efficiency of the analysis objects.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic block diagram of the present invention as a whole;
FIG. 2 is a schematic block diagram of embodiment 1 of the present invention;
fig. 3 is a schematic block diagram of embodiment 2 of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the anesthesia machine operation failure prediction system based on big data comprises a server, wherein the server is in communication connection with an equipment historical operation analysis unit, an equipment failure prediction unit, a part type division unit, an occasional part analysis unit and a frequent part analysis unit; the server is connected with the equipment historical operation analysis unit, the equipment fault prediction unit and the part type division unit in a bidirectional communication manner;
example 1
Referring to fig. 2, the server generates an equipment historical operation analysis signal and sends the equipment historical operation analysis signal to the equipment historical operation analysis unit, and after the equipment historical operation analysis unit receives the equipment historical operation analysis signal, the equipment historical operation analysis unit analyzes and judges the fault reason of the anesthesia machine equipment through the historical operation process of the anesthesia machine equipment, so that the maintenance pertinence of the anesthesia machine equipment is improved, the working efficiency of the anesthesia machine equipment is ensured, and the prediction accuracy of the anesthesia machine equipment is improved according to the fault reason of the anesthesia machine equipment;
marking anesthesia machine equipment as an analysis object, setting a label i, wherein i is a natural number greater than 1, acquiring a historical operation time period of the analysis object, dividing the historical operation time period into o sub time points, wherein o is a natural number greater than 1, acquiring the sub time point of the analysis object with a fault, marking the sub time point as a fault time point, and marking the corresponding fault time point as a preselected equipment fault time point if the fault time point is inconsistent with an operator of the analysis object; if the corresponding failure time point is consistent with the operator of the analysis object, marking the corresponding failure time point as a preselected artificial failure time point;
acquiring the shortest interval duration of the preselected device fault time point and the continuous frequency of the preselected device fault time point, and respectively comparing the shortest interval duration of the preselected device fault time point and the continuous frequency of the preselected device fault time point with a shortest interval duration threshold value and a continuous frequency threshold value:
if the shortest interval duration of the preselected device fault time point does not exceed the shortest interval duration threshold or the continuous frequency of the corresponding preselected device fault time point exceeds the continuous frequency threshold, marking the corresponding preselected device fault time point as the selected device fault time point; if the shortest interval duration of the preselected device fault time point exceeds the shortest interval duration threshold and the continuous frequency of the corresponding preselected device fault time point does not exceed the continuous frequency threshold, marking the corresponding preselected device fault time point as a non-device fault time point;
acquiring the consistent frequency of a preselected artificial fault time point and an operator of an analysis object and the fault frequencies of different analysis objects operated by corresponding operators, and respectively comparing the consistent frequency of the preselected artificial fault time point and the operator of the analysis object and the fault frequencies of different analysis objects operated by corresponding operators with a consistent frequency threshold and a fault frequency threshold:
if the consistency frequency of the preselected artificial fault time point and an operator of the analysis object exceeds the consistency frequency threshold value, or the fault frequency of the corresponding operator operating different analysis objects exceeds the fault frequency threshold value, marking the corresponding preselected artificial fault time point as a selected artificial fault time point; if the consistency frequency of the preselected artificial fault time point and the operator of the analysis object does not exceed the consistency frequency threshold value, and the fault frequency of the corresponding operator operating different analysis objects does not exceed the fault frequency threshold value, marking the corresponding preselected artificial fault time point as a non-selected artificial fault time point;
the selected equipment fault time point, the non-equipment fault time point, the selected man-made fault time point and the non-selected man-made fault time point are sent to a server, the server checks the maintenance of the corresponding analysis object after receiving the selected equipment fault time point and the non-equipment fault time point, and if the maintenance subject is not correct, the maintenance is carried out; the maintenance subject is denoted as equipment maintenance and personnel rectification;
the server generates a part category dividing signal and sends the part category dividing signal to the part category dividing unit, the part category dividing unit acquires an equipment failure time period according to a selected equipment failure time point after receiving the part category dividing signal, and simultaneously analyzes a failure part of an analysis object in the equipment failure time period, and the failure prediction of the analysis object is performed through the acquisition of the failure part, so that the failure prediction accuracy of the equipment for operating the analysis object is improved;
