Remote maintenance and fault diagnosis method and system for projection equipment

Abstract

The invention relates to the field of electronic information, and discloses a remote maintenance and fault diagnosis method and system for projection equipment, and the method comprises the steps: firstly collecting equipment operation parameters, verifying the equipment operation parameters through an identification code, and generating an operation verification record; then associating historical fault data, extracting abnormal parameters, monitoring deviation degree in real time, predicting a fault type and positioning a problem component; a maintenance target is formulated based on the specific state points, and a remote instruction is constructed with reference to a maintenance standard; and after maintenance operation is executed through the cloud platform, validity of feedback data is verified, and a digital report including fault diagnosis, maintenance effect evaluation and trend pre-judgment is generated. According to the invention, the efficiency of projection equipment maintenance and fault processing can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for remote maintenance and fault diagnosis of a projection device, belonging to the field of electronic information. Background Art

[0002] In the current era of rapid digital and intelligent development, projection devices, as important tools in the fields of education, conferences, entertainment, etc., are used more frequently and in a wider range of scenarios. However, with the increase in the number of projection devices and the complexity of their functions, the maintenance and fault diagnosis of the devices have become the main challenges faced by users and operation and maintenance personnel.

[0003] Currently, the maintenance and fault diagnosis of projection devices mainly rely on on-site inspections and manual operations. This method is not only inefficient but also costly. Especially when the devices are widely distributed or located in remote areas, it is difficult for operation and maintenance personnel to respond quickly, resulting in an extended downtime of the devices and affecting normal use. In addition, the traditional maintenance method lacks the ability to monitor the running status of the devices in real time and analyze data, and cannot give early warnings of potential faults, resulting in the discovery of serious problems only after they occur, further affecting the maintenance efficiency. Therefore, a method for remote maintenance and fault diagnosis of projection devices is needed to improve the efficiency of projection device maintenance and fault handling. Summary of the Invention

[0004] The present invention provides a method and system for remote maintenance and fault diagnosis of a projection device, and its main purpose is to improve the efficiency of projection device maintenance and fault handling.

[0005] To achieve the above object, a method for remote maintenance and fault diagnosis of a projection device provided by the present invention includes:

[0006] Obtain the device operation parameters corresponding to the projection device, and collect the device identification code corresponding to the device operation parameters. Based on the device identification code, perform a verification process on the device operation parameters to obtain a running verification record;

[0007] Perform an association process on the running verification record and the historical fault data of the projection device to obtain associated fault data. Query the fault running traces in the associated fault data, screen out the abnormal operation parameters in the fault running traces, and monitor the real-time running status corresponding to the projection device in real time, and calculate the deviation degree value corresponding to the abnormal operation parameters and the real-time running status;

[0008] Based on the deviation degree value, analyze the possible fault types of the projection device. Based on the fault types, locate the problem components in the projection device, perform a detailed inspection on the problem components to obtain the actual state of the components, and query the specific state points in the actual state of the components;

[0009] Based on the specific state points, formulate the maintenance planning objectives corresponding to the projection device, analyze the maintenance measures in the maintenance planning objectives, query the maintenance standards corresponding to the maintenance measures, and construct the remote maintenance instructions corresponding to the projection device based on the maintenance standards;

[0010] After sending the remote maintenance instructions to a preset cloud monitoring platform, perform remote maintenance operations on the projection device to obtain device maintenance records, acquire the maintenance feedback data in the device maintenance records, and perform validity verification on the maintenance feedback data to obtain valid verification data. Generate a fault diagnosis and maintenance report corresponding to the projection device based on the valid verification data.

[0011] Optionally, the verification processing of the device operation parameters based on the device identification code to obtain an operation verification record includes:

[0012] Analyze the device operation type corresponding to the device identification code;

[0013] Query the standard operation range corresponding to the device operation type;

[0014] Extract the key operation indicators in the standard operation range;

[0015] Based on the key operation indicators, construct an operation status matrix corresponding to the device operation parameters;

[0016] Perform state point detection on the operation status matrix to obtain operation state points;

[0017] Based on the operation state points, perform verification processing on the device operation parameters to obtain an operation verification record.

[0018] Optionally, the correlation processing of the operation verification record with the historical fault data of the projection device to obtain correlation fault data includes:

[0019] Extract the abnormal verification parameters in the operation verification record;

[0020] Based on the abnormal verification parameters, match the historical fault data of the projection device to obtain preliminary matching features;

[0021] Analyze the feature distribution state corresponding to the preliminary matching features;

[0022] Based on the feature distribution state, calculate the feature correlation values between the preliminary matching features;

[0023] Based on the feature correlation values, screen the highly correlated fault data in the historical fault data;

[0024] Normalize the highly correlated fault data to obtain correlated fault data.

[0025] Optionally, calculating the feature correlation value between the preliminary matching features based on the feature distribution state includes:

[0026] Calculate the feature correlation value between the preliminary matching features using the following formula:

[0027]

[0028] where Fl represents the feature correlation value between the preliminary matching features, n represents the number of the preliminary matching features, V i represents the distribution state value corresponding to the i-th preliminary matching feature, represents the average distribution state value corresponding to the preliminary matching features, S V represents the standard deviation corresponding to the distribution state value, W i represents the feature weight value corresponding to the i-th preliminary matching feature, M represents the covariance matrix corresponding to the preliminary matching features, and det(M) represents the determinant of matrix M.

[0029] Optionally, calculating the deviation degree value corresponding to the abnormal operating parameter and the real-time operating state includes:

[0030] Calculate the deviation degree value corresponding to the abnormal operating parameter and the real-time operating state using the following formula:

[0031]

[0032] where DP represents the deviation degree value corresponding to the abnormal operating parameter and the real-time operating state, m represents the number of parameters corresponding to the abnormal operating parameter, j represents the number index corresponding to the abnormal operating parameter, A j represents the actual measured value corresponding to the j-th abnormal operating parameter, R j represents the parameter reference value corresponding to the j-th abnormal operating parameter for the real-time operating state, and W represents the average fluctuation coefficient corresponding to the abnormal operating parameter.

[0033] Optionally, analyzing the possible fault types of the projection device based on the deviation degree value includes:

[0034] Divide the deviation degree value into intervals to obtain a deviation degree interval;

[0035] Extract the abnormal interval features in the deviation degree interval;

[0036] Perform pattern matching on the abnormal interval features to obtain an abnormal feature pattern;

[0037] Query the fault matching rules in the preset fault type library based on the abnormal feature pattern;

[0038] Analyze the possible fault types of the projection device based on the fault matching rules.

[0039] Optionally, the specific state points in querying the actual state of the component include:

[0040] Analyze the component state indicators corresponding to the actual state of the component;

[0041] Construct a component index matrix corresponding to the component state indicators;

[0042] Perform dimensionality reduction processing on the component index matrix to obtain a dimensionality reduction state feature set;

[0043] Extract the specific state points in the dimensionality reduction state feature set.

[0044] Optionally, based on the specific state points, formulating the maintenance planning objectives corresponding to the projection device includes:

[0045] Analyze the state impact factors corresponding to the specific state points;

[0046] Based on the state impact factors, divide the maintenance requirements of the projection device to obtain a set of maintenance requirements;

[0047] Query the requirement priorities corresponding to the requirements in the set of maintenance requirements;

[0048] Based on the requirement priorities, formulate the requirement maintenance processes corresponding to the requirements in the set of maintenance requirements;

[0049] Based on the requirement maintenance processes, formulate the maintenance planning objectives corresponding to the projection device.

[0050] Optionally, based on the maintenance standards, constructing the remote maintenance instructions corresponding to the projection device includes:

[0051] Analyze the maintenance levels corresponding to different maintenance tasks under the maintenance standards;

[0052] Based on the maintenance levels, identify the core components and auxiliary components corresponding to the projection device;

[0053] Determine the component operation sequence corresponding to the core components and auxiliary components;

[0054] Based on the component operation sequence, formulate the detailed execution logic corresponding to the projection device;

[0055] Based on the detailed execution logic, construct the remote maintenance instructions corresponding to the projection device.

