CN117373635B - Intelligent management method and system for medical equipment - Google Patents
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Abstract
The invention belongs to the field of equipment management, relates to the technical field of data analysis, and aims to solve the problem that an existing intelligent management method of medical equipment cannot accurately correct a detection result of each equipment through an integral running state, in particular to an intelligent management method and an intelligent management system of the medical equipment, wherein the intelligent management method comprises an intelligent management platform which is in communication connection with an effective performance detection module, a linear analysis module, a prediction analysis module and a storage module; the efficiency detection module is used for detecting and analyzing the operation efficiency and the productivity of the medical equipment: marking medical equipment for performance detection as a detection object; the invention can detect and analyze the operation efficiency and the productivity of the medical equipment, comprehensively analyze and calculate the parameters such as the medical equipment consumable, the operation time length and the like in a single working day to obtain the efficiency coefficient, and mark the medical equipment differently through the efficiency coefficient, so as to detect and remind the raw materials when the abnormal operation phenomenon occurs in the whole.
Description
Technical Field
The invention belongs to the field of equipment management, relates to a data analysis technology, and particularly relates to an intelligent management method and system for medical equipment.
Background
The medical equipment refers to instruments, equipment, appliances, materials or other articles used for a human body, and also comprises needed software, and the medical equipment is the most basic element of medical treatment, scientific research, teaching, institutions and clinical discipline work, and comprises professional medical equipment and household medical equipment;
the existing intelligent management method of the medical equipment cannot feed back the operation state of the whole medical equipment through the operation detection result of the single equipment, and cannot accurately correct the detection result of each equipment through the whole operation state, so that the actual operation state of the equipment cannot be effectively monitored.
Aiming at the technical problems, the application provides a solution.
Disclosure of Invention
The invention aims to provide an intelligent management method and system of medical equipment, which are used for solving the problem that the existing intelligent management method of the medical equipment cannot accurately correct the detection result of each equipment through the whole running state;
the technical problems to be solved by the invention are as follows: how to provide an intelligent management method and system for medical equipment, which can accurately correct the detection result of each equipment through the whole running state.
The aim of the invention can be achieved by the following technical scheme:
an intelligent management system of medical equipment comprises an intelligent management platform, wherein the intelligent management platform is in communication connection with an effective energy detection module, a linear analysis module, a prediction analysis module and a storage module;
the efficiency detection module is used for detecting and analyzing the operation efficiency and the productivity of the medical equipment: marking medical equipment for performance detection as a detection object, and acquiring consumable data HC, operation data YX and time length data SC of the detection object in a working day; obtaining the efficiency coefficient XN of the detection object in the working days by carrying out numerical calculation on consumable data HC, operation data YX and duration data SC; acquiring an efficacy threshold XNmax through a storage module, comparing an efficacy coefficient XN of a detection object with the efficacy threshold XNmax, and judging whether the whole operation state of the medical equipment in the working day is normal or not according to a comparison result;
the linear analysis module is used for carrying out fluctuation detection on the efficiency detection result of the medical equipment: generating a detection period, marking the times of the detection object marked as an abnormal object in the normal day in the detection period as different-standard data YB, establishing a performance set by the performance coefficients XN of the detection object in all the normal days in the detection period, performing variance calculation on the performance set to obtain effective wave data XB, and performing numerical calculation on the different-standard data YB and the effective wave data XB to obtain a linear coefficient XX of the detection object; judging whether the running stability of the detection object in the detection period meets the requirement or not through a linear coefficient XX;
the prediction analysis module is used for performing prediction analysis on the residual life of the medical equipment.
As a preferred embodiment of the present invention, the consumable data HC is a raw material quantity value consumed by the test object in the working day, the operation data YX is the working times of the test object in the working day, and the duration data SC is a total working duration value of the test object in the working day.
