Equipment data analysis management system and method applying temperature sensor

Abstract

The invention discloses an equipment data analysis management system and method applying a temperature sensor, and relates to the technical field of equipment data analysis. Equipment operation data is analyzed, and then fault feature evaluation data of equipment is generated; calculating an equipment fault characteristic value according to characteristic data when the equipment breaks down in the history; analyzing an abnormal association degree between the fault characteristic value and the corresponding first environment data; constructing a fault feature fluctuation model according to the abnormal association combination; building an environment correlation fitting model according to the environment data correlation set, and building an environment confidence fitting model through the environment correlation fitting model of more than two kinds of other environment data; analyzing the operation condition of the equipment by using the characteristic fluctuation model; generating a fault risk early warning according to the operation condition of the equipment; and judging the real situation of fault risk early warning according to the environment confidence fitting model. The monitoring cost of a user can be reduced, and the comprehensiveness and reliability of a monitoring system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment data analysis, and in particular to an equipment data analysis management system and method using a temperature sensor. Background Art

[0002] Temperature sensors can monitor the temperature changes of equipment in real time. By monitoring the temperature changes of equipment in real time, temperature sensors can help to promptly detect potential problems such as equipment overheating and avoid equipment damage due to overheating. Correspondingly, when equipment failure occurs, a large amount of heat energy is often generated. There is an implicit correlation between the data monitored by the temperature sensor and the operating status of the equipment.

[0003] In the prior art, the single use of temperature sensors to analyze equipment data is easily affected by environmental interference, resulting in inaccurate judgments; if the sensor fails, the equipment status cannot be accurately monitored; a single data source cannot fully reflect the complex operating status of the equipment and lacks comprehensiveness and reliability.

[0004] Therefore, the present invention discloses an equipment data analysis management system and method using a temperature sensor to solve the above problems. Summary of the invention

[0005] The object of the present invention is to provide a device data analysis management system and method using a temperature sensor to solve the problems raised in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: a device data analysis and management method using a temperature sensor, the method comprising the following steps: S1: Install sensors on the equipment and use them to collect environmental data. Extract equipment operation data when failures occurred during the equipment's historical operation, analyze the equipment operation data, and generate equipment failure feature evaluation data. S2: Calculate the equipment fault characteristic value according to the characteristic data when the equipment fails in history; analyze the abnormal correlation between the fault characteristic value and the corresponding first environment data; generate an abnormal correlation combination according to the abnormal correlation, and construct a fault characteristic fluctuation model according to the abnormal correlation combination; S3: extracting other environmental data except the first environmental data when the device fails in the history, forming an environmental data association set according to the first environmental data and other environmental data, establishing an environmental association fitting model according to the environmental data association set, and constructing an environmental confidence fitting model through the environmental association fitting models of two or more other environmental data; S4: Collect real-time first environment data and use the characteristic fluctuation model to analyze the operation status of the equipment; generate a fault risk warning based on the operation status of the equipment; and determine the actual situation of the fault risk warning based on the environmental confidence fitting model.

[0007] According to the above scheme, S1 includes the following contents: S101: marking and extracting the equipment operation data of each equipment when a fault occurs, and generating an equipment fault data set; the equipment operation data includes current value, voltage value, operation power and load; S102: Set weights for each device operation data, and calculate the device failure operation value according to the device failure data set of each device and the weight corresponding to the device operation data; i The fault operation value set is recorded as SDE i ={SDE （i，1） , SDE （i，2） , …, SDE （i，J）}; i∈[1,I], where I represents the total number of faulty devices; where SDE （i，j） Indicates the device DE i The jth fault operation value in the fault operation value set; j∈[1,J]; J represents the number of fault operation values ​​in the fault operation value set; S103: Calculate the fault difference of each device according to the fault operation value set of each device: CDF i =max{SSDE i max -RSSDE i ,SSDE i max -VSSDE i}; Among them, CDF i Indicates the device DE i Fault difference, max{} indicates maximum value operation, SSDE i max Represents the fault operation value set SDE i The maximum value of the fault operation value, RSSDE i Represents the fault operation value set SDE i The root mean square value of the fault operation value, VSSDE i Represents the fault operation value set SDE i The mean of the fault operation values; S104: Generate a set of equipment fault difference values ​​according to the calculation formula in S103, denoted as CDF={CDF1, CDF2, …, CDF I}; Calculate the mean of the equipment fault difference set and record it as the equipment fault feature evaluation data VCDF.

[0008] According to the above solution, the first environmental data is temperature sensor data.