[0056] To solve the above problems, the present invention further provides a remote maintenance and fault diagnosis system for a projection device, and the system includes:

[0057] A verification processing module, configured to obtain device operation parameters corresponding to the projection device, collect a device identification code corresponding to the device operation parameters, and based on the device identification code, perform verification processing on the device operation parameters to obtain an operation verification record;

[0058] A deviation calculation module, configured to perform association processing on the operation verification record and historical fault data of the projection device to obtain associated fault data, query fault operation traces in the associated fault data, filter abnormal operation parameters in the fault operation traces, and real-time monitor a real-time operation state corresponding to the projection device, and calculate a deviation degree value corresponding to the abnormal operation parameter and the real-time operation state;

[0059] A state point query module, configured to analyze possible fault types of the projection device based on the deviation degree value, locate a problem component in the projection device based on the fault type, perform detailed detection on the problem component to obtain an actual state of the component, and query specific state points in the actual state of the component;

[0060] An instruction construction module, configured to formulate a maintenance planning goal corresponding to the projection device based on the specific state points, analyze maintenance measures in the maintenance planning goal, query maintenance standards corresponding to the maintenance measures, and based on the maintenance standards, construct a remote maintenance instruction corresponding to the projection device;

[0061] A report generation module, configured to send the remote maintenance instruction to a preset cloud monitoring platform, perform a remote maintenance operation on the projection device to obtain a device maintenance record, obtain maintenance feedback data in the device maintenance record, perform validity verification on the maintenance feedback data to obtain valid verification data, and based on the valid verification data, generate a fault diagnosis and maintenance report corresponding to the projection device.

[0062] Compared with the problems described in the background art, the present invention can realize real-time monitoring and accurate positioning of the operating state of the projection device by obtaining the device operating parameters corresponding to the projection device and collecting the device identification code corresponding to the device operating parameters, providing a data traceability basis for subsequent fault diagnosis. At the same time, by combining historical data, rapid identification of abnormal parameters and early warning of potential faults can be achieved, effectively improving the remote maintenance efficiency and reducing the manual inspection cost. The present invention associates the operation verification record with the historical fault data of the projection device to obtain associated fault data, and can mine potential fault patterns through multi-dimensional data comparison, realizing accurate attribution and trend prediction of device anomalies, quickly positioning the corresponding relationship between repeatedly occurring parameter deviations and historical faults, and improving the accuracy and timeliness of fault diagnosis. Further, based on the deviation degree value, the present invention analyzes the possible fault types of the projection device, which helps the operation and maintenance personnel quickly judge problems in aspects such as heat dissipation, light source, or circuit, prepare maintenance plans and spare parts in advance, reduce device downtime, and at the same time, by long-term analyzing the association between the deviation degree value and the fault type, the device maintenance strategy can be optimized, improving the reliability and service life of the device. Further, based on the specific state points, the present invention formulates the maintenance planning objectives corresponding to the projection device, can accurately locate the components and time nodes that need to be key maintained, such as replacing the lamp in advance for the state point with excessive light decay to avoid sudden failures, and combined with the trend analysis of the state points, can also predict the remaining life of the components, formulate preventive maintenance strategies, extend the overall service life of the device, and ensure continuous and stable operation. Finally, after the present invention sends the remote maintenance instruction to a preset cloud monitoring platform, performs a remote maintenance operation on the projection device to obtain a device maintenance record, can automatically analyze the maintenance effect (such as the light flux recovery rate), providing data support for optimizing the maintenance strategy; at the same time, by associating historical fault data, can predict the fault cycle of similar components (such as the life model of the cooling fan), changing passive maintenance to active prevention. Therefore, a projection device remote maintenance and fault diagnosis method and system provided by an embodiment of the present invention can improve the efficiency of projection device maintenance and fault handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 FIG. is a schematic flow chart of a projection device remote maintenance and fault diagnosis method provided by an embodiment of the present invention;

[0064] Figure 2 FIG. is a schematic module diagram of a projection device remote maintenance and fault diagnosis system provided by an embodiment of the present invention.

[0065] The implementation, functional features, and advantages of the present invention will be further described in conjunction with the embodiments with reference to the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] It should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0067] An embodiment of the present application provides a method for remote maintenance and fault diagnosis of a projection device. The execution subject of the method for remote maintenance and fault diagnosis of a projection device includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for remote maintenance and fault diagnosis of a projection device can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0068] Embodiment 1:

[0069] Refer to Figure 1 As shown, it is a schematic flowchart of a method for remote maintenance and fault diagnosis of a projection device provided by an embodiment of the present invention. In this embodiment, the method for remote maintenance and fault diagnosis of a projection device includes:

[0070] S1. Obtain the device operation parameters corresponding to the projection device, collect the device identification code corresponding to the device operation parameters, and based on the device identification code, perform a verification process on the device operation parameters to obtain an operation verification record.

[0071] By obtaining the device operation parameters corresponding to the projection device and collecting the device identification code corresponding to the device operation parameters, the present invention can realize real-time monitoring and accurate positioning of the device operation status, provide a data traceability basis for subsequent fault diagnosis, and at the same time, combine historical data to realize rapid identification of abnormal parameters and early warning of potential faults, effectively improving the remote maintenance efficiency and reducing the manual inspection cost.

[0072] Among them, the projection device refers to an electronic device that converts digital signals into visual images through optical imaging technology, mainly including types such as projectors, laser TVs, and smart micro-projectors. Its core function is to project the content of terminal devices such as computers and mobile phones onto a screen or wall, and is widely used in scenarios such as education and teaching, enterprise meetings, and home entertainment. Modern projection devices integrate functions such as intelligent operating systems and wireless transmission modules, and can achieve remote control and data interaction through the network; the device operating parameters refer to the key data indicators reflecting the working state of the projection device, including but not limited to: ① The temperature of core components (such as the light source module, heat dissipation system); ② Consumable usage data (lamp cumulative duration, filter replacement cycle); ③ Signal transmission quality (HDMI / USB interface voltage, wireless connection stability); ④ Environmental parameters (ambient light intensity, device tilt angle); ⑤ System logs (error codes, abnormal operation records). These parameters are collected in real time through modules such as sensors and embedded chips to form a device health record; the device identification code refers to the unique identity identifier assigned to each projection device, usually composed of a combination of hardware encoding (such as MAC address, IMEI code) and software encoding (serial number, UUID). Its core role is to establish a mapping relationship between physical devices and digital systems, supporting the remote system to quickly locate the target device. For example, binding network ports through the MAC address, or associating device purchase information and warranty records through the serial number. Optionally, the acquisition of the device operating parameters corresponding to the projection device can be achieved through hardware sensor acquisition methods, such as: using a built-in temperature sensor (such as an NTC thermistor) to monitor the temperature of the light source module in real time, obtaining power load data through a current sensor, combining an acceleration sensor to detect the vibration state of the device, and finally converting the analog signal into digital operating parameters through an analog-to-digital conversion module (such as an ADC chip); the acquisition of the device identification code corresponding to the device operating parameters can be achieved through the hardware register reading method, such as: using an I2C bus tool (such as i2cdetect) to scan an EEPROM chip (such as 24C02), and parsing the MAC address (00:1A:79:xx:xx:xx) stored therein as the identification code.

[0073] Furthermore, based on the device identification code, the present invention performs a verification process on the device operating parameters to obtain an operation verification record, which can timely detect abnormal offsets or transmission errors in the data acquisition process, provide a reliable data basis for subsequent fault diagnosis, and at the same time establish a verification record file for the entire life cycle of the device, facilitating the tracing of historical data change trends and improving the accuracy and efficiency of maintenance decisions.

[0074] Among them, the operation verification record refers to a verification report recording the matching degree between the device operating parameters and the standard range, including verification time, abnormal parameter name, deviation value, historical comparison curve, etc., and is used to generate a basis for maintenance decisions.

[0075] As an embodiment of the present invention, the verification process of the device operation parameters based on the device identification code to obtain an operation verification record includes: parsing the device operation type corresponding to the device identification code; querying the standard operation range corresponding to the device operation type; extracting the key operation indicators in the standard operation range; constructing an operation status matrix corresponding to the device operation parameters based on the key operation indicators; performing a status point detection on the operation status matrix to obtain an operation status point; and performing a verification process on the device operation parameters based on the operation status point to obtain an operation verification record.

[0076] Among them, the device operation type refers to the classification of projection devices based on application scenarios or functional modes, such as education mode (high brightness / long life), cinema mode (high color restoration), energy-saving mode (low power consumption), etc. Different types correspond to different operation parameter standards; the standard operation range refers to the parameter threshold range preset according to device models, operation types, etc. For example, in the education mode, the light source temperature should be between 40-70°C, and the fan speed should be maintained at 2000-4000 rpm. If it exceeds this range, it is determined as abnormal; the key operation indicators refer to the core monitoring parameters selected from the device operation parameters, usually including light source temperature, fan speed, signal delay rate, light flux attenuation degree, etc.; the operation status matrix refers to a multi-dimensional evaluation model constructed by weighting the key operation indicators. For example, the horizontal axis is indicators such as temperature and speed, and the vertical axis is the time series. The matrix unit shows the deviation degree of the real-time value from the standard range; the operation status point refers to the specific data point representing the current state of the device in the matrix, such as temperature 65°C (standard 40-70°C), speed 3500 rpm (standard 2000-4000 rpm), and the abnormal dimension can be quickly identified through coordinate positioning.