As a preferred embodiment of the present invention, the specific process of comparing the efficacy factor XN of the test object with the efficacy threshold XNmax includes: if the efficiency coefficient XN is smaller than the efficiency threshold XNmax, judging that the efficiency detection result of the detection object in the working day is qualified, and marking the corresponding detection object as a qualified object; if the efficiency coefficient XN is greater than or equal to the efficiency threshold XNmax, judging that the efficiency detection result of the detection object in the working day is unqualified, and marking the corresponding detection object as an abnormal object; the number ratio of the abnormal objects to the detection objects in the working days is marked as an abnormal coefficient of the working days, an abnormal threshold value is obtained through the storage module, the abnormal coefficient is compared with the abnormal threshold value, and the working days are marked as normal days or abnormal days through the comparison result.
As a preferred embodiment of the present invention, the specific process of comparing the anomaly coefficient with the anomaly threshold value includes: if the abnormality coefficient is smaller than the abnormality threshold, judging that the whole medical equipment in the working day operates normally, and marking the corresponding working day as a normal day; if the abnormality coefficient is greater than or equal to the abnormality threshold, judging that the whole operation of the medical equipment in the working day is abnormal, marking the corresponding working day as the abnormal day, generating a raw material detection signal and sending the raw material detection signal to the intelligent management platform, and sending the raw material detection signal to a mobile phone terminal of a manager after the intelligent management platform receives the raw material detection signal.
As a preferred embodiment of the present invention, the specific process for determining whether the running stability of the test object in the test period satisfies the requirement includes: obtaining a linear threshold value XXmax through a storage module, and comparing a linear coefficient XX of a detection object in a detection period with the linear threshold value XXmax: if the linear coefficient XX is smaller than the linear threshold XXmax, judging that the running stability of the detection object in the detection period meets the requirement; if the linear coefficient XX is greater than or equal to the linear threshold XXmax, judging that the running stability of the detection object in the detection period does not meet the requirement, generating a predictive analysis signal and sending the predictive analysis signal to an intelligent management platform, and sending the predictive analysis signal to a predictive analysis module after the intelligent management platform receives the predictive analysis signal.
As a preferred embodiment of the present invention, the specific process of the predictive analysis module for performing predictive analysis on the remaining life of the medical device includes: marking a normal day of a detection object marked as an abnormal object in a detection period as a marked day, marking a date difference value of adjacent marked days as an interval value, marking an average value of all interval values of the detection object in the detection period as a concentration coefficient, acquiring a concentration threshold value through a storage module, and comparing the concentration coefficient with the concentration threshold value: if the centralized value is smaller than the centralized threshold value, generating an overhaul training signal and sending the overhaul training signal to the intelligent management platform, and after receiving the overhaul training signal, the intelligent management platform sends the overhaul training signal to a mobile phone terminal of a manager; if the concentration value is greater than or equal to the concentration threshold value, marking the date difference value of the abnormal day and the adjacent marked day as an abnormal difference value, marking the average value of all abnormal difference values of the detected object in the detection period as an abnormal difference coefficient, acquiring the abnormal difference threshold value through a storage module, comparing the abnormal difference coefficient with the abnormal difference threshold value, and generating a raw material optimization signal or a life early warning signal through a comparison result.
As a preferred embodiment of the present invention, the specific process of comparing the difference coefficient with the difference threshold value includes: if the difference coefficient is smaller than the difference threshold, generating a raw material optimizing signal and sending the raw material optimizing signal to an intelligent management platform, and after receiving the raw material optimizing signal, the intelligent management platform sends the raw material optimizing signal to a mobile phone terminal of a manager; if the difference coefficient is greater than or equal to the difference threshold, generating a life early-warning signal and sending the life early-warning signal to the intelligent management platform, and after receiving the life early-warning signal, the intelligent management platform sends the life early-warning signal to a mobile phone terminal of a manager.
