A big data-based automotive equipment data management system and method

Abstract

The present invention relates to the technical field of equipment management, and specifically to a big data-based automotive equipment data management system and method, including: an equipment usage data collection module, an equipment heat dissipation analysis module, a maintenance management model establishment module, and an automotive equipment maintenance planning module. The equipment usage data collection module collects the historical usage data and historical temperature change data of the power amplifier equipment in the vehicle. The equipment heat dissipation analysis module analyzes the historical heat dissipation status of the power amplifier equipment and counts the number of times and time information of heat dissipation anomalies of the power amplifier equipment. The maintenance management model establishment module establishes a maintenance management model for the power amplifier equipment. The automotive equipment maintenance planning module obtains the historical usage data and historical heat dissipation data of the current power amplifier equipment, substitutes the data into the maintenance management model, predicts the heat dissipation anomaly time of the current power amplifier equipment, and plans the maintenance time of the power amplifier equipment, improving the maintenance efficiency of the power amplifier equipment and reducing unnecessary maintenance work.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and specifically to a big data-based vehicle equipment data management system and method. Background Technique

[0002] The power amplifier equipment in the vehicle has the function of amplifying the weak electrical signals emitted by the signal source to drive the speaker to emit sound. For example, the DSP power amplifier is a power amplifier equipment that optimizes and manages audio parameters through digital signal processing algorithms. It can attenuate the overlapping frequencies caused by the vehicle interior environment and add the frequencies attenuated by the environment, which can effectively improve the sound playback effect in the vehicle;

[0003] After the power amplifier equipment is used for a long time, some dust and dirt are likely to accumulate inside. These things will block the heat dissipation holes and heat sinks between components, resulting in the inability to dissipate heat normally. Therefore, it is often necessary to perform maintenance and cleaning on the inside of the power amplifier equipment to ensure the normal heat dissipation of the power amplifier equipment. However, maintenance work is only necessary when the equipment has abnormal heat dissipation. However, the time when it is necessary to clean the power amplifier equipment may be affected by various factors such as equipment usage data and change. Performing maintenance on the power amplifier equipment at the appropriate time can not only improve the maintenance efficiency but also reduce unnecessary maintenance and cleaning work. In the prior art, the maintenance management time of the power amplifier equipment is not adaptively planned, and the maintenance efficiency cannot be effectively improved.

[0004] Therefore, people need a big data-based vehicle equipment data management system and method to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a big data-based vehicle equipment data management system and method to solve the problems raised in the above background technique.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: A big data-based data management system for automotive equipment, the system comprising: a device usage data acquisition module, a device heat dissipation analysis module, a maintenance management model establishment module, and an automotive equipment maintenance planning module. The device usage data acquisition module is used to collect the historical usage data and historical temperature change data of the power amplifier device in the vehicle. The power amplifier device refers to a DSP power amplifier. The device heat dissipation analysis module is used to analyze the historical heat dissipation status of the power amplifier device based on the historical temperature change data of the power amplifier device and count the number of times and time information of heat dissipation anomalies of the power amplifier device. The maintenance management model establishment module is used to establish a maintenance management model of the power amplifier device based on the statistical data and the historical usage data of the power amplifier device. The automotive equipment maintenance planning module is used to obtain the historical usage data and historical heat dissipation data of the current power amplifier device, substitute the obtained data into the maintenance management model, predict the heat dissipation anomaly time of the current power amplifier device, and plan the maintenance time of the power amplifier device.

[0007] Further, the device usage data acquisition module includes a usage time acquisition unit and a heat dissipation data acquisition unit. The usage time acquisition unit is used to collect the number of times the power amplifier device in the vehicle has been used in the past and the duration data of each use. The heat dissipation data acquisition unit is used to collect the temperature data of the power amplifier device each time it was used in the past by using a temperature sensor.

[0008] Further, the device heat dissipation analysis module includes a temperature change index analysis unit and a heat dissipation anomaly data acquisition unit. The temperature change index analysis unit is used to analyze the temperature change index of the power amplifier device based on the collected temperature data. The heat dissipation anomaly data acquisition unit is used to set a change index threshold, compare the temperature change index with the threshold. If the temperature change index exceeds the threshold, it is determined that the power amplifier device has a heat dissipation anomaly, and the number of times of heat dissipation anomalies of the power amplifier device in the past and the interval time of heat dissipation anomalies in the past are counted. The interval time of heat dissipation anomalies in the past includes the interval time from the time when the first heat dissipation anomaly occurred to the time when the power amplifier device started to be used, and the interval time from the time when each subsequent heat dissipation anomaly occurred to the time when the previous heat dissipation anomaly occurred. The number of times the power amplifier device has been used and the duration of each use when the heat dissipation anomaly occurred in the past are retrieved from the usage time acquisition unit.