[0009] According to the above solution, in S2, analyzing the abnormal correlation between the fault characteristic value and the corresponding first environment data includes the following contents: S201: Extract the fault feature evaluation data VCDF when the equipment fails, and form a set denoted as FCD={VCDF1, VCDF2, ..., VCDF N}, where N represents the total number of fault feature evaluation data in the fault feature evaluation data set; the fault feature value is calculated based on the fault feature evaluation data set analysis: ; Where FCV represents the fault characteristic value, VCDF n represents the nth fault feature evaluation data in the fault feature evaluation data set, n∈[1,N]; μ represents the mean of the fault feature evaluation data set, σ represents the standard deviation of the fault feature evaluation data set; exp[] represents an exponential function with the real number e as the base; S202: Extract the fault occurrence time when the fault feature evaluation data is greater than the fault feature value from the fault feature evaluation data set, and form a set denoted as TG={TG1, TG2, ..., TG M}; where TG m represents the mth fault moment in the fault moment set; M represents the total number of fault moments in the fault moment set, M<N; set the first environmental threshold for the first environmental value, if the first environmental value is greater than or equal to the first environmental threshold, mark the first environmental value, if the first environmental value is less than the first environmental threshold, do not process the first environmental value; generate the first environmental abnormal moment set for all the marked first environmental values ​​corresponding moments; denoted as TFE={TFE1, TFE2, ..., TFE Q}, where TFE1, TFE2, ..., TFE Q Respectively represent the moments when the 1st, 2nd, ..., Qth marked first environment values ​​are greater than or equal to the threshold; S203: Calculate the abnormal correlation between the fault time set and the first environment abnormal time set in the same monitoring period: A(TG→TFE)=num 2 [TG∩TFE]÷(M×Q); A(TG→TFE) represents the abnormal correlation between the fault time set TG and the first environmental abnormal time set TFE, num 2 [TG∩TFE] represents the square of the total number of moments in the intersection of the fault time set TG and the first environment abnormal time set TFE.

[0010] By calculating the anomaly correlation, the fault time set and the first environmental anomaly time set can be effectively closely linked, providing data support for the establishment of subsequent multiple models.

[0011] According to the above scheme, a fault characteristic fluctuation model is constructed in S2, including the following contents: S211: Set a threshold for the abnormal correlation, and obtain the corresponding fault time set and the first environment abnormal time set when the abnormal correlation is less than the threshold; extract the corresponding fault feature value and the first environment value to form a data pair when the fault time and the first environment abnormal time are the same, recorded as (TEM a , FCV a ); where TEM a Represents the first environment data of the a-th data pair, where FCV a represents the fault characteristic value of the ath data pair; S212: With the coordinate point (0,0) as the origin, the x-axis corresponding to the first environmental data and the y-axis corresponding to the fault characteristic value, a plane rectangular coordinate system is constructed, the coordinate points corresponding to the data are marked in the plane rectangular coordinate system, and the adjacent marked coordinate points in the plane rectangular coordinate system are connected in order from small to large in the x-axis coordinate value, and the function corresponding to the obtained broken line is used as the constructed fault characteristic fluctuation model.

[0012] By establishing a fault characteristic fluctuation model, the operating status and fault status of the equipment can be effectively judged through the first environmental data. The administrator can set up regular monitoring of the equipment operating data, without the need to test the equipment operating data for a long time, which can effectively reduce energy consumption and reduce users' monitoring costs.

[0013] According to the above scheme, S3 includes the following: S301: Set a data acquisition time for other environmental data, and extract other environmental data except the first environmental data before the fault time involved in the fault feature fluctuation model according to the data acquisition time; generate an environmental data association set based on the extracted first environmental data and other environmental data, recorded as EDA={(TEM b ,TTEM （c，b） )|b∈[1,B],c∈[1,C]}; where B represents the number of data groups in the environmental data association set, C represents the number of categories of other environmental data, TEM b Indicates the first environmental value of the bth data group, TTEM （c，b） Indicates other environmental values ​​of the bth data group of the cth type of other environmental data; S302: Establishing an environment association fitting model based on the environment data association set: TTEM=β c1×TEM+β c 2; where β c 1 and β c 2 represents the fitting coefficient, TEM represents the independent variable of the first environmental value, TTEM represents the dependent variable of other environmental values, and the β in the environmental association fitting model is fitted using the least squares method. c 1 and β c 2. Perform calculations and solve; S303: For the first fitting coefficient β c 1 Set the threshold, extract the first fitting coefficient greater than the threshold, and calculate the mean, which is recorded as the first fitting coefficient eigenvalue β1; extract the second fitting coefficient β corresponding to the first fitting coefficient greater than the threshold c 2, and calculate the mean, which is recorded as the second fitting coefficient eigenvalue β2; according to the first fitting coefficient eigenvalue and the second fitting coefficient eigenvalue, the environmental confidence fitting model is constructed: TTEM=β1×TEM+β2.

[0014] By further constructing an environmental confidence fitting model through a variety of environmental association fitting models, it is possible to effectively avoid the inability of a single data source to fully reflect the complex operating status of the equipment and further improve the accuracy of the system.

