Multimedia equipment health management system and method based on big data

Abstract

The invention relates to the technical field of multimedia equipment management, in particular to a multimedia equipment health management system and method based on big data. The method comprises the following steps: acquiring multimedia equipment data in the running process of multimedia equipment, and calculating to obtain the running reliability and data monitoring reliability of the multimedia equipment in a management time period based on the equipment running data, based on the operation reliability of the multimedia equipment and the data monitoring reliability, the operation reliability index of the multimedia equipment in the management time period is calculated; performing clustering analysis processing on the equipment state data to obtain a clustering result, and performing analysis processing on the clustering result to obtain an equipment fault result; and a multimedia equipment early warning signal is generated based on the multimedia equipment operation reliability index and the equipment fault result, so that the accuracy of predicting and diagnosing the health state of the multimedia equipment can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multimedia device management, and in particular to a multimedia device health management system and method based on big data. Background Art

[0002] With the rapid development of information technology, multimedia devices have been widely used in various fields, such as education, entertainment, and medical treatment. However, the health status of these devices is directly related to their performance and lifespan, which in turn affects user experience and business efficiency. Therefore, it is particularly important to develop a multimedia device health management system based on big data. The system can monitor the status of the device in real time, predict potential failures, and provide corresponding maintenance suggestions to ensure the stable operation of the device.

[0003] Traditional multimedia device health management systems often rely on limited data sources, such as the device's own sensor data, to analyze the health status of multimedia devices, resulting in inaccurate prediction and diagnosis of the device's health status.

[0004] Therefore, there is an urgent need for a multimedia device health management system and method based on big data to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a multimedia device health management system and method based on big data: to solve the technical problem that existing solutions often rely on limited data sources to analyze the health status of multimedia devices, resulting in inaccurate prediction and diagnosis of the health status of the device by the system.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] On the one hand, a multimedia equipment health management system based on big data, the system includes a multimedia equipment data acquisition module, an equipment operation data analysis module, an equipment fault analysis module and an early warning module;

[0008] A multimedia device data acquisition module, used to acquire multimedia device data during the operation of the multimedia device, wherein the multimedia device data includes device operation data and device status data;

[0009] The device operation data analysis module is used to calculate the multimedia device operation reliability and data monitoring reliability within the management period based on the device operation data, and calculate the multimedia device operation reliability index within the management period based on the multimedia device operation reliability and data monitoring reliability;

[0010] The equipment failure analysis module is used to perform cluster analysis on the equipment status data to obtain cluster results, and to analyze and process the cluster results to obtain equipment failure results;

[0011] The early warning module is used to generate a multimedia device early warning signal based on the multimedia device operation reliability index and the device failure result.

[0012] Furthermore, calculating the operation reliability of the multimedia device within the management period based on the device operation data specifically includes the following process:

[0013] Based on the equipment operation data, the vibration data, temperature data, current data and voltage data of the equipment in the management period S are obtained, and based on the vibration data, temperature data, current data and voltage data of the equipment in the management period S, a matrix with N elements is constructed. T The actual state matrix A, where the total amount of vibration data, temperature data, current data and voltage data of the equipment in the management period S is N S ;

[0014] Based on the equipment operation data, the standard state matrix B within the management period S is obtained, where the number of elements in the standard state matrix B is N. S ;

[0015] Calculate the operating reliability of multimedia equipment within the management period S:

[0016]

[0017] Among them, C S For the reliability of multimedia equipment operation, A i is the element in the actual state matrix A, B i is an element in the standard state matrix B, i = {1, 2, ..., N S}.

[0018] Furthermore, the data monitoring reliability calculated based on the equipment operation data specifically includes the following processes:

[0019] Acquire vibration data of the equipment in the management period S based on the equipment operation data, acquire a vibration frequency wave based on the vibration data, and determine the number of abnormal vibration frequencies ZD of the equipment in the management period S based on the vibration frequency wave;

[0020] Based on the equipment operation data, the temperature data of the equipment in the management period S is obtained, based on the temperature data, the average temperature of the equipment in the management period S is obtained, and based on the equipment operation data, the standard average temperature of the equipment in the management period S is obtained, and the difference WD between the average temperature and the standard average temperature is calculated;

[0021] Based on the equipment operation data, current data of the equipment in the management period S is obtained, based on the current data, the average current of the equipment in the management period S is obtained, and based on the equipment operation data, the standard average current of the equipment in the management period S is obtained, and the difference DL between the average current and the standard average current is calculated;