[0009] Further, the maintenance management model establishment module includes a data combination unit, a combined data fitting unit, and a maintenance management model establishment unit. The data combination unit is used to analyze the usage frequency of the power amplifier device, and combine the number of times of abnormal heat dissipation, the usage frequency, and the interval time of abnormal heat dissipation of the power amplifier device in the past. The combined data fitting unit is used to perform fitting processing on the combined data. The maintenance management model establishment unit is used to establish a maintenance management model for the power amplifier device after the fitting processing.

[0010] Further, the vehicle equipment maintenance planning module includes a current equipment data input unit, a maintenance time planning unit, an equipment maintenance reminder unit, and a maintenance time update unit. The current equipment data input unit is used to obtain the number of times of abnormal heat dissipation of the current power amplifier device in the past and the usage frequency of the current power amplifier device in the past, and input the number data and the usage frequency data into the maintenance management model to predict the time when the current power amplifier device will have abnormal heat dissipation next time. The maintenance time planning unit is used to plan the time when the current power amplifier device will have abnormal heat dissipation next time predicted as the time when the current power amplifier device needs to be maintained next time. The equipment maintenance reminder unit is used to remind the user to perform maintenance on the current power amplifier device at the predicted time. The maintenance work can be to clean the dust around the power amplifier device, etc. The accumulation of dust on the power amplifier is likely to cause abnormal heat dissipation. Therefore, cleaning the dust can alleviate the abnormal heat dissipation phenomenon to a certain extent.

[0011] A method for managing vehicle equipment data based on big data includes the following steps:

[0012] S100: Collect the usage historical data and historical temperature change data of the power amplifier device in the vehicle;

[0013] S101: Analyze the historical heat dissipation condition of the power amplifier device, and count the number of times and time information of abnormal heat dissipation of the power amplifier device in the past;

[0014] S102: Establish a maintenance management model for the power amplifier device based on the statistical data and the usage historical data of the power amplifier device;

[0015] S103: Predict the abnormal heat dissipation time of the current power amplifier device and plan the maintenance time of the current power amplifier device.

[0016] Further, in S100: Collect the number of times the power amplifier device in the vehicle has been used in the past and the duration data of each use. Use a temperature sensor to collect the temperature data each time the power amplifier device was used in the past. Combine the time when the temperature changes and the temperature data of the power amplifier device collected at the corresponding time to form a data set. Obtain a data set collected during a random use of a random power amplifier device in the past as {(A1, H1), (A2, H2),...(A i , H i ),...(A m , H m )}, where A i represents the time when the temperature changes for the i-th time, and H i represents the temperature of the corresponding power amplifier device collected when the temperature changes for the i-th time. Collect the set of durations during which each temperature in the set {H1, H2,...H i ,...H m} remains unchanged as t = {t1, t2,...t i ,...t m}, where t i represents the duration during which the temperature H i of the corresponding power amplifier device remains unchanged, and m represents the number of times the temperature changes during a random use of a random power amplifier device in the past.

[0017] Further, in S101: According to Calculate the temperature change index W i during a random temperature change during a random use of a random power amplifier device in the past. Set the temperature change index threshold as F. Calculate the temperature change index for each temperature change during a random use of the corresponding power amplifier device in the past. If the temperature change index for any one of the m temperature changes is greater than F, it is determined that the corresponding power amplifier device has a heat dissipation abnormality during the corresponding use. When the power amplifier device has a heat dissipation abnormality, there will be a rapid temperature change, that is, a phenomenon of rapid temperature rise and long high-temperature duration. By collecting the temperature change data of the power amplifier device during use and combining the temperature rise speed and the high-temperature duration, the temperature change index of the power amplifier device is analyzed. The faster the temperature rises, the higher the temperature after the change, and the longer the duration, the higher the temperature change index. Set the temperature change index threshold, and judge the heat dissipation abnormality of the power amplifier device through the temperature change index and the threshold, which improves the accuracy of the heat dissipation condition analysis result. The set of the number of times of heat dissipation abnormality of different power amplifier devices of the same type in the past is counted as K = {K1, K2,...K f}, f represents the number of power amplifier devices, and the set of the intervals between heat dissipation abnormalities of a random power amplifier device in the past is counted as T = {T1, T2,...T n}, the average value of the interval times within T is obtained to get the average interval time of a randomly selected power amplifier device having had abnormal heat dissipation in the past. The interval times of f power amplifier devices having had abnormal heat dissipation in the past are statistically analyzed, and the set of average interval times of f power amplifier devices having had abnormal heat dissipation in the past is D = {D1,