dividing an analysis object into k parts, wherein k is a positive integer greater than 1, acquiring the failure times of the corresponding part of the analysis object and the shortest interval duration of the failure of the corresponding part of the analysis object in a failure equipment time period, and comparing the failure times of the corresponding part of the analysis object and the shortest interval duration of the failure of the corresponding part of the analysis object in the failure equipment time period with a failure time threshold and an interval duration threshold respectively:
if the failure times of the corresponding parts of the analysis objects in the failure equipment time period exceed the failure time threshold, or the shortest interval duration of the failures of the corresponding parts of the analysis objects does not exceed the interval duration threshold, marking the corresponding failure parts of the analysis objects as frequent failure parts; if the failure times of the corresponding parts of the analysis object in the failure equipment time period do not exceed the failure time threshold, and the shortest interval duration of the failure of the corresponding parts of the analysis object exceeds the interval duration threshold, marking the corresponding failure parts of the analysis object as occasional failure parts;
then, the acquired frequent fault parts and the acquired occasional fault part corresponding numbers are sent to a server;
example 2
The part classification unit generates a frequent fault part analysis signal and an occasional fault part analysis signal, and respectively sends the frequent fault part analysis signal and the occasional fault part analysis signal to the frequent fault part analysis unit and the occasional fault part analysis unit; after receiving the frequent fault part analysis signal, the frequent fault part analysis unit analyzes the frequent fault part of the analysis object, judges the real-time state of the frequent fault part of the analysis object, and accurately judges whether the frequent fault part of the analysis object can be maintained, so that the working efficiency of maintenance of the analysis object is improved, and meanwhile, equipment fault prediction is carried out according to the frequent fault part of the analysis object, and the accuracy of equipment fault prediction is favorably improved;
acquiring interval duration of a fault time corresponding to the frequent fault part and a recent historical maintenance ending time and a time-consuming increase speed corresponding to the frequent fault part, and comparing the interval duration of the fault time corresponding to the frequent fault part and the recent historical maintenance ending time and the time-consuming increase speed corresponding to the frequent fault part with an interval duration threshold and a time-consuming increase speed threshold respectively:
if the interval duration of the fault time corresponding to the frequent fault part and the latest historical maintenance finishing time exceeds an interval duration threshold value and the increase speed of the maintenance time consumption corresponding to the frequent fault part does not exceed a time consumption increase speed threshold value, judging that the maintainability of the corresponding frequent fault part is high, marking the corresponding frequent fault part as a high maintainability part, generating a high maintainability signal and sending the high maintainability signal to a server;
if the interval duration of the fault time corresponding to the frequent fault part and the latest historical maintenance finishing time does not exceed the interval duration threshold, or the increase speed of the maintenance time consumption corresponding to the frequent fault part exceeds the time consumption increase speed threshold, judging that the maintainability of the corresponding frequent fault part is low, marking the corresponding frequent fault part as a low maintainability part, generating a low maintainability signal and sending the low maintainability signal to a server;
it can be understood that, the high maintainability part indicates that the maintenance value of the corresponding part is high, that is, the operation efficiency of the part after the fault maintenance is not greatly influenced, whereas, the low maintainability part indicates that the maintenance value of the corresponding part is low, that is, the operation efficiency of the part after the fault maintenance is greatly influenced, so that the part cannot be used;
the occasional fault part analysis unit analyzes the occasional fault part of the analysis object after receiving the occasional fault part analysis signal, judges the real-time state of the occasional fault part of the analysis object, and analyzes the avoidability of the corresponding occasional fault part, so that the fault prevention efficiency of the analysis object in the operation process is improved, the fault prediction accuracy of the analysis object can be improved, and the risk of fault prediction result deviation is reduced;
acquiring peripheral environment parameters of an analysis object at the current moment according to the occurrence moment of an occasional fault part of the analysis object, wherein the environment parameters are expressed by parameters such as temperature, humidity and the like, acquiring the floating type quantity of the peripheral environment parameters when the analysis object is corresponding to the occasional fault part and has a fault and the lowest floating value difference value of the same peripheral environment parameter when the analysis object is corresponding to the accidental fault part and has a fault, and comparing the floating type quantity of the peripheral environment parameters when the analysis object is corresponding to the accidental fault part and the lowest floating value difference value of the same peripheral environment parameter when the analysis object is corresponding to the accidental fault part and has a fault with the floating type quantity threshold value and the floating value difference threshold value respectively:
if the type quantity of the peripheral environment parameters floating when the analysis object corresponding to the occasional fault part fails exceeds a floating type quantity threshold value, or the lowest floating value difference value of the same peripheral environment parameters when the analysis object corresponding to the occasional fault part has adjacent faults exceeds a floating value difference value threshold value, judging that the avoidability corresponding to the occasional fault part is low, and marking the corresponding occasional fault part as a low-avoidability part;