[0022] Based on the equipment operation data, voltage data of the equipment in the management period S is obtained; based on the voltage data, an average voltage of the equipment in the management period S is obtained; based on the equipment operation data, a standard average voltage of the equipment in the management period S is obtained; and a difference DY between the average voltage and the standard average voltage is calculated;

[0023] Substitute the vibration frequency abnormality times ZD, difference WD, difference DL and difference DY into the data monitoring reliability calculation formula to obtain the data monitoring reliability, which is as follows:

[0024]

[0025] Among them, JCZ represents the data monitoring reliability, k1, k2, and k3 are weight coefficients set by the system, and k1>k2>k3, WDB is the standard average temperature, DLB is the standard average current, DYB is the standard average voltage, and the value of e is 2.72.

[0026] Further, determining the number of abnormal vibration frequencies ZD of the equipment within the management period S based on the vibration frequency wave specifically includes the following process:

[0027] Establish a one-dimensional wave equation based on the vibration frequency wave:

[0028]

[0029] Where u is the frequency of the vibration detection point to be identified in the vibration frequency wave, t is the interval time of the vibration detection point, and CB is the standard frequency;

[0030] According to the one-dimensional wave equation, the frequency domain equation is obtained by performing discrete Fourier transform on it:

[0031]

[0032] Among them, τ is the phase angle. When the difference between the natural frequencies of adjacent vibration peaks is equal, that is, Δf=CB / S, the frequency of the vibration detection point to be identified is a normal frequency. If Δf is not equal to CB / S, the frequency of the vibration detection point to be identified is an abnormal vibration frequency.

[0033] Furthermore, cluster analysis is performed on the device status data to obtain cluster results, which specifically includes the following process:

[0034] Generate a sample set based on device status data;

[0035] Randomly generate cluster center matrix V j , where each cluster center V j Represents a device failure category;

[0036] Calculate the cluster center V for each sample in the sample set j The distance D j 2 :

[0037] D j 2 =(y k -V j ) T M j (y k -V j );

[0038] Among them, y k Represents the data matrix of the sample, (y k -V j ) T Represents y k -V j The device matrix, M j Represents the variable matrix to be optimized so that each cluster adjusts the distance norm according to the local topological structure of the data. The matrix M j The calculation is as follows:

[0039] M j =ρF j -1 ;

[0040] Among them, ρ is a constant, whose value is set by the system, F j The covariance matrix of the jth equipment failure category, matrix F j The calculation is as follows:

[0041]

[0042] in, represents the membership matrix of the equipment fault category of samples k and j in the l-1th iteration, N represents the number of samples in the sample set, and m is the iteration parameter;

[0043] Based on the distance D j 2 Calculate the minimum clustering optimization value J:

[0044]

[0045] Determine whether the minimum cluster optimization value J is less than the preset threshold. If so, cluster the sample k to the cluster center V. j In the nearby area, a data function image is obtained, and the data function image is recorded as the clustering result.

[0046] Further, the clustering results are analyzed and processed to obtain the equipment failure results, which specifically includes the following process:

[0047] Based on the data function image, determine whether the data distribution after clustering is a spherical cluster:

[0048] The data after the preliminary clustering processing is plotted into a two-dimensional scatter plot to observe whether the scattered points show a spherical distribution. If the scattered points are roughly distributed in a spherical area, it is considered that the data distribution after the preliminary clustering processing is a spherical cluster.

[0049] Alternatively, use the compactness coefficient and shape factor sphericity metrics to assess whether the data distribution after clustering is close to spherical;

[0050] In the case where the data distribution is a spherical cluster, the sample k belongs to the jth equipment failure category.

[0051] Furthermore, generating a multimedia device early warning signal based on the multimedia device operation reliability index and the device failure result specifically includes the following process:

[0052] Loading a multimedia device operation reliability index threshold, determining whether the multimedia device operation reliability index exceeds the multimedia device operation reliability index threshold, if not, generating a multimedia device operation warning signal, if yes, not generating a multimedia device operation warning signal, wherein the multimedia device operation reliability index threshold is set by the system and stored in the system;

[0053] Generate corresponding fault warning signals based on equipment failure results.