[0077] Further, the parsing of the device operation type corresponding to the device identification code can be implemented by the database query method. For example, the corresponding relationship between the device identification code and the operation type is stored in a database (such as MySQL). After obtaining the device identification code, the corresponding operation type is retrieved from the database through an SQL query statement. The query of the standard operation range corresponding to the device operation type can be implemented by the data interface call method. For example, by calling the API interface provided by the device manufacturer and passing the device operation type as a parameter, the standard operation range corresponding to this operation type is obtained from the cloud server. The extraction of the key operation indicators in the standard operation range can be implemented by a feature selection algorithm. For example, using feature selection algorithms in machine learning, such as chi-square test, information gain, etc., to evaluate all the parameters in the standard operation range, and screening out the parameters highly relevant to the device performance and stability as the key operation indicators. The construction of the operation status matrix corresponding to the device operation parameters can be implemented by the NumPy library in Python. For example, using the array operation function of NumPy to organize the device operation parameters into a matrix form according to certain rules. The detection of the status points for the operation status matrix can be implemented by a clustering algorithm. For example, using the K-Means clustering algorithm to perform clustering analysis on the data in the operation status matrix, dividing the data into different categories, and the center point or boundary point of each category can be used as the operation status point. The verification process for the device operation parameters can be implemented by a machine learning model verification. For example, using a pre-trained machine learning model (such as a support vector machine, neural network, etc.) to verify the device operation parameters. The device operation parameters are used as the input of the model, and the model outputs the verification result.

[0078] S2. Correlate the operation verification record with the historical fault data of the projection device to obtain associated fault data, query the fault operation traces in the associated fault data, screen out the abnormal operation parameters in the fault operation traces, and real-time monitor the real-time operation status corresponding to the projection device, and calculate the deviation degree value between the abnormal operation parameters and the real-time operation status.

[0079] By correlating the operation verification record with the historical fault data of the projection device, the present invention obtains associated fault data, and can mine potential fault modes through multi-dimensional data comparison, realizes the accurate attribution and trend prediction of device anomalies, can quickly locate the corresponding relationship between the repeatedly occurring parameter deviations and historical faults, and improves the accuracy and timeliness of fault diagnosis.

[0080] Among them, the historical failure data refers to the set of failure information accumulated during the long-term operation of the projection device, including multi-dimensional data such as device model, failure occurrence time, failure phenomenon description, maintenance records, replaced component models, environmental parameters, etc. Its core value lies in establishing the mapping relationship between the failure type and the deviation of operating parameters by analyzing the occurrence law of historical failures. For example, it is found that there is a strong correlation between the continuously high light source temperature and the shortening of the lamp life. The historical failure data not only provides a comparison benchmark for the current device anomalies, but also can predict potential failure risks through time series analysis; the associated failure data refers to the structured data set formed after normalizing the highly associated failure data. The normalization methods include Z-score standardization, Min-Max scaling, etc., aiming to eliminate the influence of features with different dimensions. The processed associated data contains fields in a unified format, such as failure type (code + description), occurrence probability (based on historical frequency), recommended maintenance measures (replaced component model + operation steps), etc.

[0081] As an embodiment of the present invention, the associating the operation verification record with the historical failure data of the projection device to obtain associated failure data includes: extracting abnormal verification parameters from the operation verification record; based on the abnormal verification parameters, matching the historical failure data of the projection device to obtain preliminary matching features; analyzing the feature distribution state corresponding to the preliminary matching features; calculating the feature association value between the preliminary matching features based on the feature distribution state; screening the highly associated failure data in the historical failure data based on the feature association value; and normalizing the highly associated failure data to obtain associated failure data.

[0082] Among them, the abnormal verification parameter refers to the core monitoring parameter with a significant deviation from the standard operating range of the device in the operation verification record. These parameters are usually identified through threshold detection, statistical process control (SPC), or machine learning models. For example, the light source temperature exceeds the reference value by ±15°C, the light flux attenuation rate exceeds the expected value by 20%, etc. The abnormal verification parameter is the key signal triggering fault diagnosis, and the degree of its deviation directly reflects the current health status of the device; the preliminary matching feature refers to the set of matching features extracted from the historical fault database based on the abnormal verification parameter. These features can include fault codes (such as ERR_037 indicating color wheel abnormality), fault occurrence timestamps, environmental parameters (such as humidity > 70%), consumable usage status (such as filter clogging level), etc. The realization of preliminary matching usually depends on keyword matching or vector space model (VSM). For example, mapping the abnormal temperature parameter to the record with the "high temperature" label in the historical data; the feature distribution state refers to the distribution law of the preliminary matching features shown in the time, space, or numerical dimension through statistical analysis methods. For example, using KDE kernel density estimation to show the occurrence frequency of a certain fault code in different months, or presenting the concentrated area of abnormal parameters in different parts of the device through a heat map; the feature correlation value refers to the degree of correlation between the preliminary matching features. The higher the feature correlation value, the closer the connection between these preliminary matching features, and the greater the influence of their simultaneous occurrence or mutual influence in scenarios such as fault analysis; the high-correlation fault data refers to the historical fault records with strong correlation with the current abnormal parameter selected according to the feature correlation value. The screening process usually sets a dynamic threshold and filters in combination with business rules (such as giving priority to faults of the same type of device). For example, when a device currently has a temperature abnormality, the system will screen out the fault records in the historical data with a temperature correlation value greater than the preset threshold and occurring in the same type of device.

[0083] Further, the extraction of abnormal verification parameters in the operation verification record can be achieved by the threshold comparison method. For example, set the normal threshold range for each operation parameter, compare the parameters in the operation verification record with this threshold, and the parameters outside the range are the abnormal verification parameters. The matching of the historical fault data of the projection device can be achieved by the rule matching algorithm. For example, a series of matching rules are predefined in advance, and according to the abnormal verification parameters in the operation verification record, search for records that meet the rules in the historical fault data. For example, when the abnormal verification parameter is "too high temperature", search for records in the historical fault data that contain relevant descriptions of "too high temperature". The analysis of the feature distribution state corresponding to the preliminary matching features can be achieved by the histogram statistics method. For example, group the preliminary matching features according to a certain interval, count the number of features in each interval, and draw a histogram to show the distribution of the features. The calculation of the feature correlation value between the preliminary matching features can be achieved by the following calculation formula. The screening of the high-correlation fault data in the historical fault data can be achieved by the threshold screening method. For example, set a threshold for the feature correlation value, and screen out the historical fault data with a feature correlation value higher than this threshold as the high-correlation fault data. The normalization processing of the high-correlation fault data can be achieved by the normalization method. For example, the Min-Max scaling method, the Z-score standardization method, etc.

[0084] As an embodiment of the present invention, the calculation of the feature correlation value between the preliminary matching features based on the feature distribution state includes:

[0085] Calculate the feature correlation value between the preliminary matching features by using the following formula:

[0086]

[0087] where Fl represents the feature correlation value between the preliminary matching features, n represents the number of the preliminary matching features, V i represents the distribution state value corresponding to the i-th preliminary matching feature, V represents the average distribution state value corresponding to the preliminary matching features, S V represents the standard deviation corresponding to the distribution state value, W i represents the feature weight value corresponding to the i-th preliminary matching feature, M represents the covariance matrix corresponding to the preliminary matching features, and det(M) represents the determinant of the matrix M.

[0088] Specifically, the feature correlation value is used to measure the degree of association between initially matched features. The higher the feature correlation value, the closer the connection between these initially matched features, and the greater their influence when occurring simultaneously or interacting with each other in scenarios such as fault analysis. The distribution state value refers to the numerical value corresponding to each initially matched feature that can reflect its distribution characteristics, which reflects the distribution of this feature in the dataset, such as information on frequencies, probabilities, etc. in different intervals, and is one of the key parameters in the formula for calculating the feature correlation value. The average distribution state value refers to the average of the distribution state values corresponding to all initially matched features, which provides an averaged description of the distribution states of these features as a whole. When calculating the feature correlation value, it is used to compare with the distribution state value of each feature to measure the degree to which the distribution state value of a single feature deviates from the overall average level. The standard deviation refers to the standard deviation corresponding to the distribution state value, which is used to measure the degree of dispersion of these distribution state values. The larger the standard deviation, the more dispersed the distribution state values, and vice versa. The feature weight value refers to the relative importance of each initially matched feature in the analysis process. Different features have different importance in scenarios such as fault analysis. By assigning corresponding weight values, the role of key features can be highlighted when calculating the feature correlation value. The covariance matrix refers to a matrix that describes the mutual relationship between initially matched features, and the elements in the matrix reflect the covariance between different features. A positive covariance indicates that two features tend to change in the same direction, a negative covariance indicates that they tend to change in the opposite direction, and a covariance of 0 indicates that there is no linear correlation between the two. The determinant refers to the determinant of the covariance matrix, and the value of the determinant can reflect some characteristics of the covariance matrix and is used to comprehensively adjust the feature correlation value in the formula, considering the overall situation of the mutual relationship between multiple features.