[0018] D2,... D f}, n represents the number of times a randomly selected power amplifier device has had abnormal heat dissipation in the past, n ∈ K, T1 represents the interval time from the time when the corresponding power amplifier device first had abnormal heat dissipation to the time when the corresponding power amplifier device started to be used, T2 represents the interval time from the second time the corresponding power amplifier device had abnormal heat dissipation to the first time it had abnormal heat dissipation. The number of times of use of the corresponding power amplifier device in the period from the start of being used to the first time it had abnormal heat dissipation in the past is retrieved as e, and the set of durations of each use in the corresponding period is U = {U1, U2,... U j ,... U e}.

[0019] Furthermore, in S102: According to the formula calculate the usage frequency P1 of the corresponding power amplifier device in the period from the start of being used to the first time it had abnormal heat dissipation in the past. By the same calculation method, the set of usage frequencies of the corresponding power amplifier device is P = {P1, P2,... P n}, P n represents the usage frequency of the corresponding power amplifier device in the period from the (n - 1)-th time it had abnormal heat dissipation to the n-th time it had abnormal heat dissipation, U j represents the duration of the j-th use of the corresponding power amplifier device in the period from the start of being used to the first time it had abnormal heat dissipation in the past. According to the formula calculate the comprehensive usage frequency Q r of the corresponding power amplifier device, P v represents the usage frequency of the corresponding power amplifier device in the period from the (v - 1)-th time it had abnormal heat dissipation to the v-th time it had abnormal heat dissipation. By the same calculation method, the set of comprehensive usage frequencies of f power amplifier devices is Q = {Q1, Q2,... Q f}. Combine the data in the sets K, Q, and D to obtain a data set as {(K1, Q1, D1), (K2, Q2, D2),... (K f , Q f , D f )}. Fit the combined data and establish a maintenance management model:

[0020] z = σ1 * x + σ2 * y + σ0;

[0021] Among them, σ1, σ2, and σ0 represent fitting coefficients, x represents the first independent variable in the maintenance management model that refers to the number of times of abnormal heat dissipation, y represents the second independent variable in the maintenance management model that refers to the comprehensive usage frequency, and z represents the dependent variable in the maintenance management model that refers to the abnormal heat dissipation time;

[0022] Considering that the time of abnormal heat dissipation of the power amplifier device is affected by the usage situation of the device, the more frequently the device is used and the longer the duration of each use, the more likely the device is to have abnormal heat dissipation. The present invention collects the historical data of different power amplifier devices of the same type being used and the historical data of abnormal heat dissipation through big data technology, analyzes the comprehensive usage frequency of the devices, trains the historical data of abnormal heat dissipation and the analysis data, and establishes a maintenance management model for the power amplifier device. Data that is likely to affect the heat dissipation of the device and is likely to cause changes in the abnormal heat dissipation time due to being affected is selected as training data from multiple levels and dimensions, and then a model is established. The established model is used for the maintenance time planning of the power amplifier device, which can effectively improve the planning utility. Specifically, the historical data of power amplifier devices of the same type is selected as training data, effectively excluding the interference factors of abnormal heat dissipation caused by the performance problems of the power amplifier devices themselves, and improving the accuracy of the data analysis results.