[0054] On the other hand, a multimedia device health management method based on big data includes:

[0055] Acquire multimedia device data during the operation of the multimedia device, wherein the multimedia device data includes device operation data and device status data;

[0056] The multimedia equipment operation reliability and data monitoring reliability within the management period are calculated based on the equipment operation data, and the multimedia equipment operation reliability index within the management period is calculated based on the multimedia equipment operation reliability and data monitoring reliability;

[0057] Perform cluster analysis on the equipment status data to obtain cluster results, and analyze and process the cluster results to obtain equipment failure results;

[0058] Generate multimedia equipment early warning signals based on multimedia equipment operation reliability index and equipment failure results.

[0059] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0060] The present invention obtains multimedia device data during the operation of the multimedia device, calculates the multimedia device operation reliability and data monitoring reliability within the management period based on the device operation data, and calculates the multimedia device operation reliability index within the management period based on the multimedia device operation reliability and data monitoring reliability; performs cluster analysis processing on the device status data to obtain clustering results, performs analysis processing on the clustering results to obtain device failure results; generates a multimedia device early warning signal based on the multimedia device operation reliability index and the device failure results, which can improve the accuracy of predicting and diagnosing the health status of the multimedia device.

[0061] The present invention can monitor the running status of multimedia devices in real time, and once abnormal behavior or potential failure is found, an early warning signal is immediately issued to remind the user to take corresponding maintenance measures. This helps to timely discover and handle equipment failures, avoiding the expansion of failures or causing greater losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0063] Figure 1 is a system block diagram of a multimedia device health management system based on big data according to an embodiment of the present invention;

[0064] Figure 2 This is a workflow diagram of a multimedia device health management system based on big data according to an embodiment of the present invention;

[0065] Figure 3 It is a workflow diagram of a multimedia device health management method based on big data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] 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.

[0067] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0068] This embodiment provides a multimedia device health management system based on big data. Figure 1 is a system block diagram of a multimedia device health management system based on big data according to an embodiment of the present invention, such as Figure 1 As shown, the system includes a multimedia device data acquisition module, a device operation data analysis module, a device fault analysis module and an early warning module;

[0069] A multimedia device data acquisition module, used to acquire multimedia device data during the operation of the multimedia device, wherein the multimedia device data includes device operation data and device status data;

[0070] The device operation data analysis module is used to calculate the multimedia device operation reliability and data monitoring reliability within the management period based on the device operation data, and calculate the multimedia device operation reliability index within the management period based on the multimedia device operation reliability and data monitoring reliability;

[0071] The equipment failure analysis module is used to perform cluster analysis on the equipment status data to obtain cluster results, and to analyze and process the cluster results to obtain equipment failure results;

[0072] The early warning module is used to generate a multimedia device early warning signal based on the multimedia device operation reliability index and the device failure result.

[0073] In summary, the present invention obtains multimedia device data during the operation of multimedia devices, calculates the multimedia device operation reliability and data monitoring reliability within the management period based on the device operation data, and calculates the multimedia device operation reliability index within the management period based on the multimedia device operation reliability and data monitoring reliability; performs cluster analysis on the device status data to obtain clustering results, analyzes and processes the clustering results to obtain device failure results; generates a multimedia device early warning signal based on the multimedia device operation reliability index and the device failure results, which can improve the accuracy of prediction and diagnosis of the health status of multimedia devices.

[0074] Furthermore, the present invention can monitor the operating status of multimedia devices in real time, and once abnormal behavior or potential failure is found, an early warning signal is immediately issued to remind the user to take corresponding maintenance measures. This helps to timely discover and handle equipment failures, avoiding the expansion of failures or causing greater losses.

[0075] In some embodiments, calculating the multimedia device operation reliability within the management period based on the device operation data specifically includes the following process:

[0076] Acquire vibration data, temperature data, current data and voltage data of the equipment within the management period S based on the equipment operation data;

[0077] The number of elements in the matrix is ​​N, which is constructed based on the vibration data, temperature data, current data and voltage data of the equipment in the management period S. T The actual state matrix A, where the total amount of vibration data, temperature data, current data and voltage data of the equipment in the management period S is N S ;

[0078] Based on the equipment operation data, the standard state matrix B within the management period S is obtained, where the number of elements in the standard state matrix B is N. S ;

[0079] Calculate the operating reliability of multimedia equipment within the management period S:

[0080]

[0081] Among them, C S For the reliability of multimedia equipment operation, A i is the element in the actual state matrix A, B i is an element in the standard state matrix B, i = {1, 2, ..., N S}.