[0089] By querying the fault operation traces in the associated fault data, the present invention can accurately trace the entire process of the occurrence of the projection device fault, which not only helps to quickly locate the root cause of the fault and improve the maintenance efficiency, but also can predict potential faults based on historical traces, take preventive maintenance measures in advance, reduce the equipment downtime risk, and ensure the stable operation of the equipment.

[0090] Among them, the fault operation traces refer to a series of information records generated during the operation of the projection device before and after a fault occurs, which can reflect the changes in the device state. It includes the abnormal fluctuations of device operation parameters, such as the time nodes and change amplitudes when parameters such as temperature, voltage, and light flux deviate from the normal range; device operation logs, such as power-on and power-off times, function switching records, etc.; and environmental data collected by sensors, such as humidity, dust concentration, etc. Optionally, querying the fault operation traces in the associated fault data can be achieved through a rule matching-based method. For example, a series of rules related to fault operation traces are predefined, and by comparing the associated fault data with these rules, the data that conforms to the rules is the fault operation trace.

[0091] Furthermore, by screening the abnormal operation parameters in the fault operation traces and real-time monitoring the corresponding real-time operation state of the projection device, the present invention can promptly detect potential risks of the device, give early warnings at the budding stage of the fault, avoid the interruption of use caused by sudden faults. At the same time, based on the analysis of abnormal parameters, it can accurately locate the problems of the device, provide clear guidance for maintenance, and can also optimize the device operation strategy through long-term monitoring, improving the overall performance and service life of the device.

[0092] Among them, the abnormal operation parameters refer to the key monitoring parameters of the projection device that deviate from its standard operation range or historical baseline during operation, and are usually identified through threshold detection, statistical process control (SPC), or machine learning algorithms. For example, the temperature of the light source module exceeds 75 °C (standard 40 - 70 °C), the fan speed is lower than 1800 rpm (standard 2000 - 4000 rpm), the light flux attenuation rate exceeds 15% per thousand hours, etc. The abnormal fluctuations of these parameters indicate problems such as component aging, poor heat dissipation, or circuit failures of the device; the real-time operation state refers to the set of current working state information of the projection device, which is obtained in real time through sensors, embedded systems, or remote monitoring protocols. These state data include: ① Core component states (such as the bulb lighting duration, color wheel rotation speed); ② Signal transmission quality (HDMI interface voltage, wireless screen mirroring delay rate); ③ Environmental parameters (ambient temperature and humidity, device tilt angle); ④ System health status (memory occupancy rate, firmware version number). The monitoring of the real-time operation state usually relies on Internet of Things (IoT) technology. For example, device data is uploaded to the cloud platform through the MQTT protocol, or the device status is remotely retrieved using the RESTful API interface. Optionally, the abnormal operation parameters in the fault operation traces can be screened through the Isolation Forest algorithm. For example, the parameter data in the fault operation traces is used as input, and the outliers in the data are identified through the Isolation Forest algorithm, and the parameters corresponding to these outliers are the abnormal operation parameters; the real-time monitoring of the real-time operation state corresponding to the projection device can be achieved through the device management protocol method. For example, communicate with the projection device through standard device management protocols (such as SNMP, HTTP / RESTful API) to regularly obtain the real-time operation state information of the device.

[0093] Furthermore, by calculating the deviation degree value corresponding to the abnormal operation parameters and the real-time operation state, the present invention can quantify the deviation degree of the current state of the device from the standard operation baseline, provide an intuitive risk assessment basis for fault diagnosis, and through dynamic analysis of the deviation trend, potential fault hazards can be identified in advance, such as abnormal bulb aging rate or decline in the efficiency of the heat dissipation system, to achieve preventive maintenance.

[0094] Among them, the deviation degree value refers to the numerical value used to quantify the difference degree between the abnormal operation parameters and the real-time operation state, which comprehensively considers the differences between the actual measured values of multiple abnormal operation parameters and the parameter reference values under the corresponding real-time operation state, as well as the average fluctuation characteristics of the abnormal operation parameters themselves.

[0095] As an embodiment of the present invention, the calculation of the deviation degree value corresponding to the abnormal operation parameters and the real-time operation state includes:

[0096] Calculate the deviation degree value corresponding to the abnormal operation parameter and the real-time operation state by using the following formula:

[0097]

[0098] where DP represents the deviation degree value corresponding to the abnormal operation parameter and the real-time operation state, m represents the number of parameters corresponding to the abnormal operation parameter, j represents the quantity index corresponding to the abnormal operation parameter, A j represents the actual measured value corresponding to the jth abnormal operation parameter, R j represents the parameter reference value corresponding to the jth abnormal operation parameter for the real-time operation state, and W represents the average fluctuation coefficient corresponding to the abnormal operation parameter.

[0099] Specifically, the actual measured value refers to the specific value of the jth abnormal operation parameter obtained through sensors or other measurement means during actual operation. For example, when the projection device is running, the current light source temperature measured by a temperature sensor, the actual rotation speed of the fan obtained through a rotation speed sensor, etc.; the parameter reference value refers to the reference standard value of the jth abnormal operation parameter in the real-time operation state. For example, according to the design standard of the projection device, the reference value range of the light source temperature during normal operation, or the average value obtained by statistically analyzing the temperature data of the device during a large number of normal operation periods, etc.; the average fluctuation coefficient is a measure index of the fluctuation characteristics of the abnormal operation parameter, which reflects the average level of the fluctuation amplitude of these parameters under normal circumstances. For example, some parameters are allowed to have a certain range of fluctuations during device operation, and the average fluctuation coefficient can make the calculated deviation degree value more accurately reflect the true deviation situation and avoid misjudging normal fluctuations as abnormalities.

[0100] S3. Based on the deviation degree value, analyze the possible failure types of the projection device. Based on the failure types, locate the problem components in the projection device, perform a detailed inspection on the problem components to obtain the actual state of the components, and query the specific state points in the actual state of the components.

[0101] Based on the deviation degree value, the present invention analyzes the possible failure types of the projection device, which helps the operation and maintenance personnel quickly judge problems in aspects such as heat dissipation, light source, or circuit, prepare maintenance plans and spare parts in advance, reduce the device downtime. At the same time, by analyzing the correlation between the deviation degree value and the failure types in the long term, the device maintenance strategy can be optimized, and the reliability and service life of the device can be improved.

[0102] Among them, the fault type refers to various problem classifications that may occur during the operation of the projection device, covering hardware faults and software faults, etc., such as bulb faults, cooling fan faults, and color wheel faults in terms of hardware; system crashes, driver faults, signal transmission faults, etc. in terms of software.

[0103] As an embodiment of the present invention, analyzing the fault types that the projection device may have based on the deviation degree value includes: dividing the deviation degree value into intervals to obtain deviation degree intervals; extracting abnormal interval features from the deviation degree intervals; performing pattern matching on the abnormal interval features to obtain abnormal feature patterns; querying the fault matching rules in a preset fault type library based on the abnormal feature patterns; and analyzing the fault types that the projection device may have based on the fault matching rules.

[0104] Among them, the deviation degree interval refers to different range segments obtained by dividing the deviation degree value according to a certain standard. Different intervals represent the differences in the deviation degree of the device operation state from the standard state. For example, the low deviation degree interval indicates that the device operation is basically normal, while the high deviation degree interval means that there are serious problems with the device operation state; the abnormal interval feature refers to the typical characteristics or attributes extracted from the deviation degree interval that can reflect the abnormal situation of the device operation. For example, when the deviation degree value shows a rapid upward trend within a certain interval, or remains in a relatively high numerical range for a long time, etc., these can all be used as abnormal interval features; the abnormal feature pattern refers to a regular manifestation form formed by integrating and summarizing the abnormal interval features. For example, a group of abnormal interval features combined into a specific pattern may represent that a certain component of the device is gradually aging or experiencing a decline in performance. By identifying this pattern, the device fault can be analyzed more accurately; the preset fault type library refers to the collection of fault information that the projection device may have in various scenarios. Its content mainly includes detailed descriptions of different fault types, such as fault phenomena (picture flickering, no image output, etc.), causes (component aging, overheating, software conflict, etc.); the corresponding abnormal feature patterns, that is, the combination of operation parameter deviations and device state manifestations associated with various faults; and solutions for each fault type, including repair steps, recommended replacement parts, software repair methods, etc.; the fault matching rule refers to the criteria and conditions set in advance in the fault type library for judging the device fault type. Each rule corresponds to a specific abnormal feature pattern. When the abnormal feature pattern that appears during the device operation matches a certain rule, the fault type that the device has can be inferred.