[0023] Further, in S103: Obtain that the number of times of previous abnormal heat dissipation of the current power amplifier device is L, and the previous comprehensive usage frequency of the current power amplifier device is J. Let x = L, y = J, and the previous comprehensive usage frequency of the current power amplifier device is the same as that of Q r The calculation method is the same. The predicted time interval from the current time to the next abnormal heat dissipation of the current power amplifier device since the last abnormal heat dissipation is: σ1 * L + σ2 * J + σ0. Take the predicted time of the next abnormal heat dissipation of the current power amplifier device as the time for the next maintenance of the current power amplifier device: When the time interval from the current time is σ1 * L + σ2 * J + σ0 since the last abnormal heat dissipation, prompt the user to perform maintenance on the current power amplifier device;

[0024] By inputting the historical data of previous abnormal heat dissipation and the usage history data of the current power amplifier device into the maintenance management model, referring to the usage habits of the current power amplifier device to predict the time of the next abnormal heat dissipation of the current power amplifier device, and then making an adaptive planning adjustment to the maintenance time of the power amplifier device. Compared with the existing technology where the maintenance time is uncertain and there is no planning method, performing maintenance on the power amplifier device at an appropriate time can not only improve the maintenance efficiency but also reduce unnecessary maintenance cleaning work.

[0025] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0026] In consideration that the time when the power amplifier device has abnormal heat dissipation is affected by the usage of the device, the more frequently the device is used and the longer the duration of each use, the more likely the device is to have abnormal heat dissipation. By collecting the historical data of different power amplifier devices of the same type being used and the historical data of abnormal heat dissipation conditions through big data technology, analyzing the comprehensive usage frequency of the devices, training the historical data of abnormal heat dissipation conditions and the analysis data, a maintenance management model for power amplifier devices is established. Data that is likely to affect the device's heat dissipation and cause changes in the time of abnormal heat dissipation due to influence is selected as training data from multiple levels and dimensions, and then a model is established. The established model is used for the maintenance time planning of power amplifier devices, which can effectively improve the planning effectiveness. Specifically, the historical data of power amplifier devices of the same type is selected as training data, effectively excluding the interference factors of abnormal heat dissipation caused by the performance problems of the power amplifier devices themselves, and improving the accuracy of the data analysis results;

[0027] By inputting the historical data of abnormal heat dissipation and the historical data of being used of the current power amplifier device into the maintenance management model, predicting the time when the current power amplifier device will have abnormal heat dissipation next time with reference to the usage habits of the current power amplifier device, and then making an adaptive planning adjustment to the maintenance time of the power amplifier device. Compared with the existing technology where the maintenance time is uncertain and there is no planning method, maintaining the power amplifier device at an appropriate planned time can not only improve the maintenance efficiency but also reduce unnecessary maintenance cleaning work. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The 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 to the present invention. In the drawings:

[0029] Figure 1 is a structural diagram of a vehicle equipment data management system based on big data according to the present invention;

[0030] Figure 2 is a flowchart of a vehicle equipment data management method based on big data according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0032] The following combines Figure 1 - Figure 2 and specific embodiments to further illustrate the present invention.

[0033] Embodiment 1:

[0034] As Figure 1As shown in the figure, this embodiment provides a big data-based automotive equipment data management system, which includes: an equipment usage data collection module, an equipment heat dissipation analysis module, a maintenance management model establishment module, and an automotive equipment maintenance planning module. The output end of the equipment usage data collection module is connected to the input ends of the equipment heat dissipation analysis module and the maintenance management model establishment module. The output end of the equipment heat dissipation analysis module is connected to the input end of the maintenance management model establishment module. The output end of the maintenance management model establishment module is connected to the input end of the automotive equipment maintenance planning module. The equipment usage data collection module is used to collect the historical usage data and historical temperature change data of the power amplifier equipment in the vehicle. The equipment heat dissipation analysis module is used to analyze the historical heat dissipation status of the power amplifier equipment based on the historical temperature change data of the power amplifier equipment, and count the number of times and time information of heat dissipation anomalies of the power amplifier equipment. The maintenance management model establishment module is used to establish a maintenance management model of the power amplifier equipment based on the statistical data and the historical usage data of the power amplifier equipment. The automotive equipment maintenance planning module is used to obtain the historical usage data and historical heat dissipation data of the current power amplifier equipment, substitute the obtained data into the maintenance management model, predict the heat dissipation anomaly time of the current power amplifier equipment, and plan the maintenance time of the power amplifier equipment.

[0035] The equipment usage data collection module includes a usage time collection unit and a heat dissipation data collection unit. The usage time collection unit is used to collect the number of times the power amplifier equipment in the vehicle has been used in the past and the duration data of each use. The heat dissipation data collection unit is used to collect the temperature data of the power amplifier equipment each time it was used in the past by using a temperature sensor.