if the type quantity of the peripheral environment parameter floating when the analysis object is corresponding to the fault of the occasional fault part does not exceed the floating type quantity threshold value, and the lowest floating value difference value of the same peripheral environment parameter when the analysis object is corresponding to the adjacent fault of the occasional fault part does not exceed the floating value difference value threshold value, judging that the avoidability of the corresponding occasional fault part is high, and marking the corresponding occasional fault part as a high-avoidability part;
it can be understood that the smaller the number of types of the environmental parameters and the smaller the floating difference value of the corresponding environmental parameters, the easier the screening and control of the environmental parameters affected by the current position are indicated, i.e. the fault avoidance can be improved;
sending the corresponding numbers of the low-avoidance part and the high-avoidance part to a server;
the server generates an equipment fault prediction signal and sends the equipment fault prediction signal to the equipment fault prediction unit, and the equipment fault prediction unit carries out equipment fault prediction on a corresponding analysis object after receiving the equipment fault prediction signal, so that the influence caused by the fault of the analysis object is reduced, the timeliness of fault maintenance of the analysis object is improved, and the operation efficiency of the analysis object is ensured;
acquiring the cycle shortening speed of the analysis object corresponding to the high maintainability part converted into the low maintainability part, and marking the cycle shortening speed of the analysis object corresponding to the high maintainability part converted into the low maintainability part as SDVi; acquiring accident frequency of a high-avoidance part corresponding to an analysis object and real-time qualified running time of a low-avoidance part corresponding to the analysis object, and respectively marking the accident frequency of the high-avoidance part corresponding to the analysis object and the real-time qualified running time of the low-avoidance part corresponding to the analysis object as SGPi and HGYi;
by the formula
Acquiring an equipment fault prediction coefficient Xi of an analysis object, wherein a1, a2 and a3 are all preset proportionality coefficients, and a1 is greater than a2 and greater than a3 is greater than 0; beta is an error correction factor, and the value is 1.36;
comparing the equipment failure prediction coefficient Xi of the analysis object with an equipment failure prediction coefficient threshold value:
if the equipment failure prediction coefficient Xi of the analysis object exceeds the equipment failure prediction coefficient threshold, judging that the equipment failure prediction of the corresponding analysis object is high in abnormal failure risk, generating a high-risk failure signal and sending the high-risk failure signal to a server; after receiving the high-risk fault signal, the server monitors the operation of the corresponding analysis object; and if the equipment failure prediction coefficient Xi of the analysis object does not exceed the equipment failure prediction coefficient threshold, judging that the equipment failure prediction of the corresponding analysis object is low in abnormal failure risk, generating a low-risk failure signal and sending the low-risk failure signal to the server.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions;
when the anesthesia machine fault detection device is used, the historical operation process of the anesthesia machine equipment is analyzed and judged through the equipment historical operation analysis unit to obtain the fault reason of the anesthesia machine equipment, the anesthesia machine equipment is marked as an analysis object, the historical operation time period of the analysis object is obtained, the historical operation time period is divided into o sub time points, the sub time points are divided into a preselected equipment fault time point and a preselected artificial fault time point through analysis, and then a selected equipment fault time point, a non-equipment fault time point, a selected artificial fault time point and a non-selected artificial fault time point are obtained through analysis and sent to a server; acquiring an equipment fault time period according to a selected equipment fault time point through a part type dividing unit, analyzing fault parts of an analysis object in the equipment fault time period, dividing the analysis object into k parts, dividing the parts of the analysis object into frequent fault parts and occasional fault parts through analysis, and sending corresponding numbers of the frequent fault parts and the occasional fault parts to a server; analyzing the frequent fault part of the analysis object by a frequent fault part analysis unit, dividing the frequent fault part of the analysis object into a high maintainability part and a low maintainability part by analysis, generating a high maintainability signal and a low maintainability signal, and sending the signals to a server; analyzing the occasional fault part of the analysis object by an occasional fault part analysis unit, dividing the occasional fault part of the analysis object into a low-avoidance part and a high-avoidance part by analysis, and sending the corresponding numbers of the occasional fault parts to a server; and performing equipment fault prediction on the corresponding analysis object through an equipment fault prediction unit, acquiring an equipment fault prediction coefficient of the analysis object through analysis, comparing the equipment fault prediction coefficient of the analysis object to generate a high-risk fault signal and a low-risk fault signal, and sending the high-risk fault signal and the low-risk fault signal to the server.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. The utility model provides an anesthesia machine operation fault prediction system based on big data which characterized in that, includes the server, and the server communication is connected with:
the device historical operation analysis unit is used for analyzing and judging the fault reason of the anesthesia machine device through the historical operation process of the anesthesia machine device, marking the anesthesia machine device as an analysis object, acquiring the historical operation time period of the analysis object, dividing the historical operation time period into o sub time points, dividing the sub time points into a preselected device fault time point and a preselected artificial fault time point through analysis, acquiring a selected device fault time point, a non-device fault time point, a selected artificial fault time point and a non-selected artificial fault time point through analysis, and sending the selected device fault time point, the non-device fault time point, the selected artificial fault time point and the non-selected artificial fault time point to the server;
the part type dividing unit is used for acquiring an equipment fault time period according to the selected equipment fault time point, analyzing fault parts of an analysis object in the equipment fault time period, dividing the analysis object into k parts, dividing the parts of the analysis object into frequent fault parts and occasional fault parts through analysis, and sending corresponding numbers of the frequent fault parts and the occasional fault parts to the server;
a frequent fault part analysis unit for analyzing a frequent fault part of an analysis object, dividing the frequent fault part of the analysis object into a high maintainability part and a low maintainability part by analysis, generating a high maintainability signal and a low maintainability signal, and transmitting the signals to a server;
the occasional fault part analysis unit is used for analyzing the occasional fault part of the analysis object, dividing the occasional fault part of the analysis object into a low-avoidance part and a high-avoidance part through analysis, and sending the corresponding numbers to the server;
the equipment fault prediction unit is used for predicting equipment faults of the corresponding analysis object, obtaining an equipment fault prediction coefficient of the analysis object through analysis, comparing the equipment fault prediction coefficient of the analysis object to generate a high-risk fault signal and a low-risk fault signal, and sending the high-risk fault signal and the low-risk fault signal to the server;
the operation process of the equipment failure prediction unit is as follows:
acquiring the cycle shortening speed of the analysis object corresponding to the high maintainability part converted into the low maintainability part, and marking the cycle shortening speed of the analysis object corresponding to the high maintainability part converted into the low maintainability part as SDVi; acquiring accident frequency of a high-avoidance part corresponding to an analysis object and real-time qualified running time of a low-avoidance part corresponding to the analysis object, and respectively marking the accident frequency of the high-avoidance part corresponding to the analysis object and the real-time qualified running time of the low-avoidance part corresponding to the analysis object as SGPi and HGYi;
by the formula
Acquiring an equipment fault prediction coefficient Xi of an analysis object, wherein a1, a2 and a3 are all preset proportionality coefficients, and a1 is greater than a2 and greater than a3 is greater than 0; beta is an error correction factor, and the value of beta is 1.36;
comparing the equipment failure prediction coefficient Xi of the analysis object with an equipment failure prediction coefficient threshold value:
if the equipment failure prediction coefficient of the analysis object exceeds the equipment failure prediction coefficient threshold, judging that the equipment failure prediction of the corresponding analysis object is high in abnormal failure risk, generating a high-risk failure signal and sending the high-risk failure signal to a server; and if the equipment failure prediction coefficient of the analysis object does not exceed the equipment failure prediction coefficient threshold, judging that the equipment failure prediction of the corresponding analysis object is low in abnormal failure risk, generating a low-risk failure signal and sending the low-risk failure signal to the server.
2. The big data-based anesthesia machine operation failure prediction system of claim 1, wherein the operation process of the equipment historical operation analysis unit is as follows:
acquiring a historical operation time period of an analysis object, acquiring a sub-time point of the analysis object with a fault, marking the sub-time point as a fault time point, and marking the corresponding fault time point as a fault time point of the pre-selection equipment if the fault time point corresponds to the operator of the analysis object and is inconsistent; if the corresponding fault time points are consistent with the operators of the analysis objects, marking the corresponding fault time points as pre-selected artificial fault time points;
the shortest interval duration of the preselected equipment fault time point and the continuous frequency of the corresponding preselected equipment fault time point are collected and are respectively compared with the shortest interval duration threshold value and the continuous frequency threshold value:
if the shortest interval duration of the fault time point of the pre-selection equipment does not exceed the shortest interval duration threshold value or the continuous frequency of the fault time point of the corresponding pre-selection equipment exceeds the continuous frequency threshold value, marking the fault time point of the corresponding pre-selection equipment as the fault time point of the selected equipment; and if the shortest interval duration of the preselected device fault time point exceeds the shortest interval duration threshold and the continuous frequency of the corresponding preselected device fault time point does not exceed the continuous frequency threshold, marking the corresponding preselected device fault time point as a non-device fault time point.