[0082] In some embodiments, Figure 2 is a workflow diagram of a multimedia device health management system based on big data according to an embodiment of the present invention, such as Figure 2 As shown, the calculation of data monitoring reliability based on equipment operation data specifically includes the following steps:

[0083] Step S201: acquiring vibration data of the equipment in the management period S based on the equipment operation data, acquiring a vibration frequency wave based on the vibration data, and determining the number of abnormal vibration frequencies ZD of the equipment in the management period S based on the vibration frequency wave;

[0084] Step S202: acquiring temperature data of the device in the management period S based on the device operation data, acquiring the average temperature of the device in the management period S based on the temperature data, and acquiring the standard average temperature of the device in the management period S based on the device operation data, and calculating the difference WD between the average temperature and the standard average temperature;

[0085] Step S203: acquiring current data of the device in the management period S based on the device operation data, acquiring the average current of the device in the management period S based on the current data, and acquiring the standard average current of the device in the management period S based on the device operation data, and calculating the difference DL between the average current and the standard average current;

[0086] Step S204: obtaining voltage data of the device in the management period S based on the device operation data, obtaining the average voltage of the device in the management period S based on the voltage data, and obtaining the standard average voltage of the device in the management period S based on the device operation data, and calculating the difference DY between the average voltage and the standard average voltage;

[0087] Step S205: Substitute the vibration frequency abnormality times ZD, the difference WD, the difference DL and the difference DY into the data monitoring reliability calculation formula to obtain the data monitoring reliability, which is as follows:

[0088]

[0089] Among them, JCZ represents the data monitoring reliability, k1, k2, and k3 are weight coefficients set by the system, and k1>k2>k3, WDB is the standard average temperature, DLB is the standard average current, DYB is the standard average voltage, and the value of e is 2.72.

[0090] In some embodiments, determining the number of abnormal vibration frequencies ZD of the device within the management period S based on the vibration frequency wave specifically includes the following process:

[0091] Establish a one-dimensional wave equation based on the vibration frequency wave:

[0092]

[0093] Where u is the frequency of the vibration detection point to be identified in the vibration frequency wave, t is the interval time of the vibration detection point, and CB is the standard frequency;

[0094] According to the one-dimensional wave equation, the frequency domain equation is obtained by performing discrete Fourier transform on it:

[0095]

[0096] Among them, τ is the phase angle. When the difference between the natural frequencies of adjacent vibration peaks is equal, that is, Δf=CB / S, the frequency of the vibration detection point to be identified is a normal frequency. If Δf is not equal to CB / S, the frequency of the vibration detection point to be identified is an abnormal vibration frequency.

[0097] In some embodiments, cluster analysis is performed on the device status data to obtain cluster results, specifically including the following process:

[0098] Generate a sample set based on device status data;

[0099] Randomly generate cluster center matrix V j , where each cluster center V j Represents a device failure category;

[0100] Calculate the cluster center V for each sample in the sample set j The distance D j 2 :

[0101] D j 2 =(y k -V j ) T M j (y k -V j );

[0102] Among them, y k Represents the data matrix of the sample, (y k -V j ) T Represents y k -V j The device matrix, M j Represents the variable matrix to be optimized so that each cluster adjusts the distance norm according to the local topological structure of the data. The matrix M j The calculation is as follows:

[0103] M j =ρF j -1 ;

[0104] Among them, ρ is a constant, whose value is set by the system, F j The covariance matrix of the jth equipment failure category, matrix F j The calculation is as follows:

[0105]

[0106] in, represents the membership matrix of the equipment fault category of samples k and j in the l-1th iteration, N represents the number of samples in the sample set, and m is the iteration parameter;

[0107] Based on the distance D j 2 Calculate the minimum clustering optimization value J:

[0108]

[0109] Determine whether the minimum cluster optimization value J is less than the preset threshold. If so, cluster the sample k to the cluster center V. j In the nearby area, a data function image is obtained, and the data function image is recorded as the clustering result.

[0110] In some embodiments, analyzing and processing the clustering results to obtain the equipment failure results specifically includes the following process:

[0111] Based on the data function image, determine whether the data distribution after clustering is a spherical cluster:

[0112] The data after the preliminary clustering processing is plotted into a two-dimensional scatter plot to observe whether the scattered points show a spherical distribution. If the scattered points are roughly distributed in a spherical area, it is considered that the data distribution after the preliminary clustering processing is a spherical cluster.