[0036] The equipment heat dissipation analysis module includes a temperature change index analysis unit and a heat dissipation anomaly data acquisition unit. The input end of the temperature change index analysis unit is connected to the output end of the heat dissipation data collection unit, and the output end of the temperature change index analysis unit is connected to the input end of the heat dissipation anomaly data acquisition unit. The temperature change index analysis unit is used to analyze the temperature change index of the power amplifier equipment based on the collected temperature data. The heat dissipation anomaly data acquisition unit is used to set a change index threshold, compare the temperature change index with the threshold. If the temperature change index exceeds the threshold, it is determined that the power amplifier equipment has a heat dissipation anomaly, and the number of times of heat dissipation anomalies of the power amplifier equipment in the past and the interval time of heat dissipation anomalies in the past are counted. The interval time of heat dissipation anomalies in the past includes the interval time from the time when the first heat dissipation anomaly occurred to the time when the power amplifier equipment started to be used, and the interval time from the time when each subsequent heat dissipation anomaly occurred to the previous heat dissipation anomaly. The number of times the power amplifier equipment has been used and the duration of each use when the heat dissipation anomaly occurred in the past are retrieved from the usage time collection unit.

[0037] The maintenance management model establishment module includes a data combination unit, a combined data fitting unit, and a maintenance management model establishment unit. The input end of the data combination unit is connected to the output ends of the heat dissipation anomaly data acquisition unit and the usage time acquisition unit. The output end of the data combination unit is connected to the input end of the combined data fitting unit. The output end of the combined data fitting unit is connected to the input end of the maintenance management model establishment unit. The data combination unit is used to analyze the usage frequency of the power amplifier device, and combine the number of times of heat dissipation anomalies that have occurred in the past for the power amplifier device, the usage frequency, and the interval time between heat dissipation anomalies that have occurred in the past for the power amplifier device. The combined data fitting unit is used to perform fitting processing on the combined data. The maintenance management model establishment unit is used to establish a maintenance management model for the power amplifier device after the fitting processing.

[0038] The vehicle equipment maintenance planning module includes a current equipment data input unit, a maintenance time planning unit, an equipment maintenance reminder unit, and a maintenance time update unit. The input end of the current equipment data input unit is connected to the output end of the maintenance management model establishment unit. The output end of the current equipment data input unit is connected to the input end of the maintenance time planning unit. The output end of the maintenance time planning unit is connected to the input end of the equipment maintenance reminder unit. The current equipment data input unit is used to obtain the number of times of heat dissipation anomalies that have occurred in the past for the current power amplifier device and the usage frequency of the current power amplifier device in the past, and input the number data and the usage frequency data into the maintenance management model to predict the time when the current power amplifier device will have a heat dissipation anomaly next time. The maintenance time planning unit is used to plan the time when the current power amplifier device will have a heat dissipation anomaly next time, which is predicted, as the time when the current power amplifier device needs to be maintained next time. The equipment maintenance reminder unit is used to remind the user to perform maintenance processing on the current power amplifier device at the predicted time. The maintenance processing work can be to clean the dust around the power amplifier device, etc. The accumulation of dust on the power amplifier is likely to cause heat dissipation anomalies. Therefore, cleaning the dust can alleviate the heat dissipation anomaly phenomenon to a certain extent.

[0039] Embodiment 2:

[0040] As Figure 2 shown, this embodiment provides a big data-based vehicle equipment data management method, which is implemented based on the data management system in the embodiment, and specifically includes the following steps:

[0041] S100: Collect the usage history data and historical temperature change data of the power amplifier device in the vehicle, collect the number of times the power amplifier device in the vehicle has been used in the past and the duration data of each use, use a temperature sensor to collect the temperature data of the power amplifier device each time it was used in the past, and form a data set with the temperature change time and the temperature data of the power amplifier device collected at the corresponding time. Obtain the data set collected during a random use of a random power amplifier device in the past, which is {(A1, H1), (A2, H2),...(Ai , H i ),...(A m , H m )}, where A i represents the time when the temperature changes for the i-th time, and H i represents the temperature of the corresponding power amplifier device collected when the temperature changes for the i-th time. The set of durations during which each temperature in the collected set {H1,

[0042] H2,...H i ,...H m} remains unchanged is t = {t1, t2,...t i ,...t m}, where t i represents the duration during which the temperature H i of the corresponding power amplifier device remains unchanged;