3. The big data-based anesthesia machine operation failure prediction system of claim 2, wherein the coincidence frequency of the preselected artificial failure time point with the operator of the analysis object and the failure frequency of the corresponding operator operating different analysis objects are collected and compared with the coincidence frequency threshold and the failure frequency threshold, respectively:
if the consistency frequency of the preselected artificial fault time point and an operator of the analysis object exceeds the consistency frequency threshold value, or the fault frequency of the corresponding operator operating different analysis objects exceeds the fault frequency threshold value, marking the corresponding preselected artificial fault time point as a selected artificial fault time point; if the consistency frequency of the preselected artificial fault time point and the operator of the analysis object does not exceed the consistency frequency threshold value, and the fault frequency of the corresponding operator operating different analysis objects does not exceed the fault frequency threshold value, marking the corresponding preselected artificial fault time point as a non-selected artificial fault time point.
4. The big data-based anesthesia machine operation fault prediction system according to claim 1, wherein the operation process of the part classification unit is as follows:
collecting the failure times of the corresponding parts of the analysis object and the shortest interval duration of the failure of the corresponding parts of the analysis object in the failure equipment time period, and respectively comparing the failure times with a failure time threshold and an interval duration threshold:
if the failure times of the corresponding parts of the analysis object in the failure equipment time period exceed a failure time threshold, or the shortest interval duration of the failure of the corresponding parts of the analysis object does not exceed an interval duration threshold, marking the corresponding failure parts of the analysis object as frequent failure parts;
and if the failure times of the corresponding parts of the analysis object in the failure equipment time period do not exceed the failure time threshold, and the shortest interval duration of the failure of the corresponding parts of the analysis object exceeds the interval duration threshold, marking the corresponding failure parts of the analysis object as occasional failure parts.
5. The big data-based anesthesia machine operation fault prediction system according to claim 1, wherein the frequent fault location analysis unit operates as follows:
acquiring interval duration of a fault moment corresponding to a frequent fault part and a recent historical maintenance ending moment and the increase speed of maintenance time of the corresponding frequent fault part, and comparing the interval duration with an interval duration threshold and a time consumption increase speed threshold respectively:
if the interval duration of the fault time corresponding to the frequent fault part and the latest historical maintenance ending time exceeds the interval duration threshold and the time-consuming increase speed corresponding to the frequent fault part does not exceed the time-consuming increase speed threshold, marking the corresponding frequent fault part as a high-maintainability part, generating a high-maintainability signal and sending the high-maintainability signal to a server; if the interval duration of the fault time corresponding to the frequent fault part and the latest historical maintenance ending time does not exceed the interval duration threshold, or the time consumption increasing speed of the maintenance of the corresponding frequent fault part exceeds the time consumption increasing speed threshold, marking the corresponding frequent fault part as a low-maintainability part, generating a low-maintainability signal and sending the low-maintainability signal to the server.
6. The big data-based anesthesia machine operation failure prediction system of claim 1, wherein the operation process of the occasional failure location analysis unit is as follows:
acquiring peripheral environment parameters of an analysis object at the current moment according to the occurrence moment of an occasional fault part of the analysis object, wherein the environment parameters are represented by temperature and humidity, acquiring the floating type quantity of the peripheral environment parameters when the analysis object is corresponding to the occasional fault part and the lowest floating value difference value of the same peripheral environment parameter when the analysis object is corresponding to the adjacent fault of the occasional fault part, and respectively comparing the floating type quantity threshold value and the floating value difference value threshold value:
if the type quantity of the peripheral environment parameters floating when the analysis object is corresponding to the occasional fault part and has faults exceeds a floating type quantity threshold value, or the lowest floating value difference value of the same peripheral environment parameters when the analysis object is corresponding to the adjacent faults of the occasional fault part and has faults exceeds a floating value difference value threshold value, the corresponding occasional fault part is marked as a low-avoidance part; if the type quantity of the peripheral environment parameters floating when the analysis object corresponds to the occasional fault part and has faults does not exceed the threshold value of the floating type quantity, and the lowest floating value difference value of the same peripheral environment parameters when the analysis object corresponds to the occasional fault part and has adjacent faults does not exceed the threshold value of the floating value difference value, the corresponding occasional fault part is marked as a high-avoidance part.
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