[0105] Furthermore, the interval division of the deviation degree value can be achieved through clustering analysis methods, such as the K-Means algorithm, which clusters the deviation degree values and groups similar values into one category to form different intervals. The extraction of the abnormal interval features in the deviation degree interval can be achieved through trend analysis methods, such as observing the change trend of the data in the deviation degree interval over time or other factors, such as an upward trend, a downward trend, or a fluctuating trend, and taking these trends as the abnormal interval features. The pattern matching of the abnormal interval features can be achieved through machine learning matching algorithms, such as using machine learning algorithms, such as support vector machines and neural networks, to learn the preset abnormal feature patterns, and then inputting the extracted abnormal interval features into the trained model, and the model will output the matching abnormal feature patterns. The query of the fault matching rules in the preset fault type library can be achieved through database query methods, such as using the abnormal feature pattern as the query condition and performing a query operation in the database to obtain the corresponding fault matching rules. The analysis of the possible fault types of the projection device can be achieved through rule-based reasoning methods, such as based on the queried fault matching rules, combining the operating principle and relevant knowledge of the projection device, and performing logical reasoning to determine the occurring fault types.

[0106] Based on the fault types, the present invention locates the problem components in the projection device, can quickly prepare the required spare parts, avoid blind troubleshooting, save maintenance time and costs. At the same time, clearly identifying the fault source in advance helps to formulate a more targeted maintenance plan, improve the maintenance quality, ensure that the device resumes normal operation as soon as possible, and reduce the business impact caused by device failures.

[0107] Among them, the problem components refer to the specific hardware modules or components in the projection device that directly cause specific fault types due to aging, damage, performance degradation, or external factor interference, such as the light source component (lamp / laser module), optical component (color wheel / lens), heat dissipation component (fan / heat sink), circuit component (main board / power board), etc. Through fault type analysis, these components can be accurately located and targeted maintenance can be implemented. Optionally, the location of the problem components in the projection device can be achieved through fault tree analysis methods, such as when the projection device has a fault of a blurred image, a fault tree can be constructed, considering the problems that are likely to occur in components such as the lens, imaging chip, and optical correction system, and finally determining the problem components by gradually checking each branch of the fault tree.

[0108] Furthermore, the present invention obtains the actual state of the components through a detailed detection of the problem components, can accurately locate the specific manifestations of component aging, physical damage, or performance decline, provides a direct basis for maintenance decisions, and by analyzing the actual state of the components, it can be determined whether to repair, replace, or optimize parameters, avoiding blind replacement of components and reducing maintenance costs.

[0109] Among them, the actual state of the component refers to the specific situation of the problem component at the current physical, electrical, and performance levels obtained through professional detection means, including: ① Physical integrity (such as whether the component is deformed, whether the solder joints are detached, and whether the insulation layer is aged); ② Key performance parameters (such as the remaining life percentage of the bulb, the deviation rate of the actual rotation speed of the fan from the rated value, and the voltage fluctuation range of the circuit node); ③ Historical fault traces (such as high-temperature burning traces, mechanical wear degree, and oxidation corrosion area); ④ Environmental adaptability indicators (such as the dust filter clogging level, the dust accumulation thickness in the heat dissipation holes, and the failure area of the moisture-proof coating). These state data can be cross-validated with the historical data in the equipment operation log, and finally form a component health record containing quantitative indicators (such as "the color wheel rotation speed decays to 82% of the nominal value") and visual evidence (such as "the C3 capacitor on the power supply board bulges"). Optionally, the detailed detection of the problem component can be achieved through the function test method. For example, for the lens of a projection device, different test patterns can be projected, and the clarity, color restoration degree, focus accuracy, etc. of the image can be observed to evaluate the actual functional state of the lens component.

[0110] Furthermore, by querying the specific state points in the actual state of the component, the root cause of the failure can be accurately located, such as determining the degree of solder joint looseness or component aging, providing a direct basis for maintenance. This helps to reduce blind component replacement, lower maintenance costs, and guide targeted repair.

[0111] Among them, the specific state point refers to the specific state information with clear physical meaning or diagnostic value extracted from the dimensionality-reduced state feature set. For example, after dimensionality reduction processing, it is found that a certain eigenvalue exceeds a certain threshold, and the state corresponding to this eigenvalue can be used as a specific state point. Moreover, the specific state point can directly reflect the actual state of the component, such as whether there is a fault and what working mode it is in.

[0112] As an embodiment of the present invention, the querying of the specific state points in the actual state of the component includes: analyzing the component state indicators corresponding to the actual state of the component; constructing a component index matrix corresponding to the component state indicators; performing dimensionality reduction processing on the component index matrix to obtain a dimensionality-reduced state feature set; and extracting the specific state points in the dimensionality-reduced state feature set.

[0113] Among them, the component status index refers to a series of quantitative parameters or characteristics used to measure and describe the actual status of the component. For example, for the bulb component of the projection equipment, the status indicators may include luminous intensity, color temperature, usage time, light decay rate, etc.; for the cooling fan component, the status indicators can be speed, air volume, noise level, etc.; the component indicator matrix refers to a matrix formed by arranging the various status indicators of the component according to certain rules. For example, when monitoring the bulb components of multiple projection devices, each row of the matrix corresponds to the status data of a bulb at different time points, and the columns correspond to status indicators such as luminous intensity and color temperature respectively; the reduced dimensionality state feature set refers to a new feature set obtained after dimensionality reduction processing of the component indicator matrix. Through dimensionality reduction processing, such as principal component analysis (PCA), linear discriminant analysis (LDA) and other methods, the number of features can be reduced while retaining the main information, and the key features that best represent the component status can be extracted.

[0114] Furthermore, the analysis of the component status indicators corresponding to the actual status of the components can be achieved through machine learning feature engineering, such as: feature screening of the voltage fluctuation data of the power module of the projection equipment, retaining key indicators such as ripple factor and overvoltage protection response time; the construction of the component indicator matrix corresponding to the component status indicators can be achieved through standardization processing, such as: standardizing the fan speed (rpm) and luminous flux (lumens) through Z-score, and constructing a standardized indicator matrix after eliminating unit differences; the dimensionality reduction processing of the component indicator matrix can be achieved through linear discriminant analysis, such as: applying LDA to component status data of known fault types, reducing the original 8-dimensional features to 2 dimensions, and forming a discriminant feature set; the extraction of specific state points in the reduced dimensionality state feature set can be achieved through a machine learning classification model, such as: classifying the features after t-SNE dimensionality reduction based on a decision tree model, and outputting specific state point labels such as "light decay exceeds the standard" and "heat dissipation failure".

[0115] S4. Based on the specific status point, formulate a maintenance planning target corresponding to the projection device, analyze the maintenance measures in the maintenance planning target, query the maintenance standards corresponding to the maintenance measures, and construct a remote maintenance instruction corresponding to the projection device based on the maintenance standards.

[0116] Based on the specific status points, the present invention formulates maintenance planning goals corresponding to the projection equipment, and can accurately locate components and time nodes that require key maintenance. For example, light bulbs can be replaced in advance at status points where light decay exceeds the standard to avoid sudden failures. In addition, combined with status point trend analysis, the present invention can also predict the remaining life of components, formulate preventive maintenance strategies, extend the overall service life of the equipment, and ensure continuous and stable operation.

[0117] Among them, the maintenance planning objective refers to the equipment full-life cycle maintenance plan formulated based on the comprehensive maintenance requirement set, priority, and process. For example, based on the status point of "excessive light decay of the bulb", the planning objectives may include: ① replacing the bulb within 3 days; ② shortening the bulb replacement cycle from 2000 hours to 1800 hours; ③ establishing a light decay early warning mechanism (threshold set at 85%). The objectives need to clarify the time node, responsible person, and acceptance criteria, and track the execution progress through a project management tool (such as Jira).

[0118] As an embodiment of the present invention, formulating the maintenance planning objective corresponding to the projection device based on the specific status point includes: analyzing the status impact factors corresponding to the specific status point; based on the status impact factors, dividing the maintenance requirements of the projection device to obtain a maintenance requirement set; querying the requirement priorities corresponding to the requirements in the maintenance requirement set; based on the requirement priorities, formulating the requirement maintenance processes corresponding to the requirements in the maintenance requirement set; and based on the requirement maintenance processes, formulating the maintenance planning objective corresponding to the projection device.