[0043] S101: Analyze the historical heat dissipation status of the power amplifier device, count the number and time information of previous heat dissipation anomalies of the power amplifier device. According to , calculate the temperature change index W i when the temperature changes randomly during a random use process of a previous power amplifier device. Set the temperature change index threshold as F, and calculate the temperature change index when the temperature changes each time during a random use process of the corresponding power amplifier device. If the temperature change index during any one of the m times is greater than F, it is determined that the corresponding power amplifier device has a heat dissipation anomaly during the corresponding use process. The set of the number of times of heat dissipation anomalies of different power amplifier devices of the same type in the past is K = {K1, K2,...K f}, f represents the number of power amplifier devices. The set of the time intervals between previous heat dissipation anomalies of a random power amplifier device is T = {T1, T2,...T n}. Calculate the average value of the time intervals in T to obtain the average time interval between previous heat dissipation anomalies of a random power amplifier device. Count the time intervals between previous heat dissipation anomalies of f power amplifier devices to obtain the set of the average time intervals between previous heat dissipation anomalies of f power amplifier devices as D = {D1, D2,...D f}, n represents the number of times a random power amplifier device has had a heat dissipation anomaly in the past, n ∈ K, T1 represents the time interval between the start time of the corresponding power amplifier device's first heat dissipation anomaly and the start time of its use, T2 represents the time interval between the second heat dissipation anomaly of the corresponding power amplifier device and the first heat dissipation anomaly. Retrieve the number of times the corresponding power amplifier device has been used during the period from the start of its use to the first heat dissipation anomaly as e, and the set of the duration of each use during the corresponding period is U = {U1, U2,...U j ,...Ue};

[0044] S102: Establish a maintenance management model for the power amplifier device based on statistical data and the usage history data of the power amplifier device. According to the formula calculate the usage frequency P1 of the corresponding power amplifier device during the period from the start of its use to the first occurrence of heat dissipation abnormality. Calculate the set of usage frequencies of the corresponding power amplifier device as P = {P1,

[0045] P2,...P n}, where P n represents the usage frequency of the corresponding power amplifier device during the period from the (n - 1)-th occurrence of heat dissipation abnormality to the n-th occurrence of heat dissipation abnormality. According to the formula calculate the comprehensive usage frequency Q of the corresponding power amplifier device r . Calculate the set of comprehensive usage frequencies of f power amplifier devices as Q = {Q1, Q2,...Q f}. Combine the data in sets K, Q, and D to obtain a data set as {(K1, Q1, D1), (K2, Q2, D2),...(K f , Q f , D f )}. Fit the combined data to establish a maintenance management model: z = σ1 * x + σ2 * y + σ0, where σ1, σ2, and σ0 represent fitting coefficients, x represents the first independent variable in the maintenance management model that represents the number of occurrences of heat dissipation abnormality, y represents the second independent variable in the maintenance management model that represents the comprehensive usage frequency, and z represents the dependent variable in the maintenance management model that represents the heat dissipation abnormality time;

[0046] S103: Predict the heat dissipation abnormality time of the current power amplifier device and plan the maintenance time of the current power amplifier device. Obtain the number of times the current power amplifier device has had heat dissipation abnormalities in the past as L, and the past comprehensive usage frequency of the current power amplifier device as J. Let x = L and y = J. Predict that the time interval from the current time to the next occurrence of heat dissipation abnormality of the current power amplifier device since the most recent occurrence of heat dissipation abnormality is: σ1 * L + σ2 * J + σ0. Use the predicted time of the next occurrence of heat dissipation abnormality of the current power amplifier device as the time for the next maintenance of the current power amplifier device: Prompt the user to perform maintenance on the current power amplifier device at the time interval σ1 * L + σ2 * J + σ0 since the most recent occurrence of heat dissipation abnormality before the current time;

[0047] For example: establish a maintenance management model: z = 0.2*x + 0.01*y + 1. Obtain that the number of times of abnormal heat dissipation occurred in the current power amplifier device in the past is L = 3, and the comprehensive usage frequency of the current power amplifier device in the past is J = 8. Let x = L = 3 and y = J = 8. The time interval from the current time to the next occurrence of abnormal heat dissipation in the current power amplifier device since the most recent occurrence of abnormal heat dissipation is predicted to be: σ1*L + σ2*J + σ0 = 1.68. When the time interval is 1.68 months since the most recent occurrence of abnormal heat dissipation before the current time, prompt the user to perform maintenance on the current power amplifier device.