[0113] Alternatively, use the compactness coefficient and shape factor sphericity metrics to assess whether the data distribution after clustering is close to spherical;

[0114] In the case where the data distribution is a spherical cluster, the sample k belongs to the jth equipment failure category.

[0115] Furthermore, generating a multimedia device early warning signal based on the multimedia device operation reliability index and the device failure result specifically includes the following process:

[0116] Loading a multimedia device operation reliability index threshold, determining whether the multimedia device operation reliability index exceeds the multimedia device operation reliability index threshold, if not, generating a multimedia device operation warning signal, if yes, not generating a multimedia device operation warning signal, wherein the multimedia device operation reliability index threshold is set by the system and stored in the system;

[0117] Generate corresponding fault warning signals based on equipment failure results.

[0118] In some embodiments, Figure 3 is a workflow diagram of a multimedia device health management method based on big data according to an embodiment of the present invention, such as Figure 3 As shown, the method comprises the following steps:

[0119] Step S301: Acquire multimedia device data during the operation of the multimedia device, wherein the multimedia device data includes device operation data and device status data;

[0120] Step S302: Calculate the multimedia device operation reliability and data monitoring reliability within the management period based on the device operation data, and calculate the multimedia device operation reliability index within the management period based on the multimedia device operation reliability and data monitoring reliability;

[0121] Step S303: performing cluster analysis on the device status data to obtain cluster results, and performing analysis on the cluster results to obtain device failure results;

[0122] Step S304: Generate a multimedia device warning signal based on the multimedia device operation reliability index and the device failure result.

[0123] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0124] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0126] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0127] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A multimedia equipment health management system based on big data, characterized in that: The system includes a multimedia device data acquisition module, a device operation data analysis module, a device failure analysis module and an early warning module; A multimedia device data acquisition module, used to acquire multimedia device data during the operation of the multimedia device, wherein the multimedia device data includes device operation data and device status data; The device operation data analysis module is used to calculate the multimedia device operation reliability and data monitoring reliability within the management period based on the device operation data, and calculate the multimedia device operation reliability index within the management period based on the multimedia device operation reliability and data monitoring reliability; The equipment failure analysis module is used to perform cluster analysis on the equipment status data to obtain cluster results, and to analyze and process the cluster results to obtain equipment failure results; The early warning module is used to generate a multimedia device early warning signal based on the multimedia device operation reliability index and the device failure result.

2. A multimedia device health management system based on big data according to claim 1, characterized in that: The operation reliability of multimedia equipment within the management period is calculated based on the equipment operation data, which specifically includes the following processes: Acquire vibration data, temperature data, current data and voltage data of the equipment within the management period S based on the equipment operation data; The number of elements in the matrix is ​​N, which is constructed based on the vibration data, temperature data, current data and voltage data of the equipment in the management period S. T The actual state matrix A, where the total amount of vibration data, temperature data, current data and voltage data of the equipment in the management period S is N S ; Based on the equipment operation data, the standard state matrix B within the management period S is obtained, where the number of elements in the standard state matrix B is N. S ; Calculate the operating reliability of multimedia equipment within the management period S: Among them, C S For the reliability of multimedia equipment operation, A i is the element in the actual state matrix A, B i is an element in the standard state matrix B, i = {1, 2, ..., N S }.

3. A multimedia device health management system based on big data according to claim 1, characterized in that: The reliability of data monitoring is calculated based on the equipment operation data. The process includes: Acquire vibration data of the equipment in the management period S based on the equipment operation data, acquire a vibration frequency wave based on the vibration data, and determine the number of abnormal vibration frequencies ZD of the equipment in the management period S based on the vibration frequency wave; Based on the equipment operation data, the temperature data of the equipment in the management period S is obtained, based on the temperature data, the average temperature of the equipment in the management period S is obtained, and based on the equipment operation data, the standard average temperature of the equipment in the management period S is obtained, and the difference WD between the average temperature and the standard average temperature is calculated; Based on the equipment operation data, current data of the equipment in the management period S is obtained, based on the current data, the average current of the equipment in the management period S is obtained, and based on the equipment operation data, the standard average current of the equipment in the management period S is obtained, and the difference DL between the average current and the standard average current is calculated; Based on the equipment operation data, voltage data of the equipment in the management period S is obtained; based on the voltage data, an average voltage of the equipment in the management period S is obtained; based on the equipment operation data, a standard average voltage of the equipment in the management period S is obtained; and a difference DY between the average voltage and the standard average voltage is calculated; Substitute the vibration frequency abnormality times ZD, difference WD, difference DL and difference DY into the data monitoring reliability calculation formula to obtain the data monitoring reliability, which is as follows: Among them, JCZ represents the data monitoring reliability, k1, k2, and k3 are weight coefficients set by the system, and k1>k2>k3, WDB is the standard average temperature, DLB is the standard average current, DYB is the standard average voltage, and the value of e is 2.