[0119] Among them, the status impact factors refer to the physical, environmental, or operating parameters directly related to the specific status point. Changes in these factors will cause the component status to deviate from normal. For example, the light decay rate of the projection device bulb is directly related to the working duration and the heat dissipation system efficiency, and the rotational speed fluctuation of the heat dissipation fan may be affected by the dust accumulation amount and the bearing wear degree. By analyzing these factors, the cause of the specific status point can be traced. For example, for the status point of "light flux decays to 75% of the nominal value", its impact factors may include the cumulative bulb usage duration (1200 hours), environmental temperature (40°C), etc.; the maintenance requirement set refers to the classification of maintenance tasks divided based on the status impact factors, covering maintenance requirements from component level to system level. For example, when detecting the status point of "the rotational speed of the heat dissipation fan drops to 80% of the rated value", the maintenance requirement set may include: ① cleaning the dust on the fan blades; ② replacing the aging bearings; ③ optimizing the layout of the heat dissipation holes. Each requirement corresponds to a specific maintenance operation. For example, "cleaning the fan blades" needs to be completed within 48 hours, and "replacing the bearings" requires reserving spare parts, etc.; the requirement priority refers to the priority level set according to the impact degree of the maintenance requirement on the equipment operation safety and performance. For example, the maintenance requirement priority corresponding to the status point of "capacitor bulge of the power module" is the highest (P0), and immediate shutdown and replacement are required; while the priority of the status point of "slight dust on the lens" is the lowest (P3), which can be included in the monthly maintenance plan; the requirement maintenance process refers to the standardized operation steps designed for each maintenance requirement, including tool usage, technical parameters, safety specifications, etc. For example, the process of "replacing the bulb" includes: ① powering off and cooling the equipment; ② removing the old bulb; ③ installing the new bulb and calibrating the optical axis; ④ testing whether the light flux meets the standard. The process needs to be solidified in the form of a standard operating procedure (SOP).

[0120] Furthermore, the analysis of the state impact factors corresponding to the specific state points can be achieved through the correlation analysis method. For example, by using the pandas library and scipy library in Python, calculate the correlation coefficients between specific state points (such as the brightness decline of the projection device bulb) and parameters such as ambient temperature and usage duration. If the correlation coefficient is high, it can be used as a state impact factor. The division of the maintenance requirements for the projection device can be achieved through the rule matching method. For example, if the specific state point is that the temperature of the projection device is too high, according to the preset rules, the maintenance requirements can be divided into checking the cooling system, cleaning the cooling channel, etc. The formulation of the demand maintenance process corresponding to the requirements in the maintenance requirement set can be achieved through the business impact analysis method. For example, for a projection device used in an important meeting, if a certain maintenance requirement will cause the device to be unable to be used normally during the meeting, then the priority of this requirement is high. The formulation of the demand maintenance process corresponding to the requirements in the maintenance requirement set can be achieved through the standardized process template method. For example, for the maintenance requirement of replacing parts of the projection device, on the basis of the general part replacement process template, it can be modified according to the characteristics and installation requirements of the specific parts to obtain the demand maintenance process. The formulation of the maintenance planning goal corresponding to the projection device can be achieved through the project management tool method. For example, create a projection device maintenance project in Microsoft Project, take each maintenance requirement as a project task, set the priority and dependency relationship of the task, and finally generate the maintenance planning goal.

[0121] By analyzing the maintenance measures in the maintenance planning goal of the present invention and querying the maintenance standards corresponding to the maintenance measures, it is possible to verify the rationality of the measures against the standards (such as whether the technical parameters of replacing the bulb meet the manufacturer's specifications), which can avoid secondary failures caused by non-standardized operations. At the same time, the best practices in the maintenance standards can guide the optimization of the maintenance process (such as shortening the cleaning cycle of the cooling module), improving efficiency and reducing costs.

[0122] Among them, the maintenance measure refers to the targeted action plan taken to solve the problem of the specific state point of the projection device, including operation steps, tool usage, and resource allocation. For example, for the state point of "excessive light decay of the bulb", the maintenance measures can include: ① purchasing original factory bulb spare parts; ② shutting down the machine to replace the bulb; ③ calibrating the light output parameters of the new bulb; ④ updating the device maintenance log. The maintenance standard refers to the maintenance operation guidelines formulated by the device manufacturer, industry organization, or technical specification, covering technical parameters, safety requirements, and quality acceptance conditions. For example, the projection device bulb replacement standard may stipulate: ① using a bulb that meets the ISO19798 standard; ② the luminous flux after replacement should be ≥ 95% of the nominal value; ③ following the ESD protection specification during the operation process.

[0123] ④During acceptance, the color temperature deviation needs to be recorded as <±500K>. Optionally, the maintenance measures in the analyzed maintenance planning objectives can be achieved through cost-benefit analysis. For example, for the lens cleaning and maintenance of a projection device, by analyzing the costs and cleaning effects of different cleaning methods (such as manual cleaning and using cleaning equipment), the most cost-effective maintenance measure is determined. The query of the maintenance standards corresponding to the maintenance measures can be achieved through document retrieval. For example, in the technical manual provided by the manufacturer of the projection device, find the standards for the maintenance of the heat dissipation system, including the rotation speed requirements and cleaning cycles of the heat dissipation fans.

[0124] Based on the maintenance standards, the present invention constructs remote maintenance instructions corresponding to the projection device, which can automatically trigger a preset maintenance process (such as heat dissipation system cleaning and firmware upgrade) through remote instructions, reduce the cost of manual intervention, shorten the equipment downtime, avoid the spread of faults. At the same time, the instruction execution data can feed back to optimize the maintenance standards, form a closed-loop management, and improve the intelligent level of the full life cycle management of the equipment.

[0125] Among them, the remote maintenance instruction refers to a digital operation instruction remotely sent to the device through the network, which is used to guide the device to automatically execute or assist manual completion of maintenance tasks. These instructions include the maintenance operation type (such as replacing parts and calibrating parameters), specific execution parameters (such as component models and technical indicators), operation sequence (such as power off first and then repair), and verification criteria for task completion (such as performance parameter compliance values).

[0126] As an embodiment of the present invention, the construction of the remote maintenance instruction corresponding to the projection device based on the maintenance standards includes: analyzing the maintenance levels corresponding to different maintenance tasks under the maintenance standards; identifying the core components and auxiliary components corresponding to the projection device based on the maintenance levels; determining the component operation sequence corresponding to the core components and auxiliary components; formulating the detailed execution logic corresponding to the projection device based on the component operation sequence; and constructing the remote maintenance instruction corresponding to the projection device based on the detailed execution logic.

[0127] Among them, the maintenance level refers to the priority level divided according to the impact degree and urgency of maintenance tasks on the operation safety and performance of the equipment. For example, by evaluating the tasks in the maintenance standard, "replacing the power module capacitor" is set as the highest level (L1) and needs to be executed immediately; "calibrating the color wheel parameters" is set as the intermediate level (L2) and can be completed within 48 hours; "cleaning the lens surface" is set as the low level (L3) and is included in the monthly maintenance plan. The core components refer to the key components that play a decisive role in the realization of the functions of the projection equipment, and their failure will directly lead to equipment shutdown or loss of core functions. For example, through Fault Tree Analysis (FTA), it is determined that the light source system (lamp / laser module), imaging system (DMD chip / liquid crystal panel), and heat dissipation system (fan / heat pipe) are core components. The auxiliary components refer to the peripheral components that support the operation of the core components, and their failures will affect the equipment performance but will not immediately cause shutdown. For example, through Reliability Block Diagram (RBD) analysis, it is determined that the signal interface board, dust filter, and control panel are auxiliary components. The component operation sequence refers to the sequence of component operations specified to ensure safety and effectiveness during the maintenance process. For example, the maintenance process is defined using a Finite State Machine (FSM) model: ① Turn off the power and release the residual charge (safe state); ② Remove the outer cover (enter the maintenance state); ③ Replace the core components (execution state); ④ Test the functions of the auxiliary components (verification state); ⑤ Restore the equipment to the operating state. The sequence is controlled by state transition conditions (such as "voltage zero" triggering the next step). The detailed execution logic refers to the maintenance operation sequence that includes conditional judgment, parameter verification, and safety rules. For example, the logic is designed based on the BPMN flowchart tool: when the maintenance level is L1 and the core component is the power module, the logic branch is: ① Check whether the capacitor model matches (Y / N); ② If it matches, perform the welding operation, otherwise trigger the spare part application process; ③ After welding, measure whether the output voltage is within the range of 12V ± 5%, otherwise return for retry. The logic nodes are driven by predefined rules (such as "voltage exceeding the limit requires recalibration").

[0128] Further, the maintenance levels corresponding to different maintenance tasks under the maintenance standard can be achieved through the analytic hierarchy process. For example, the various influencing factors of the maintenance tasks are constructed into a hierarchical structure model. By comparing the relative importance of each factor, the comprehensive score of each maintenance task is calculated, and then the maintenance level is determined. The identification of the core components and auxiliary components corresponding to the projection device can be achieved through functional analysis. For example, by analyzing the various functions of the projection device, the components necessary to implement the core functions are determined as the core components, while the components that support the operation of the core components or provide additional functions are the auxiliary components. The determination of the component operation sequence corresponding to the core components and auxiliary components can be achieved through the state machine model. For example, a finite state machine can divide the states of the components of the projection device into on, running, off, etc. By defining the state transition rules, the components are determined to start and stop in a certain order. The formulation of the detailed execution logic corresponding to the projection device can be achieved through a rule engine. For example, a series of rules are defined, and they are matched according to the input conditions and the rules in the rule library to determine the detailed execution logic. The construction of the remote maintenance instructions corresponding to the projection device can be achieved through data encoding. For example, information such as the maintenance task type, component number, operation parameters, etc. is encoded according to a certain encoding rule and then transmitted to the projection device through the network.