[0048] Finally, it should be noted that the above are only preferred examples of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for managing automotive device data based on big data, characterized in that: It includes the following steps: S100: Collect the historical usage data and historical temperature change data of the power amplifier device in the vehicle; S101: Analyze the historical heat dissipation status of the power amplifier device, and count the number of times and time information of abnormal heat dissipation of the power amplifier device in the past; S102: Establish a maintenance management model of the power amplifier device based on the statistical data and the historical usage data of the power amplifier device; S103: Predict the abnormal heat dissipation time of the current power amplifier device and plan the maintenance time of the current power amplifier device; In S100: Collect the data on the number of times the in-vehicle power amplifier device has been used in the past and the duration of each use. Use a temperature sensor to collect the temperature data each time the power amplifier device was used in the past. Combine the time when the temperature changes and the temperature data of the power amplifier device collected at the corresponding time to form a data set. Obtain a data set collected during a random use of a random power amplifier device in the past, which is {(A1, H1), (A2, H2),...(A i , H i ),...(A m , H m )}, where A i represents the time when the temperature changes for the i-th time, and H i represents the temperature of the corresponding power amplifier device collected when the temperature changes for the i-th time. Collect the set of durations during which each temperature in the set {H1, H2,...H i ,...H m} remains unchanged, which is t = {t1, t2,...t i ,...t m}, where t i represents the duration during which the temperature H i of the corresponding power amplifier device remains unchanged; In S101: According to calculate the temperature change index W during a random temperature change in the process of a random power amplifier device being used in the past. i , set the temperature change index threshold as F, calculate the temperature change index during each temperature change in the process of a corresponding power amplifier device being used randomly once in the past. If the temperature change index during any one of the m temperature changes is greater than F, it is determined that the corresponding power amplifier device has a heat dissipation abnormality during the corresponding use. The set of the number of times of heat dissipation abnormalities that occurred in different power amplifier devices of the same type in the past is K = {K1, K2,... K f}, f represents the number of power amplifier devices, the set of the intervals between heat dissipation abnormalities that occurred in a random power amplifier device in the past is T = {T1, T2,... T n}, calculate the average interval between heat dissipation abnormalities that occurred in a random power amplifier device in the past by taking the average of the intervals in T. Statistically analyze the intervals between heat dissipation abnormalities that occurred in f power amplifier devices in the past to obtain the set of the average intervals between heat dissipation abnormalities that occurred in f power amplifier devices in the past as D = {D1, D2,... D f}, n represents the number of times of heat dissipation abnormalities that occurred in a random power amplifier device in the past, n ∈ K, T1 represents the interval between the time when the corresponding power amplifier device first had a heat dissipation abnormality and the time when the corresponding power amplifier device started to be used, T2 represents the interval between the time when the corresponding power amplifier device had the second heat dissipation abnormality and the first heat dissipation abnormality. Retrieve the number of times the corresponding power amplifier device was used during the period from the start of use to the first heat dissipation abnormality in the past as e, and the set of the durations of each use during the corresponding period is U = {U1, U2,... U j ,... U e}; In S102: According to the formula Calculate the usage frequency P1 of the corresponding power amplifier device during the period from the start of its use to the first occurrence of abnormal heat dissipation. The set of usage frequencies of the corresponding power amplifier device is calculated by the same calculation method as P = {P1, P2,... P n}, where P n represents the usage frequency of the corresponding power amplifier device during the period from the (n - 1)-th occurrence of abnormal heat dissipation to the n-th occurrence of abnormal heat dissipation. According to the formula Calculate the comprehensive usage frequency Q of the corresponding power amplifier device r , and the set of comprehensive usage frequencies of f power amplifier devices is calculated by the same calculation method as Q = {Q1, Q2,... Q f}. Combine the data in the sets K, Q, and D to obtain a data set of {(K1, Q1, D1), (K2, Q2, D2),... (K f , Q f , D f ). Fit the combined data and establish a maintenance management model: z = σ1 * x + σ2 * y + σ0; Wherein, σ1, σ2 and σ0 represent fitting coefficients, x represents the first independent variable referring to the number of times of abnormal heat dissipation in the maintenance management model, y represents the second independent variable referring to the comprehensive usage frequency in the maintenance management model, and z represents the dependent variable referring to the abnormal heat dissipation time in the maintenance management model; In S103: Obtain that the number of times of abnormal heat dissipation of the current power amplifier device in the past is L, and the comprehensive usage frequency of the current power amplifier device in the past is J. Let x = L and y = J. The time interval from the current time to the next abnormal heat dissipation of the current power amplifier device since the most recent abnormal heat dissipation is predicted as: σ1 * L + σ2 * J + σ0. Take the predicted time of the next abnormal heat dissipation of the current power amplifier device as the time for the next maintenance of the current power amplifier device: Prompt the user to perform maintenance on the current power amplifier device at the time interval σ1 * L + σ2 * J + σ0 since the most recent abnormal heat dissipation before the current time.