An intelligent management method of medical equipment, comprising the following steps:
step one: detecting and analyzing the operation efficiency and the productivity of the medical equipment: marking medical equipment for performance detection as a detection object, acquiring consumable data HC, operation data YX and time length data SC of the detection object in a working day, and performing numerical calculation to obtain a performance coefficient XN;
step two: marking the detection object as a qualified object or an abnormal object through the efficiency coefficient XN, marking the quantity ratio of the abnormal object to the detection object as an abnormal coefficient, and marking the working day as a normal day or an abnormal day through the abnormal coefficient;
step three: performing fluctuation detection on the efficiency detection result of the medical equipment: generating a detection period, obtaining different standard data YB and effective wave data XB of a detection object in the detection period, performing numerical value calculation to obtain a linear coefficient XX, and judging whether the running stability of the detection object in the detection period meets the requirement or not through the linear coefficient XX;
step four: predictive analysis of remaining life of medical devices: and acquiring a concentration coefficient and a difference coefficient of the detection object in the detection period, generating an overhaul training signal, a raw material optimization signal or a life early warning signal through the concentration coefficient and the difference coefficient, and sending the overhaul training signal, the raw material optimization signal or the life early warning signal to the intelligent management platform.
The invention has the following beneficial effects:
1. the efficiency detection module can be used for detecting and analyzing the operation efficiency and the productivity of the medical equipment, comprehensively analyzing and calculating parameters such as medical equipment consumables, operation time length and the like in a single working day to obtain efficiency coefficients, marking the medical equipment differently through the efficiency coefficients, marking the working day according to the marking condition of an abnormal object in the working day, and carrying out raw material detection reminding when the abnormal operation phenomenon occurs in the whole;
2. the efficiency detection result of the medical equipment can be subjected to fluctuation detection through the linear analysis module, the stability degree of the efficiency coefficient of the medical equipment is monitored in a periodic detection mode, meanwhile, the influence of abnormal days of large-area operation abnormal phenomena in the detection period on the linear analysis result is eliminated, the whole data is combined with single linear data, and the accuracy of the efficiency detection result of the equipment is improved;
3. the prediction analysis module can be used for predicting and analyzing the residual life of the medical equipment, judging whether the life early warning of the medical equipment is needed or not by the marked frequency of the detection object in the detection period and the influence degree of the abnormal mark on the quality abnormality of the raw materials, and optimizing the operation in other modes when the life early warning is not needed.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a flowchart of a method according to a second embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in FIG. 1, an intelligent management system of a medical device comprises an intelligent management platform, wherein the intelligent management platform is in communication connection with an effective energy detection module, a linear analysis module, a predictive analysis module and a storage module.
The efficiency detection module is used for detecting and analyzing the operation efficiency and the productivity of the medical equipment: marking medical equipment for performance detection as a detection object, and acquiring consumable data HC, operation data YX and time length data SC of the detection object in a working day, wherein the consumable data HC is the raw material quantity value consumed by the detection object in the working day, the operation data YX is the working times of the detection object in the working day, and the time length data SC is the total working time length value of the detection object in the working day; obtaining the efficiency coefficient XN of the detection object in the working days through a formula XN= (alpha 1 x HC+alpha 2 x SC)/(alpha 3 x YX), wherein alpha 1, alpha 2 and alpha 3 are all proportional coefficients, and alpha 1 is more than alpha 2 is more than alpha 3 is more than 1; the method comprises the steps of obtaining a performance threshold XNmax through a storage module, and comparing a performance coefficient XN of a detected object with the performance threshold XNmax: if the efficiency coefficient XN is smaller than the efficiency threshold XNmax, judging that the efficiency detection result of the detection object in the working day is qualified, and marking the corresponding detection object as a qualified object; if the efficiency coefficient XN is greater than or equal to the efficiency threshold XNmax, judging that the efficiency detection result of the detection object in the working day is unqualified, and marking the corresponding detection object as an abnormal object; marking the number ratio of the abnormal objects to the detection objects in the working days as an abnormal coefficient in the working days, acquiring an abnormal threshold value through a storage module, and comparing the abnormal coefficient with the abnormal threshold value: if the abnormality coefficient is smaller than the abnormality threshold, judging that the whole medical equipment in the working day operates normally, and marking the corresponding working day as a normal day; if the abnormality coefficient is greater than or equal to an abnormality threshold, judging that the whole operation of the medical equipment in the working day is abnormal, marking the corresponding working day as an abnormal day, generating a raw material detection signal and sending the raw material detection signal to an intelligent management platform, and sending the raw material detection signal to a mobile phone terminal of a manager after the intelligent management platform receives the raw material detection signal; the method comprises the steps of detecting and analyzing the operation efficiency and the productivity of medical equipment, comprehensively analyzing and calculating parameters such as medical equipment consumable materials, operation time length and the like in a single working day to obtain efficiency coefficients, marking the medical equipment differently through the efficiency coefficients, marking the working day according to the marking condition of abnormal objects in the working day, and detecting and reminding raw materials when the abnormal operation phenomenon occurs in the whole.