[0015] According to the above scheme, S4 includes the following contents: S401: collecting real-time first environment data, bringing the real-time first environment data into the fault characteristic fluctuation model, calculating the real-time fault characteristic value, setting a threshold for the real-time fault characteristic value, and determining that there is a fault risk if the real-time fault characteristic value is greater than the threshold; and determining that there is no fault risk if the real-time fault characteristic value is less than or equal to the threshold; S402: Bring the real-time first environment data corresponding to the fault risk into the environment confidence fitting model to calculate the real-time other environment values; extract the real-time environment data collected by the sensor when the fault risk exists, and calculate the mean of the real-time environment data; if the mean of the real-time environment data is greater than the real-time other environment values, the fault risk is judged to be credible, and the equipment fault is maintained; if the mean of the real-time environment data is less than or equal to the real-time other environment values, the fault risk is judged to be unreliable, and the first environment data collection sensor is calibrated.

[0016] By calculating the real-time fault characteristic value of the equipment through the first environmental data, the operating status of the equipment can be effectively judged according to the model constructed by historical data; the present invention further judges the fault risk through other environmental data, which can effectively avoid the first environmental data being interfered by the environment and causing inaccurate judgment, judge the abnormality of the first environmental sensor, and remind the administrator to calibrate, which can effectively ensure the accuracy of subsequent monitoring and further improve the comprehensiveness and reliability of the system.

[0017] Another aspect of the present application provides a device data analysis and management system using a temperature sensor, the system is applied to the above-mentioned device data analysis and management method using a temperature sensor, the system includes an environment and operation data acquisition module, a fault feature analysis module, an environment fitting model construction module and an early warning judgment calibration module; The environment and operation data acquisition module is used to collect environmental data of the equipment by installing sensors on the equipment; extract the equipment operation data when the equipment fails during historical operation, and generate equipment failure feature evaluation data after analyzing the equipment operation data; The fault feature analysis module is used to calculate the equipment fault feature value according to the feature data when the equipment fails in history; analyze the abnormal correlation between the fault feature value and the corresponding first environment data; generate an abnormal correlation combination according to the abnormal correlation, and construct a fault feature fluctuation model according to the abnormal correlation combination; The environment fitting model construction module is used to extract other environment data except the first environment data when the equipment fails in the history, form an environment data association set according to the first environment data and other environment data, establish an environment association fitting model according to the environment data association set, and construct an environment confidence fitting model through the environment association fitting models of two or more other environment data; The warning judgment calibration module is used to collect real-time first environmental data, analyze the operation status of the equipment using a characteristic fluctuation model; generate a fault risk warning based on the operation status of the equipment; and judge the actual situation of the fault risk warning based on the environmental confidence fitting model.

[0018] The environment and operation data acquisition module includes an environment data acquisition unit and a fault feature evaluation unit; The environmental data collection unit collects environmental data of the device by installing sensors on the device; The fault characteristic evaluation unit marks and extracts the equipment operation data of each equipment when a fault occurs, and generates an equipment fault data set; sets weights for each equipment operation data, and calculates the fault operation value of the equipment; forms a set of the fault operation values ​​of the equipment, generates a set of equipment fault difference values ​​according to the fault operation value sets of each equipment, calculates the mean of the set of equipment fault difference values, and records it as the fault characteristic evaluation data of the equipment; The fault feature analysis module includes an abnormal correlation calculation unit and a fault feature model construction unit; The abnormal correlation calculation unit analyzes and calculates the fault characteristic value according to the fault characteristic evaluation data set; generates a fault time set and a first environmental abnormal time set, and analyzes the abnormal correlation between the fault time set and the first environmental abnormal time set in the same monitoring period; The fault feature model construction unit is used to set a threshold for the abnormal correlation degree, obtain the corresponding fault time set and the first environment abnormal time set when the abnormal correlation degree is less than the threshold; extract the corresponding fault feature value and the first environment value to form a data pair when the fault moment and the first environment abnormal moment are the same; take the coordinate point (0,0) as the origin, the x-axis corresponds to the first environment data and the y-axis corresponds to the fault feature value, construct a plane rectangular coordinate system, mark the coordinate points corresponding to the data pair in the plane rectangular coordinate system, and connect the adjacent marked coordinate points in the plane rectangular coordinate system in the order of the x-axis coordinate value from small to large, and use the function corresponding to the obtained broken line as the constructed fault feature fluctuation model.

[0019] The environment fitting model construction module includes an environment association fitting model construction unit and an environment confidence fitting model construction unit; The environment association fitting model construction unit is used to set a data acquisition time for other environment data, and extract other environment data except the first environment data before the fault moment involved in the fault characteristic fluctuation model according to the data acquisition time; generate an environment data association set according to the extracted first environment data and other environment data, and establish an environment association fitting model based on the environment data association set; The environmental confidence fitting model construction unit is used to set a threshold for the first fitting coefficient, extract the first fitting coefficient greater than the threshold, and calculate the mean, which is recorded as the first fitting coefficient eigenvalue; extract the second fitting coefficient corresponding to the first fitting coefficient greater than the threshold, and calculate the mean, which is recorded as the second fitting coefficient eigenvalue; and construct the environmental confidence fitting model according to the first fitting coefficient eigenvalue and the second fitting coefficient eigenvalue.