[0129] S5. After sending the remote maintenance instructions to a preset cloud monitoring platform, perform a remote maintenance operation on the projection device to obtain a device maintenance record, acquire the maintenance feedback data in the device maintenance record, and perform validity verification on the maintenance feedback data to obtain valid verification data. Based on the valid verification data, generate a fault diagnosis and maintenance report corresponding to the projection device.

[0130] After the present invention sends the remote maintenance instructions to a preset cloud monitoring platform and performs a remote maintenance operation on the projection device to obtain a device maintenance record, it can automatically analyze the maintenance effect (such as the light flux recovery rate) to provide data support for optimizing the maintenance strategy. At the same time, by associating with historical fault data, it can predict the fault cycle of similar components (such as the life model of the cooling fan), changing passive maintenance to active prevention.

[0131] Among them, the preset cloud monitoring platform refers to a centralized management system pre-deployed in the cloud, which is used to receive, store, and process the real-time status data and remote maintenance instructions of the projection device. This platform communicates with the device through a standardized interface (such as the MQTT protocol) and has functions such as multi-device management, instruction queue scheduling, and fault warning. For example, the platform can automatically parse the parameters in the remote maintenance instructions (such as the bulb model carried by the REPLACE_LAMP task) and push the instructions to the target device through a secure channel; the device maintenance record refers to the digital log generated after the device executes the remote maintenance operation, including information such as the operation timestamp, instruction code, component change details, and verification results. For example, the record of replacing the bulb will include: ① execution time; ② instruction parameters (new bulb serial number B20250304-01); ③ key parameter changes (luminous flux restored from 1800 lumens to 2200 lumens); ④ verification result (pass the light decay test, color temperature deviation +300K). The record is stored in a cloud database (such as MySQL) in a structured form and supports retrieval and analysis according to dimensions such as device ID and maintenance type. Optionally, the remote maintenance operation on the projection device can be implemented through a protocol instruction interaction method. For example, control instructions such as restarting the device and adjusting the brightness can be sent to the device through a serial port debugging tool (such as SecureCRT). Before and after sending the instructions, record the instruction content, sending time, device response, etc., so as to obtain the device maintenance record.

[0132] Furthermore, the present invention can quickly identify abnormalities (such as inconsistent spare part models) during the maintenance process and avoid the recurrence of unexpected failures by obtaining the maintenance feedback data in the device maintenance record and validating the effectiveness of the maintenance feedback data to obtain valid verification data. The valid verification data can also be used as training samples for a machine learning model to optimize the maintenance strategy (such as dynamically adjusting the cleaning cycle) and improve the accuracy of preventive maintenance.

[0133] Among them, the maintenance feedback data refers to the status information returned through sensors, log systems or manual input after the projection device executes the remote maintenance instruction. For example, after replacing the lamp, the device automatically reports parameters such as the luminous flux value (2200 lumens), color temperature (6500K), and operating duration (0 hours) of the new lamp, as well as the status code of operation success / failure (such as 0x00 indicating success); the valid verification data refers to the reliable data that meets the expectations after screening, comparison and verification of the maintenance feedback data through preset rules or algorithms. For example, comparing the luminous flux of 2200 lumens with the maintenance standard (≥2000 lumens), the verification result is "qualified"; if the fan speed feedback is 1800 rpm but the actual measurement is 1500 rpm, the system automatically marks this data as "abnormal" and triggers secondary verification. Optionally, the acquisition of the maintenance feedback data in the device maintenance record can be achieved through a log parsing tool. For example: using the ELK tool, collecting the log files of the projection device, parsing and filtering them, and extracting the log content related to the maintenance operation, such as maintenance time, operation type, device status change, etc., and finally obtaining the maintenance feedback data; the validity verification of the maintenance feedback data can be achieved through rule engine verification. For example: for the brightness feedback data of the projection device, it is stipulated that the brightness value should be within a certain range (such as 1000-3000 lumens). When receiving the brightness feedback data, the rule engine checks whether the data is within this range. If it is, it is considered valid data.

[0134] Furthermore, based on the valid verification data, the present invention generates a fault diagnosis and maintenance report for the projection device, which can accurately locate the root cause of the device abnormality, provide a quantitative basis for the maintenance decision, and the report predicts potential faults through trend analysis, guides preventive maintenance, and reduces the risk of sudden shutdown.

[0135] Among them, the fault diagnosis and maintenance report refers to a comprehensive document generated by analyzing the device operation data and maintenance records, including the location description of the device abnormal state (such as "the performance of the light source module has declined"), the analysis of the fault cause (such as "the insufficient efficiency of the heat dissipation system leads to the aging of the core components"), the evaluation of the maintenance operation effect (such as "the device operation resumes stability after replacing the relevant components"), and the trend prediction based on historical data (such as "predicting that a certain type of component will enter the decline cycle"). The report is presented in the form of visual charts (such as the performance fluctuation trend chart), parameter comparison tables (such as the change of key indicators before and after maintenance), and maintenance suggestions (such as "it is recommended to regularly check the heat dissipation system"). Optionally, the generation of the fault diagnosis and maintenance report for the projection device can be achieved through a report generation tool. For example: tools such as JasperReports and BIRT.

[0136] Compared with the problems described in the background art, the present invention can realize real-time monitoring and precise positioning of the device operating state by obtaining the device operating parameters corresponding to the projection device and collecting the device identification codes corresponding to the device operating parameters, providing a data traceability basis for subsequent fault diagnosis. At the same time, by combining historical data, it can achieve rapid identification of abnormal parameters and early warning of potential faults, effectively improving the remote maintenance efficiency and reducing the manual inspection cost. The present invention obtains associated fault data by performing an association process on the operation verification record and the historical fault data of the projection device, and can mine potential fault modes through multi-dimensional data comparison, realizing precise attribution and trend prediction of device anomalies, being able to quickly locate the corresponding relationship between repeatedly occurring parameter deviations and historical faults, improving the accuracy and timeliness of fault diagnosis. Further, based on the deviation degree value, the present invention analyzes the possible fault types of the projection device, which helps the operation and maintenance personnel quickly determine whether the problem is related to heat dissipation, light source, circuit, etc., prepare the repair plan and spare parts in advance, reduce the device downtime. At the same time, by long-term analyzing the association between the deviation degree value and the fault type, the device maintenance strategy can be optimized, improving the reliability and service life of the device. Further, based on the specific state point, the present invention formulates the maintenance planning goal corresponding to the projection device, which can precisely locate the components and time nodes that need key maintenance. For example, replace the lamp in advance for the state point where the light decay exceeds the standard to avoid sudden faults. And by combining the trend analysis of the state point, it can also predict the remaining life of the components, formulate preventive maintenance strategies, extend the overall service life of the device, and ensure continuous and stable operation. Finally, after the present invention sends the remote maintenance instruction to the preset cloud monitoring platform, it performs remote maintenance operations on the projection device to obtain device maintenance records, and can automatically analyze the maintenance effect (such as the light flux recovery rate), providing data support for optimizing the maintenance strategy; at the same time, by associating historical fault data, it can predict the fault cycle of similar components (such as the life model of the cooling fan), changing passive maintenance to active prevention. Therefore, a projection device remote maintenance and fault diagnosis method and system provided by the embodiments of the present invention can improve the efficiency of projection device maintenance and fault handling.

[0137] Embodiment 2:

[0138] As Figure 2 shown, it is a functional module diagram of a projection device remote maintenance and fault diagnosis system of the present invention.

[0139] The remote maintenance and fault diagnosis system 200 of a projection device according to the present invention can be installed in an electronic device. According to the implemented functions, the remote maintenance and fault diagnosis system of a projection device may include a verification processing module 201, a deviation calculation module 202, a status point query module 203, an instruction construction module 204, and a report generation module 205. The modules in the present invention may also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0140] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0141] The verification processing module 201 is configured to obtain the device operation parameters corresponding to the projection device, collect the device identification code corresponding to the device operation parameters, and perform verification processing on the device operation parameters based on the device identification code to obtain an operation verification record;

[0142] The deviation calculation module 202 is configured to perform correlation processing on the operation verification record and the historical fault data of the projection device to obtain associated fault data, query the fault operation traces in the associated fault data, filter out the abnormal operation parameters in the fault operation traces, and monitor the real-time operation state corresponding to the projection device in real time, and calculate the deviation degree value corresponding to the abnormal operation parameters and the real-time operation state;

[0143] The status point query module 203 is configured to analyze the possible fault types of the projection device based on the deviation degree value, locate the problem components in the projection device based on the fault types, perform detailed detection on the problem components to obtain the actual state of the components, and query the specific status points in the actual state of the components;

[0144] The instruction construction module 204 is configured to formulate the maintenance planning goal corresponding to the projection device based on the specific status points, analyze the maintenance measures in the maintenance planning goal, query the maintenance standards corresponding to the maintenance measures, and construct the remote maintenance instruction corresponding to the projection device based on the maintenance standards;

[0145] The report generation module 205 is configured to send the remote maintenance instruction to a preset cloud monitoring platform, perform a remote maintenance operation on the projection device to obtain a device maintenance record, obtain the maintenance feedback data in the device maintenance record, perform validity verification on the maintenance feedback data to obtain valid verification data, and generate a fault diagnosis and maintenance report corresponding to the projection device based on the valid verification data.