The linear analysis module is used for carrying out fluctuation detection on the efficiency detection result of the medical equipment: generating a detection period, marking the times of the detection object marked as an abnormal object in a normal day in the detection period as different standard data YB, establishing a performance set by the performance coefficients XN of the detection object in all normal days in the detection period, performing variance calculation on the performance set to obtain effective wave data XB, and obtaining a linear coefficient XX of the detection object through a formula XX=β1XYB+β2XB, wherein β1 and β2 are both proportional coefficients, and β2 is larger than β1 and larger than 1; obtaining a linear threshold value XXmax through a storage module, and comparing a linear coefficient XX of a detection object in a detection period with the linear threshold value XXmax: if the linear coefficient XX is smaller than the linear threshold XXmax, judging that the running stability of the detection object in the detection period meets the requirement; if the linear coefficient XX is greater than or equal to the linear threshold XXmax, judging that the running stability of the detection object in the detection period does not meet the requirement, generating a predictive analysis signal and sending the predictive analysis signal to an intelligent management platform, and sending the predictive analysis signal to a predictive analysis module after the intelligent management platform receives the predictive analysis signal; the fluctuation detection is carried out on the efficiency detection result of the medical equipment, the stability degree of the efficiency coefficient of the medical equipment is monitored in a periodical detection mode, meanwhile, the influence of abnormal days of large-area operation abnormal phenomena in the detection period on the linear analysis result is eliminated, the whole data and the single linear data are combined, and the accuracy of the efficiency detection result of the equipment is improved.
The prediction analysis module is used for performing prediction analysis on the residual life of the medical equipment: marking a normal day of a detection object marked as an abnormal object in a detection period as a marked day, marking a date difference value of adjacent marked days as an interval value, marking an average value of all interval values of the detection object in the detection period as a concentration coefficient, acquiring a concentration threshold value through a storage module, and comparing the concentration coefficient with the concentration threshold value: if the centralized value is smaller than the centralized threshold value, generating an overhaul training signal and sending the overhaul training signal to the intelligent management platform, and after receiving the overhaul training signal, the intelligent management platform sends the overhaul training signal to a mobile phone terminal of a manager; if the concentration value is greater than or equal to the concentration threshold value, marking the date difference value of the abnormal date and the adjacent marked date as an abnormal difference value, marking the average value of all abnormal difference values of the detected object in the detection period as an abnormal difference coefficient, acquiring the abnormal difference threshold value through a storage module, and comparing the abnormal difference coefficient with the abnormal difference threshold value: if the difference coefficient is smaller than the difference threshold, generating a raw material optimizing signal and sending the raw material optimizing signal to an intelligent management platform, and after receiving the raw material optimizing signal, the intelligent management platform sends the raw material optimizing signal to a mobile phone terminal of a manager; if the difference coefficient is greater than or equal to the difference threshold, generating a life early-warning signal and sending the life early-warning signal to the intelligent management platform, and after receiving the life early-warning signal, the intelligent management platform sends the life early-warning signal to a mobile phone terminal of a manager; and predicting and analyzing the residual life of the medical equipment, judging whether the life early warning is needed by the medical equipment or not by judging the marked frequency of the detection object in the detection period and the influence degree of the abnormal mark on the quality abnormality of the raw materials, and optimizing the operation by adopting other modes when the life early warning is not needed.