2. A vehicle equipment data management system based on big data, which is applied to a vehicle equipment data management method based on big data as described in claim 1, and is characterized in that: The system includes: a device usage data collection module, a device heat dissipation analysis module, a maintenance management model establishment module, and a vehicle device maintenance planning module; The device usage data collection module is used to collect the historical usage data and historical temperature change data of the power amplifier device in the vehicle; The device heat dissipation analysis module is used to analyze the historical heat dissipation status of the power amplifier device based on the historical temperature change data of the power amplifier device and count the number of times and time information of abnormal heat dissipation of the power amplifier device; The maintenance management model establishment module is used to establish a maintenance management model of the power amplifier device based on the statistical data and the historical usage data of the power amplifier device; The vehicle device maintenance planning module is used to obtain the historical usage data and historical heat dissipation data of the current power amplifier device, substitute the obtained data into the maintenance management model, predict the abnormal heat dissipation time of the current power amplifier device and plan the maintenance time of the power amplifier device.

3. The data management system for automotive equipment based on big data according to claim 2, wherein: The device usage data collection module includes a usage time collection unit and a heat dissipation data collection unit; The usage time collection unit is used to collect the number of times the power amplifier device in the vehicle has been used in the past and the duration data of each use; The heat dissipation data collection unit is used to collect the temperature data of the power amplifier device each time it was used in the past by using a temperature sensor.

4. The data management system for vehicle equipment based on big data according to claim 3, characterized in that: The device heat dissipation analysis module includes a temperature change index analysis unit and an abnormal heat dissipation data acquisition unit; The temperature change index analysis unit is used to analyze the temperature change index of the power amplifier device based on the collected temperature data; The abnormal heat dissipation data acquisition unit is used to set a change index threshold, compare the temperature change index with the threshold. If the temperature change index exceeds the threshold, it is determined that the power amplifier device has abnormal heat dissipation, and the number of times the power amplifier device has had abnormal heat dissipation in the past and the interval time of abnormal heat dissipation in the past are counted. The number of times the power amplifier device has been used and the duration of each use when the power amplifier device had abnormal heat dissipation in the past are retrieved from the usage time acquisition unit.

5. The data management system for automotive equipment based on big data according to claim 4, characterized in that: The maintenance management model establishment module includes a data combination unit, a combined data fitting unit, and a maintenance management model establishment unit; The data combination unit is used to analyze the usage frequency of the power amplifier device, and combine the number of times the power amplifier device has had abnormal heat dissipation in the past, the usage frequency, and the interval time of abnormal heat dissipation of the power amplifier device in the past; The combined data fitting unit is used to perform fitting processing on the combined data; The maintenance management model establishment unit is used to establish a maintenance management model of the power amplifier device after the fitting processing.

6. The data management system for vehicle equipment based on big data according to claim 5, characterized in that: The vehicle equipment maintenance planning module includes a current equipment data input unit, a maintenance time planning unit, an equipment maintenance reminder unit, and a maintenance time update unit; The current equipment data input unit is used to obtain the number of times the current power amplifier device has had abnormal heat dissipation in the past and the past usage frequency of the current power amplifier device, and input the number data and the usage frequency data into the maintenance management model to predict the time when the current power amplifier device will have abnormal heat dissipation next time; The maintenance time planning unit is used to plan the time when the current power amplifier device will have abnormal heat dissipation next time predicted as the time when the current power amplifier device needs to be maintained next time; The equipment maintenance reminder unit is used to remind the user to perform maintenance processing on the current power amplifier device at the predicted time.

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