[0146] Specifically, when the modules in the remote maintenance and fault diagnosis system 200 of a projection device in the embodiments of the present invention are used, they adopt the same technical means as those in the Figure 1 remote maintenance and fault diagnosis method of a projection device described therein, and can produce the same technical effects, which will not be elaborated here.

[0147] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for remote maintenance and fault diagnosis of a projection device, characterized in that, The method includes: Obtaining the device operation parameters corresponding to the projection device, collecting the device identification code corresponding to the device operation parameters, and performing a verification process on the device operation parameters based on the device identification code to obtain an operation verification record; Performing an association process on the operation verification record and the historical fault data of the projection device to obtain associated fault data, querying the fault operation traces in the associated fault data, screening the abnormal operation parameters in the fault operation traces, and real-time monitoring the real-time operation state corresponding to the projection device, and calculating the deviation degree value corresponding to the abnormal operation parameters and the real-time operation state; Analyzing the possible fault types of the projection device based on the deviation degree value, locating the problem components in the projection device based on the fault types, performing a detailed detection on the problem components to obtain the actual state of the components, and querying the specific state points in the actual state of the components; Formulating the maintenance planning objective corresponding to the projection device based on the specific state points, analyzing the maintenance measures in the maintenance planning objective, and querying the maintenance standards corresponding to the maintenance measures, and constructing the remote maintenance instruction corresponding to the projection device based on the maintenance standards; After sending the remote maintenance instruction to a preset cloud monitoring platform, performing a remote maintenance operation on the projection device to obtain a device maintenance record, obtaining the maintenance feedback data in the device maintenance record, and performing a validity verification on the maintenance feedback data to obtain valid verification data, and generating a fault diagnosis and maintenance report corresponding to the projection device based on the valid verification data.

2. The remote maintenance and fault diagnosis method of a projection device according to claim 1, characterized in that, The performing a verification process on the device operation parameters based on the device identification code to obtain an operation verification record includes: Analyzing the device operation type corresponding to the device identification code; Querying the standard operation range corresponding to the device operation type; Extracting the key operation indicators in the standard operation range; Constructing an operation state matrix corresponding to the device operation parameters based on the key operation indicators; Performing a state point detection on the operation state matrix to obtain an operation state point; Performing a verification process on the device operation parameters based on the operation state point to obtain an operation verification record.

3. A method for remote maintenance and fault diagnosis of a projection device according to claim 1, characterized in that, The performing an association process on the operation verification record and the historical fault data of the projection device to obtain associated fault data includes: Extracting the abnormal verification parameters in the operation verification record; Matching the historical fault data of the projection device based on the abnormal verification parameters to obtain preliminary matching features; Analyzing the feature distribution state corresponding to the preliminary matching features; Calculating the feature association value between the preliminary matching features based on the feature distribution state; Screening the highly associated fault data in the historical fault data based on the feature association value; Performing a normalization process on the highly associated fault data to obtain associated fault data.

4. The remote maintenance and fault diagnosis method of a projection device according to claim 3, characterized in that The calculating the feature association value between the preliminary matching features based on the feature distribution state includes: Calculating the feature association value between the preliminary matching features by using the following formula: Among them, Fl represents the feature correlation value between the preliminary matching features, n represents the number of the preliminary matching features, V i represents the distribution state value corresponding to the i-th preliminary matching feature, represents the average distribution state value corresponding to the preliminary matching feature, S V represents the standard deviation corresponding to the distribution state value, W i represents the feature weight value corresponding to the i-th preliminary matching feature, M represents the covariance matrix corresponding to the preliminary matching feature, and det(M) represents the determinant of the matrix M.

5. The remote maintenance and fault diagnosis method of a projection device according to claim 1, characterized in that The calculating the deviation degree value corresponding to the abnormal operation parameters and the real-time operation state includes: Calculate the deviation degree value corresponding to the abnormal operation parameter and the real-time operation state by using the following formula: Among them, DP represents the deviation degree value corresponding to the abnormal operation parameter and the real-time operation state, m represents the number of parameters corresponding to the abnormal operation parameter, j represents the quantity index corresponding to the abnormal operation parameter, A j represents the actual measured value corresponding to the j-th abnormal operation parameter, R j represents the parameter reference value corresponding to the j-th abnormal operation parameter for the real-time operation state, and W represents the average fluctuation coefficient corresponding to the abnormal operation parameter.

6. A method for remote maintenance and fault diagnosis of a projection device according to claim 1, characterized in that, Based on the deviation degree value, analyze the possible fault types of the projection device, including: Divide the deviation degree value into intervals to obtain a deviation degree interval; Extract the abnormal interval features in the deviation degree interval; Perform pattern matching on the abnormal interval features to obtain an abnormal feature pattern; Based on the abnormal feature pattern, query the fault matching rules in the preset fault type library; Based on the fault matching rules, analyze the possible fault types of the projection device.

7. A method for remote maintenance and fault diagnosis of a projection device according to claim 1, characterized in that The query of the specific state points in the actual state of the component includes: Analyze the component state indicators corresponding to the actual state of the component; Construct a component index matrix corresponding to the component state indicators; Perform dimensionality reduction processing on the component index matrix to obtain a dimensionality reduction state feature set; Extract the specific state points in the dimensionality reduction state feature set.

8. A method for remote maintenance and fault diagnosis of a projection device according to claim 1, characterized in that Based on the specific state points, formulate the maintenance planning objectives corresponding to the projection device, including: Analyze the state influence factors corresponding to the specific state points; Based on the state influence factors, divide the maintenance requirements of the projection device to obtain a set of maintenance requirements; Query the requirement priority levels corresponding to the requirements in the set of maintenance requirements; Based on the requirement priority levels, formulate the requirement maintenance processes corresponding to the requirements in the set of maintenance requirements; Based on the requirement maintenance processes, formulate the maintenance planning objectives corresponding to the projection device.

9. A method for remote maintenance and fault diagnosis of a projection device according to claim 1, characterized in that Based on the maintenance standards, construct the remote maintenance instructions corresponding to the projection device, including: Analyze the maintenance levels corresponding to different maintenance tasks under the maintenance standards; Based on the maintenance levels, identify the core components and auxiliary components corresponding to the projection device; Determine the component operation sequence corresponding to the core components and auxiliary components; Based on the component operation sequence, formulate the detailed execution logic corresponding to the projection device; Based on the detailed execution logic, construct the remote maintenance instructions corresponding to the projection device.

10. A remote maintenance and fault diagnosis system for a projection device, characterized in that, The system includes: A verification processing module, configured to obtain the device operation parameters corresponding to the projection device, collect the device identification codes corresponding to the device operation parameters, and perform verification processing on the device operation parameters based on the device identification codes to obtain an operation verification record; A deviation calculation module, configured to perform correlation processing on the operation verification record and the historical fault data of the projection device to obtain associated fault data, query the fault operation traces in the associated fault data, filter out the abnormal operation parameters in the fault operation traces, and real-time monitor the real-time operation state corresponding to the projection device, and calculate the deviation degree value corresponding to the abnormal operation parameter and the real-time operation state; A state point query module, configured to analyze the possible fault types of the projection device based on the deviation degree value, locate the problem components in the projection device based on the fault types, perform detailed detection on the problem components to obtain the actual state of the components, and query the specific state points in the actual state of the components; An instruction construction module, configured to formulate a maintenance planning objective corresponding to the projection device based on the specific state points, analyze the maintenance measures in the maintenance planning objective, query the maintenance standards corresponding to the maintenance measures, and construct a remote maintenance instruction corresponding to the projection device based on the maintenance standards; A report generation module, configured to, after sending the remote maintenance instruction to a preset cloud monitoring platform, perform a remote maintenance operation on the projection device to obtain a device maintenance record, acquire maintenance feedback data in the device maintenance record, perform validity verification on the maintenance feedback data to obtain valid verification data, and generate a fault diagnosis and maintenance report corresponding to the projection device based on the valid verification data.

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