Example two
As shown in fig. 2, an intelligent management method of a medical device includes the following steps:
step one: detecting and analyzing the operation efficiency and the productivity of the medical equipment: marking medical equipment for performance detection as a detection object, acquiring consumable data HC, operation data YX and time length data SC of the detection object in a working day, and performing numerical calculation to obtain a performance coefficient XN;
step two: marking the detection object as a qualified object or an abnormal object through the efficiency coefficient XN, marking the quantity ratio of the abnormal object to the detection object as an abnormal coefficient, and marking the working day as a normal day or an abnormal day through the abnormal coefficient;
step three: performing fluctuation detection on the efficiency detection result of the medical equipment: generating a detection period, obtaining different standard data YB and effective wave data XB of a detection object in the detection period, performing numerical value calculation to obtain a linear coefficient XX, and judging whether the running stability of the detection object in the detection period meets the requirement or not through the linear coefficient XX;
step four: predictive analysis of remaining life of medical devices: and acquiring a concentration coefficient and a difference coefficient of the detection object in the detection period, generating an overhaul training signal, a raw material optimization signal or a life early warning signal through the concentration coefficient and the difference coefficient, and sending the overhaul training signal, the raw material optimization signal or the life early warning signal to the intelligent management platform.
The intelligent management method and system of the medical equipment, in operation, the medical equipment for performance detection is marked as a detection object, consumable data HC, operation data YX and time length data SC of the detection object in a working day are obtained, and a performance coefficient XN is obtained by numerical value calculation; marking the detection object as a qualified object or an abnormal object through the efficiency coefficient XN, marking the quantity ratio of the abnormal object to the detection object as an abnormal coefficient, and marking the working day as a normal day or an abnormal day through the abnormal coefficient; generating a detection period, obtaining different standard data YB and effective wave data XB of a detection object in the detection period, performing numerical value calculation to obtain a linear coefficient XX, and judging whether the running stability of the detection object in the detection period meets the requirement or not through the linear coefficient XX; and acquiring a concentration coefficient and a difference coefficient of the detection object in the detection period, generating an overhaul training signal, a raw material optimization signal or a life early warning signal through the concentration coefficient and the difference coefficient, and sending the overhaul training signal, the raw material optimization signal or the life early warning signal to the intelligent management platform.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: the formula xn= (α1hc+α2sc)/(α3 yx); collecting a plurality of groups of sample data by a person skilled in the art and setting corresponding efficiency coefficients for each group of sample data; substituting the set efficiency coefficient and the acquired sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient, and taking an average value to obtain values of alpha 1, alpha 2 and alpha 3 which are respectively 3.25, 2.87 and 2.24;
the foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
The size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding efficiency coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the efficiency coefficient is in direct proportion to the value of the consumable data.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form 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 and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (1)
1. The intelligent management system of the medical equipment is characterized by comprising an intelligent management platform, wherein the intelligent management platform is in communication connection with an effective energy detection module, a linear analysis module, a prediction analysis module and a storage module;
the efficiency detection module is used for detecting and analyzing the operation efficiency and the productivity of the medical equipment: marking medical equipment for performance detection as a detection object, and acquiring consumable data HC, operation data YX and time length data SC of the detection object in a working day; obtaining the efficiency coefficient XN of the detection object in the working days by carrying out numerical calculation on consumable data HC, operation data YX and duration data SC; acquiring an efficacy threshold XNmax through a storage module, comparing an efficacy coefficient XN of a detection object with the efficacy threshold XNmax, and judging whether the whole operation state of the medical equipment in the working day is normal or not according to a comparison result;
the linear analysis module is used for carrying out fluctuation detection on the efficiency detection result of the medical equipment: generating a detection period, marking the times of the detection object marked as an abnormal object in the normal day in the detection period as different-standard data YB, establishing a performance set by the performance coefficients XN of the detection object in all the normal days in the detection period, performing variance calculation on the performance set to obtain effective wave data XB, and performing numerical calculation on the different-standard data YB and the effective wave data XB to obtain a linear coefficient XX of the detection object; judging whether the running stability of the detection object in the detection period meets the requirement or not through a linear coefficient XX;