72.

4. A multimedia device health management system based on big data according to claim 3, characterized in that: Determining the number of abnormal vibration frequencies ZD of the equipment within the management period S based on the vibration frequency wave specifically includes the following process: Establish a one-dimensional wave equation based on the vibration frequency wave: Where u is the frequency of the vibration detection point to be identified in the vibration frequency wave, t is the interval time of the vibration detection point, and CB is the standard frequency; According to the one-dimensional wave equation, the frequency domain equation is obtained by performing discrete Fourier transform on it: Among them, τ is the phase angle. When the difference between the natural frequencies of adjacent vibration peaks is equal, that is, Δf=CB / S, the frequency of the vibration detection point to be identified is a normal frequency. If Δf is not equal to CB / S, the frequency of the vibration detection point to be identified is an abnormal vibration frequency.

5. The multimedia device health management system based on big data according to claim 1, characterized in that: The clustering analysis of the equipment status data to obtain the clustering results specifically includes the following processes: Generate a sample set based on device status data; Randomly generate cluster center matrix V j , where each cluster center V j Represents a device failure category; Calculate the cluster center V for each sample in the sample set j The distance D j 2 : D j 2 =(y k -V j ) T M j (y k -V j ); Among them, y k Represents the data matrix of the sample, (y k -V j ) T Represents y k -V j The device matrix, M j Represents the variable matrix to be optimized so that each cluster adjusts the distance norm according to the local topological structure of the data. The matrix M j The calculation is as follows: M j =ρF j -1 ; Among them, ρ is a constant, whose value is set by the system, F j The covariance matrix of the jth equipment failure category, matrix F j The calculation is as follows: in, represents the membership matrix of the equipment fault category of samples k and j in the l-1th iteration, N represents the number of samples in the sample set, and m is the iteration parameter; Based on the distance D j 2 Calculate the minimum clustering optimization value J: Determine whether the minimum cluster optimization value J is less than the preset threshold. If so, cluster the sample k to the cluster center V. j In the nearby area, a data function image is obtained, and the data function image is recorded as the clustering result.

6. A multimedia device health management system based on big data according to claim 5, characterized in that: The clustering results are analyzed and processed to obtain the equipment failure results, which specifically includes the following processes: Based on the data function image, determine whether the data distribution after clustering is a spherical cluster: Draw the data after preliminary clustering processing into a two-dimensional scatter plot to observe whether the scattered points show a spherical distribution shape; If the scattered points are roughly distributed in a spherical area, the data distribution after the preliminary clustering processing is considered to be a spherical cluster; Alternatively, use the compactness coefficient and shape factor sphericity metrics to assess whether the data distribution after clustering is close to spherical; In the case where the data distribution is a spherical cluster, the sample k belongs to the jth equipment failure category.

7. The multimedia device health management system based on big data according to claim 1 is characterized in that: Generate multimedia equipment early warning signals based on multimedia equipment operation reliability index and equipment failure results The process includes: Loading a multimedia device operation reliability index threshold, determining whether the multimedia device operation reliability index exceeds the multimedia device operation reliability index threshold, if not, generating a multimedia device operation warning signal, if yes, not generating a multimedia device operation warning signal, wherein the multimedia device operation reliability index threshold is set by the system and stored in the system; Generate corresponding fault warning signals based on equipment failure results.

8. A multimedia device health management method based on big data, characterized in that: A multimedia device health management system based on big data applicable to any one of claims 1 to 7, the method comprising: Acquire multimedia device data during the operation of the multimedia device, wherein the multimedia device data includes device operation data and device status data; The multimedia equipment operation reliability and data monitoring reliability within the management period are calculated based on the equipment operation data, and the multimedia equipment operation reliability index within the management period is calculated based on the multimedia equipment operation reliability and data monitoring reliability; Perform cluster analysis on the equipment status data to obtain cluster results, and analyze and process the cluster results to obtain equipment failure results; Generate multimedia equipment early warning signals based on multimedia equipment operation reliability index and equipment failure results.

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