[0020] The early warning judgment and calibration module includes a fault risk judgment unit and a fault risk calibration unit; The fault risk judgment unit is used to collect real-time first environment data, bring the real-time first environment data into the fault characteristic fluctuation model, calculate the real-time fault characteristic value, set a threshold value for the real-time fault characteristic value, and if the real-time fault characteristic value is greater than the threshold value, it is determined that there is a fault risk; if the real-time fault characteristic value is less than or equal to the threshold value, it is determined that there is no fault risk; The fault risk calibration unit brings the real-time first environment data corresponding to the fault risk into the environmental confidence fitting model to calculate the real-time other environment values; extracts the real-time environment data collected by the sensor when the fault risk exists, and calculates the mean of the real-time environment data; if the mean of the real-time environment data is greater than the real-time other environment values, it is judged that the fault risk is credible, and the equipment fault is maintained; if the mean of the real-time environment data is less than or equal to the real-time other environment values, it is judged that the fault risk is not credible, and the first environment data collection sensor is calibrated.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: by calculating the abnormal correlation degree, the fault time set and the first environment abnormal time set can be effectively closely linked, providing data support for the establishment of multiple subsequent models; by establishing a fault characteristic fluctuation model, the operating state and fault state of the equipment can be effectively judged through the first environment data, and the administrator can perform regular monitoring and setting of the equipment operating data, without the need to detect the equipment operating data for a long time, which can effectively reduce energy consumption and reduce the monitoring cost of users; by further constructing an environmental confidence fitting model through multiple environmental association fitting models, it can effectively avoid the single data source from being unable to fully reflect the complex operating state of the equipment, and further improve the accuracy of the system; by calculating the real-time fault characteristic value of the equipment through the first environment data, the operating state of the equipment can be effectively judged according to the model constructed by historical data; the present invention further determines the fault risk through other environmental data, which can effectively avoid the first environment data being interfered by the environment, resulting in inaccurate judgment, judge the abnormality of the first environment sensor, and remind the administrator to calibrate, which can effectively ensure the accuracy of subsequent monitoring and further improve the comprehensiveness and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A schematic diagram of a flow chart of a device data analysis and management method using a temperature sensor according to the present invention; Figure 2 The present invention is a schematic structural diagram of an equipment data analysis and management system using a temperature sensor. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 The present invention provides a technical solution: a device data analysis and management method using a temperature sensor, the method comprising the following steps: S1: Install sensors on the equipment and use them to collect environmental data. Extract equipment operation data when failures occurred during the equipment's historical operation, analyze the equipment operation data, and generate equipment failure feature evaluation data. According to the above scheme, S1 includes the following contents: S101: marking and extracting the equipment operation data when each equipment fails, and generating an equipment failure data set; the equipment operation data includes current value, voltage value, operating power and load; S102: Set weights for each device operation data, and calculate the device failure operation value according to the device failure data set of each device and the weight corresponding to the device operation data; i The fault operation value set is recorded as SDE i ={SDE （i，1） , SDE （i，2） , …, SDE （i，J）}; i∈[1,I], where I represents the total number of faulty devices; where SDE （i，j） Indicates the device DE i The jth fault operation value in the fault operation value set; j∈[1,J]; J represents the number of fault operation values ​​in the fault operation value set; S103: Calculate the fault difference of each device according to the fault operation value set of each device: CDF i =max{SSDE i max -RSSDE i ,SSDE i max -VSSDE i}; Among them, CDF i Indicates the device DE i Fault difference, max{} indicates maximum value operation, SSDE i max Represents the fault operation value set SDE i The maximum value of the fault operation value, RSSDE i Represents the fault operation value set SDE i The root mean square value of the fault operation value, VSSDE i Represents the fault operation value set SDE i The mean of the fault operation values; S104: Generate a set of equipment fault difference values ​​according to the calculation formula in S103, denoted as CDF={CDF1, CDF2, …, CDF I}; Calculate the mean of the equipment fault difference set and record it as the equipment fault feature evaluation data VCDF.

[0025] S2: Calculate the equipment fault characteristic value according to the characteristic data when the equipment fails in history; analyze the abnormal correlation between the fault characteristic value and the corresponding first environment data; generate an abnormal correlation combination according to the abnormal correlation, and construct a fault characteristic fluctuation model according to the abnormal correlation combination; According to the above solution, the first environmental data is temperature sensor data.