the prediction analysis module is used for performing prediction analysis on the residual life of the medical equipment;
consumable data HC is the raw material quantity value consumed by the detection object in the working day, operation data YX is the working times of the detection object in the working day, and duration data SC is the total working duration value of the detection object in the working day;
the specific process of comparing the efficiency factor XN of the test object with the efficiency threshold XNmax includes: if the efficiency coefficient XN is smaller than the efficiency threshold XNmax, judging that the efficiency detection result of the detection object in the working day is qualified, and marking the corresponding detection object as a qualified object; if the efficiency coefficient XN is greater than or equal to the efficiency threshold XNmax, judging that the efficiency detection result of the detection object in the working day is unqualified, and marking the corresponding detection object as an abnormal object; marking the number ratio of the abnormal objects to the detection objects in the working days as an abnormal coefficient of the working days, acquiring an abnormal threshold value through a storage module, comparing the abnormal coefficient with the abnormal threshold value, and marking the working days as normal days or abnormal days through a comparison result;
the specific process of comparing the anomaly coefficient with the anomaly threshold comprises: if the abnormality coefficient is smaller than the abnormality threshold, judging that the whole medical equipment in the working day operates normally, and marking the corresponding working day as a normal day; if the abnormality coefficient is greater than or equal to an abnormality threshold, judging that the whole operation of the medical equipment in the working day is abnormal, marking the corresponding working day as an abnormal day, generating a raw material detection signal and sending the raw material detection signal to an intelligent management platform, and sending the raw material detection signal to a mobile phone terminal of a manager after the intelligent management platform receives the raw material detection signal;
the specific process for judging whether the running stability of the detection object in the detection period meets the requirement comprises the following steps: obtaining a linear threshold value XXmax through a storage module, and comparing a linear coefficient XX of a detection object in a detection period with the linear threshold value XXmax: if the linear coefficient XX is smaller than the linear threshold XXmax, judging that the running stability of the detection object in the detection period meets the requirement; if the linear coefficient XX is greater than or equal to the linear threshold XXmax, judging that the running stability of the detection object in the detection period does not meet the requirement, generating a predictive analysis signal and sending the predictive analysis signal to an intelligent management platform, and sending the predictive analysis signal to a predictive analysis module after the intelligent management platform receives the predictive analysis signal;
the specific process of the predictive analysis module for performing predictive analysis on the remaining life of the medical equipment comprises the following steps: marking a normal day of a detection object marked as an abnormal object in a detection period as a marked day, marking a date difference value of adjacent marked days as an interval value, marking an average value of all interval values of the detection object in the detection period as a concentration coefficient, acquiring a concentration threshold value through a storage module, and comparing the concentration coefficient with the concentration threshold value: if the centralized value is smaller than the centralized threshold value, generating an overhaul training signal and sending the overhaul training signal to the intelligent management platform, and after receiving the overhaul training signal, the intelligent management platform sends the overhaul training signal to a mobile phone terminal of a manager; if the concentration value is greater than or equal to the concentration threshold value, marking the date difference value of the abnormal day and the adjacent marked day as an abnormal difference value, marking the average value of all abnormal difference values of the detected object in the detection period as an abnormal difference coefficient, acquiring the abnormal difference threshold value through a storage module, comparing the abnormal difference coefficient with the abnormal difference threshold value, and generating a raw material optimization signal or a life early warning signal through a comparison result;
the specific process of comparing the difference coefficient with the difference threshold value includes: if the difference coefficient is smaller than the difference threshold, generating a raw material optimizing signal and sending the raw material optimizing signal to an intelligent management platform, and after receiving the raw material optimizing signal, the intelligent management platform sends the raw material optimizing signal to a mobile phone terminal of a manager;
if the difference coefficient is greater than or equal to the difference threshold, generating a life early-warning signal and sending the life early-warning signal to the intelligent management platform, and after receiving the life early-warning signal, the intelligent management platform sends the life early-warning signal to a mobile phone terminal of a manager.
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