[0026] According to the above solution, in S2, analyzing the abnormal correlation between the fault characteristic value and the corresponding first environment data includes the following contents: S201: Extract the fault feature evaluation data VCDF when the equipment fails, and form a set denoted as FCD={VCDF1, VCDF2, ..., VCDF N}, where N represents the total number of fault feature evaluation data in the fault feature evaluation data set; the fault feature value is calculated based on the fault feature evaluation data set analysis: ; Where FCV represents the fault characteristic value, VCDF n represents the nth fault feature evaluation data in the fault feature evaluation data set, n∈[1,N]; μ represents the mean of the fault feature evaluation data set, σ represents the standard deviation of the fault feature evaluation data set; exp[] represents an exponential function with the real number e as the base; S202: Extract the fault occurrence time when the fault feature evaluation data is greater than the fault feature value from the fault feature evaluation data set, and form a set denoted as TG={TG1, TG2, ..., TG M}; where TG m represents the mth fault moment in the fault moment set; M represents the total number of fault moments in the fault moment set, M<N; set the first environmental threshold for the first environmental value, if the first environmental value is greater than or equal to the first environmental threshold, mark the first environmental value, if the first environmental value is less than the first environmental threshold, do not process the first environmental value; generate the first environmental abnormal moment set for all the marked first environmental values ​​corresponding moments; denoted as TFE={TFE1, TFE2, ..., TFE Q}, where TFE1, TFE2, ..., TFE Q Respectively represent the moments when the 1st, 2nd, ..., Qth marked first environment values ​​are greater than or equal to the threshold; S203: Calculate the abnormal correlation between the fault time set and the first environment abnormal time set in the same monitoring period: A(TG→TFE)=num 2 [TG∩TFE]÷(M×Q); A(TG→TFE) represents the abnormal correlation between the fault time set TG and the first environmental abnormal time set TFE, num 2 [TG∩TFE] represents the square of the total number of moments in the intersection of the fault time set TG and the first environment abnormal time set TFE.

[0027] Embodiment 1, in this embodiment, the set of fault time is {1S, 2S, 3S, 4S, 5S, 6S, 7S}; The first environmental abnormal time set is {2S, 3S, 4S, 5S, 6S, 7S, 8S}; Therefore, TG∩TFE is {1S, 2S, 3S, 4S, 5S, 6S, 7S, 8S}; A (TG→TFE) = 8 2 ÷(7×7)≈1.31; According to the above scheme, a fault characteristic fluctuation model is constructed in S2, including the following contents: S211: Set a threshold for the abnormal correlation, and obtain the corresponding fault time set and the first environment abnormal time set when the abnormal correlation is less than the threshold; extract the corresponding fault feature value and the first environment value to form a data pair when the fault time and the first environment abnormal time are the same, recorded as (TEM a , FCV a ); where TEM a Represents the first environment data of the a-th data pair, where FCV a represents the fault characteristic value of the ath data pair; S212: With the coordinate point (0,0) as the origin, the x-axis corresponding to the first environmental data and the y-axis corresponding to the fault characteristic value, a plane rectangular coordinate system is constructed, the coordinate points corresponding to the data are marked in the plane rectangular coordinate system, and the adjacent marked coordinate points in the plane rectangular coordinate system are connected in order from small to large in the x-axis coordinate value, and the function corresponding to the obtained broken line is used as the constructed fault characteristic fluctuation model.

[0028] S3: extracting other environmental data except the first environmental data when the device fails in the history, forming an environmental data association set according to the first environmental data and other environmental data, establishing an environmental association fitting model according to the environmental data association set, and constructing an environmental confidence fitting model through the environmental association fitting models of two or more other environmental data; According to the above scheme, S3 includes the following: S301: Set a data acquisition time for other environmental data, and extract other environmental data except the first environmental data before the fault time involved in the fault feature fluctuation model according to the data acquisition time; generate an environmental data association set based on the extracted first environmental data and other environmental data, recorded as EDA={(TEM b ,TTEM （c，b） )|b∈[1,B],c∈[1,C]}; where B represents the number of data groups in the environmental data association set, C represents the number of categories of other environmental data, TEM b Indicates the first environmental value of the bth data group, TTEM （c，b） Indicates other environmental values ​​of the bth data group of the cth type of other environmental data; S302: Establishing an environment association fitting model based on the environment data association set: TTEM=β c 1×TEM+β c 2; where β c 1 and β c 2 represents the fitting coefficient, TEM represents the independent variable of the first environmental value, TTEM represents the dependent variable of other environmental values, and the β in the environmental association fitting model is fitted using the least squares method. c 1 and β c 2. Perform calculations and solve; S303: For the first fitting coefficient β c 1 Set the threshold, extract the first fitting coefficient greater than the threshold, and calculate the mean, which is recorded as the first fitting coefficient eigenvalue β1; extract the second fitting coefficient β corresponding to the first fitting coefficient greater than the threshold c 2, and calculate the mean, which is recorded as the second fitting coefficient eigenvalue β2; according to the first fitting coefficient eigenvalue and the second fitting coefficient eigenvalue, the environmental confidence fitting model is constructed: TTEM=β1×TEM+β2.

[0029] S4: Collect real-time first environment data and use the characteristic fluctuation model to analyze the operation status of the equipment; generate a fault risk warning based on the operation status of the equipment; and determine the actual situation of the fault risk warning based on the environmental confidence fitting model.

[0030] According to the above scheme, S4 includes the following contents: S401: collecting real-time first environment data, bringing the real-time first environment data into the fault characteristic fluctuation model, calculating the real-time fault characteristic value, setting a threshold for the real-time fault characteristic value, and determining that there is a fault risk if the real-time fault characteristic value is greater than the threshold; and determining that there is no fault risk if the real-time fault characteristic value is less than or equal to the threshold; S402: Bring the real-time first environment data corresponding to the fault risk into the environment confidence fitting model to calculate the real-time other environment values; extract the real-time environment data collected by the sensor when the fault risk exists, and calculate the mean of the real-time environment data; if the mean of the real-time environment data is greater than the real-time other environment values, the fault risk is judged to be credible, and the equipment fault is maintained; if the mean of the real-time environment data is less than or equal to the real-time other environment values, the fault risk is judged to be unreliable, and the first environment data collection sensor is calibrated.

[0031] See also Figure 2 , the present invention provides a technical solution: another aspect of the present application provides a device data analysis and management system using a temperature sensor, the system is applied to the above-mentioned device data analysis and management method using a temperature sensor to implement, the system includes an environment and operation data acquisition module, a fault feature analysis module, an environment fitting model construction module and an early warning judgment calibration module; The environment and operation data acquisition module is used to collect environmental data of the equipment by installing sensors on the equipment; extract the equipment operation data when the equipment fails during historical operation, and generate equipment failure feature evaluation data after analyzing the equipment operation data; The fault feature analysis module is used to calculate the equipment fault feature value according to the feature data when the equipment fails in history; analyze the abnormal correlation between the fault feature value and the corresponding first environment data; generate an abnormal correlation combination according to the abnormal correlation, and construct a fault feature fluctuation model according to the abnormal correlation combination; The environment fitting model construction module is used to extract other environment data except the first environment data when the equipment fails in the history, form an environment data association set according to the first environment data and other environment data, establish an environment association fitting model according to the environment data association set, and construct an environment confidence fitting model through the environment association fitting models of two or more other environment data; The early warning judgment calibration module is used to collect real-time first environment data, analyze the operation status of the equipment using the characteristic fluctuation model; generate a fault risk warning based on the operation status of the equipment; and judge the actual situation of the fault risk warning based on the environmental confidence fitting model.

[0032] The environment and operation data acquisition module includes an environment data acquisition unit and a fault characteristic evaluation unit; The environmental data collection unit collects environmental data of the equipment by installing sensors on the equipment; The fault characteristic evaluation unit marks and extracts the equipment operation data of each equipment when a fault occurs, and generates an equipment fault data set; sets weights for each equipment operation data, and calculates the equipment fault operation value; forms a set of equipment fault operation values, generates an equipment fault difference value set based on the fault operation value sets of each equipment, calculates the mean of the equipment fault difference value set, and records it as the equipment fault characteristic evaluation data; The fault feature analysis module includes an abnormal correlation calculation unit and a fault feature model construction unit; The abnormal correlation calculation unit analyzes and calculates the fault characteristic value according to the fault characteristic evaluation data set; generates a fault time set and a first environmental abnormal time set, and analyzes the abnormal correlation between the fault time set and the first environmental abnormal time set within the same monitoring period; The fault feature model construction unit is used to set a threshold for the abnormal correlation degree, and obtain the corresponding fault time set and the first environment abnormal time set when the abnormal correlation degree is less than the threshold; extract the corresponding fault feature value and the first environment value to form a data pair when the fault moment and the first environment abnormal moment are the same; take the coordinate point (0,0) as the origin, the x-axis corresponds to the first environment data and the y-axis corresponds to the fault feature value, construct a plane rectangular coordinate system, mark the coordinate points corresponding to the data pair in the plane rectangular coordinate system, and connect the adjacent marked coordinate points in the plane rectangular coordinate system in the order of the x-axis coordinate value from small to large, and use the function corresponding to the obtained broken line as the constructed fault feature fluctuation model.

[0033] The environment fitting model building module includes an environment association fitting model building unit and an environment confidence fitting model building unit; The environment association fitting model construction unit is used to set a data acquisition time for other environment data, and extract other environment data except the first environment data before the fault moment involved in the fault characteristic fluctuation model according to the data acquisition time; generate an environment data association set according to the extracted first environment data and other environment data, and establish an environment association fitting model based on the environment data association set; The environmental confidence fitting model construction unit is used to set a threshold for the first fitting coefficient, extract the first fitting coefficient greater than the threshold, and calculate the mean, which is recorded as the first fitting coefficient eigenvalue; extract the second fitting coefficient corresponding to the first fitting coefficient greater than the threshold, and calculate the mean, which is recorded as the second fitting coefficient eigenvalue; construct the environmental confidence fitting model according to the first fitting coefficient eigenvalue and the second fitting coefficient eigenvalue.

[0034] The early warning judgment and calibration module includes a fault risk judgment unit and a fault risk calibration unit; The fault risk judgment unit is used to collect real-time first environment data, bring the real-time first environment data into the fault characteristic fluctuation model, calculate the real-time fault characteristic value, set a threshold value for the real-time fault characteristic value, and if the real-time fault characteristic value is greater than the threshold value, it is determined that there is a fault risk; if the real-time fault characteristic value is less than or equal to the threshold value, it is determined that there is no fault risk; The fault risk calibration unit brings the real-time first environment data corresponding to the fault risk into the environmental confidence fitting model to calculate the real-time other environment values; extracts the real-time environment data collected by the sensor when the fault risk exists, and calculates the mean of the real-time environment data; if the mean of the real-time environment data is greater than the real-time other environment values, it is judged that the fault risk is credible and the equipment fault is maintained; if the mean of the real-time environment data is less than or equal to the real-time other environment values, it is judged that the fault risk is uncredible and the first environment data collection sensor is calibrated.

[0035] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0036] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A device data analysis and management method using a temperature sensor, characterized in that: The method comprises the following steps: S1: Install sensors on the equipment and use them to collect environmental data. Extract equipment operation data when failures occurred during the equipment's historical operation, analyze the equipment operation data, and generate equipment failure feature evaluation data. S2: Calculate the equipment fault characteristic value according to the characteristic data when the equipment fails in history; analyze the abnormal correlation between the fault characteristic value and the corresponding first environment data; generate an abnormal correlation combination according to the abnormal correlation, and construct a fault characteristic fluctuation model according to the abnormal correlation combination; S3: extracting other environmental data except the first environmental data when the device fails in the history, forming an environmental data association set according to the first environmental data and other environmental data, establishing an environmental association fitting model according to the environmental data association set, and constructing an environmental confidence fitting model through the environmental association fitting models of two or more other environmental data; S4: collect real-time first environment data, and use characteristic fluctuation model to analyze the operation status of the equipment; generate fault risk warning according to the operation status of the equipment; The actual situation of fault risk warning is judged according to the environmental confidence fitting model.

2. The device data analysis and management method using a temperature sensor according to claim 1, characterized in that: In S1, the following contents are included: S101: marking and extracting the equipment operation data of each equipment when a fault occurs, and generating an equipment fault data set; the equipment operation data includes current value, voltage value, operation power and load; S102: Set weights for each device operation data, and calculate the device failure operation value according to the device failure data set of each device and the weight corresponding to the device operation data; i The fault operation value set is recorded as SDE i ={SDE （i，1） , SDE （i，2） , …, SDE （i，J） }; i∈[1,I], where I represents the total number of faulty devices; where SDE （i，j） Indicates the device DE i The jth fault operation value in the fault operation value set; j∈[1,J]; J represents the number of faulty operation values ​​in the faulty operation value set; S103: Calculate the fault difference of each device according to the fault operation value set of each device: CDF i =max{SSDE i max -RSSDE i ,SSDE i max -VSSDE i }; Among them, CDF i Indicates the device DE i Fault difference, max{} indicates maximum value operation, SSDE i max Represents the fault operation value set SDE i The maximum value of the fault operation value, RSSDE i Represents the fault operation value set SDE i The root mean square value of the fault operation value, VSSDE i Represents the fault operation value set SDE i The mean of the fault operation values; S104: Generate a set of equipment fault difference values ​​according to the calculation formula in S103, denoted as CDF={CDF1, CDF2, …, CDF I }; Calculate the mean of the equipment fault difference set and record it as the equipment fault feature evaluation data VCDF.

3. The device data analysis and management method using a temperature sensor according to claim 1, characterized in that: The first environmental data is temperature sensor data.

4. The device data analysis and management method using a temperature sensor according to claim 2, characterized in that: In S2, analyzing the abnormal correlation between the fault characteristic value and the corresponding first environment data includes the following contents: S201: Extract the fault feature evaluation data VCDF when the equipment fails, and form a set denoted as FCD={VCDF1, VCDF2, ..., VCDF N }, where N represents the total number of fault feature evaluation data in the fault feature evaluation data set; the fault feature value is calculated based on the fault feature evaluation data set analysis: ； Where FCV represents the fault characteristic value, VCDF n represents the nth fault feature evaluation data in the fault feature evaluation data set, n∈[1,N]; μ represents the mean of the fault feature evaluation data set, σ represents the standard deviation of the fault feature evaluation data set; exp[] represents an exponential function with the real number e as the base; S202: Extract the fault occurrence time when the fault feature evaluation data is greater than the fault feature value from the fault feature evaluation data set, and form a set denoted as TG={TG1, TG2, ..., TG M }; where TG m represents the mth fault moment in the fault moment set; M represents the total number of fault moments in the fault moment set, M<N; set the first environmental threshold for the first environmental value, if the first environmental value is greater than or equal to the first environmental threshold, mark the first environmental value, if the first environmental value is less than the first environmental threshold, do not process the first environmental value; generate the first environmental abnormal moment set for all the marked first environmental values ​​corresponding moments; denoted as TFE={TFE1, TFE2, ..., TFE Q }, where TFE1, TFE2, ..., TFE Q Respectively represent the moments when the 1st, 2nd, ..., Qth marked first environment values ​​are greater than or equal to the threshold; S203: Calculate the abnormal correlation between the fault time set and the first environment abnormal time set in the same monitoring period: A (TG → TFE) = number 2 [TG∩TFE]÷(M×Q); A(TG→TFE) represents the abnormal correlation between the fault time set TG and the first environmental abnormal time set TFE, num 2 [TG∩TFE] represents the square of the total number of moments in the intersection of the fault time set TG and the first environment abnormal time set TFE.

5. The device data analysis and management method using a temperature sensor according to claim 4, characterized in that: The fault characteristic fluctuation model is constructed in S2, including the following: S211: Set a threshold for the abnormal correlation, and obtain the corresponding fault time set and the first environment abnormal time set when the abnormal correlation is less than the threshold; extract the corresponding fault feature value and the first environment value to form a data pair when the fault time and the first environment abnormal time are the same, recorded as (TEM a , FCV a ); where TEM a Represents the first environmental data of the a-th data pair, where FCV a represents the fault characteristic value of the a-th data pair; S212: With the coordinate point (0,0) as the origin, the x-axis corresponding to the first environmental data and the y-axis corresponding to the fault characteristic value, a plane rectangular coordinate system is constructed, the coordinate points corresponding to the data are marked in the plane rectangular coordinate system, and the adjacent marked coordinate points in the plane rectangular coordinate system are connected in order from small to large in the x-axis coordinate value, and the function corresponding to the obtained broken line is used as the constructed fault characteristic fluctuation model.

6. The device data analysis and management method using a temperature sensor according to claim 5, characterized in that: In S3, include the following: S301: Set a data acquisition time for other environmental data, and extract other environmental data except the first environmental data before the fault time involved in the fault feature fluctuation model according to the data acquisition time; generate an environmental data association set based on the extracted first environmental data and other environmental data, recorded as EDA={(TEM b ,TTEM （c，b） )|b∈[1,B],c∈[1,C]}; where B represents the number of data groups in the environmental data association set, C represents the number of categories of other environmental data, TEM b Indicates the first environmental value of the bth data group, TTEM （c，b） Indicates other environmental values ​​of the bth data group of the cth type of other environmental data; S302: Establishing an environment association fitting model based on the environment data association set: TTEM=β c 1×TEM+β c 2; where β c 1 and β c 2 represents the fitting coefficient, TEM represents the independent variable of the first environmental value, TTEM represents the dependent variable of other environmental values, and the β in the environmental association fitting model is fitted using the least squares method. c 1 and β c 2. Perform calculations and solve; S303: For the first fitting coefficient β c 1 Set the threshold, extract the first fitting coefficient greater than the threshold, and calculate the mean, which is recorded as the first fitting coefficient eigenvalue β1; extract the second fitting coefficient β corresponding to the first fitting coefficient greater than the threshold c 2, and calculate the mean, which is recorded as the second fitting coefficient eigenvalue β2; according to the first fitting coefficient eigenvalue and the second fitting coefficient eigenvalue, the environmental confidence fitting model is constructed: TTEM=β1×TEM+β2.

7. The device data analysis and management method using a temperature sensor according to claim 6, characterized in that: In S4, the following contents are included: S401: collecting real-time first environment data, bringing the real-time first environment data into a fault characteristic fluctuation model, calculating a real-time fault characteristic value, setting a threshold for the real-time fault characteristic value, and determining that a fault risk exists if the real-time fault characteristic value is greater than the threshold; If the real-time fault characteristic value is less than or equal to the threshold, it is determined that there is no fault risk; S402: Bring the real-time first environment data corresponding to the fault risk into the environment confidence fitting model to calculate the real-time other environment values; extract the real-time environment data collected by the sensor when the fault risk exists, and calculate the mean of the real-time environment data; if the mean of the real-time environment data is greater than the real-time other environment values, it is determined that the fault risk is credible, and the equipment fault is maintained; If the mean value of the real-time environmental data is less than or equal to other real-time environmental values, it is determined that the fault risk is not credible, and the first environmental data acquisition sensor is calibrated.

8. A device data analysis and management system using a temperature sensor, the system being applied to a device data analysis and management method using a temperature sensor as claimed in any one of claims 1 to 7, characterized in that: The system includes an environment and operation data acquisition module, a fault feature analysis module, an environment fitting model construction module and an early warning judgment calibration module; The environment and operation data acquisition module is used to collect environmental data of the equipment by installing sensors on the equipment; extract the equipment operation data when the equipment fails during historical operation, and generate equipment failure feature evaluation data after analyzing the equipment operation data; The fault feature analysis module is used to calculate the equipment fault feature value according to the feature data when the equipment fails in history; analyze the abnormal correlation between the fault feature value and the corresponding first environment data; generate an abnormal correlation combination according to the abnormal correlation, and construct a fault feature fluctuation model according to the abnormal correlation combination; The environment fitting model construction module is used to extract other environment data except the first environment data when the equipment fails in the history, form an environment data association set according to the first environment data and other environment data, establish an environment association fitting model according to the environment data association set, and construct an environment confidence fitting model through the environment association fitting models of two or more other environment data; The early warning judgment calibration module is used to collect real-time first environment data, analyze the operation status of the equipment using the characteristic fluctuation model, and generate a fault risk early warning according to the operation status of the equipment; The actual situation of fault risk warning is judged according to the environmental confidence fitting model.

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