Whole-process electric appliance equipment extended protection data tracing management platform and method

Abstract

The invention provides a full-process electrical equipment extended guarantee data tracing management platform and method, and relates to the technical field of data tracing, and the platform comprises a extended guarantee time limit obtaining module which is used for obtaining a first electrical equipment extended guarantee time limit; the in-time inspection module is used for obtaining a first electric appliance protection delay in-time inspection result; the real-time parameter acquisition module is used for establishing a first electric appliance state matrix; the health assessment module is used for obtaining a first electric appliance health coefficient; the maintenance record acquisition module is used for acquiring a first electric appliance maintenance record set; and the report sending module is used for encrypting and sending the first electric appliance protection delay suggestion report to the user. The technical problem that the health condition and the maintenance record information of the equipment cannot be obtained in time in the protection delay management of the existing electrical equipment, so that the protection delay suggestion is not accurate is solved, the personalized protection delay suggestion report is generated by collecting the equipment parameters and the health assessment in real time, the accuracy and the effectiveness of the protection delay service are improved, and the safety of the electrical equipment is improved. And the service life of the equipment is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of data traceability, and particularly relates to a full-process electrical equipment extended warranty data traceability management platform and method. Background Art

[0002] With the continuous increase in the types of household appliances and electrical equipment and the improvement of their intelligence level, the functional complexity and usage frequency of electrical equipment in daily use have also increased accordingly. This development trend not only has significantly increased the maintenance requirements of electrical equipment but also promoted the growth of the demand for extended warranty services. However, in the existing electrical equipment extended warranty service system, there are generally problems such as information isolation, inconsistent management, inaccurate assessment of extended warranty service content and equipment health status, etc., resulting in the difficulty of accurately meeting the actual needs of consumers for extended warranty services. Moreover, it may even increase the maintenance cost of the equipment due to improper or omitted extended warranty services, affecting the user experience and the service life of the equipment. Traditional electrical equipment extended warranty services mainly rely on standardized extended warranty packages, often lacking personalized suggestions for the specific health status of the equipment. And after a failure occurs, the correlation analysis between the repair history and the current state of the equipment is insufficient, resulting in an incomplete assessment of the equipment health and affecting the optimization of subsequent maintenance and extended warranty strategies. In addition, information such as the repair records, extended warranty time limits, and usage status of electrical equipment is often scattered in different platforms or systems, lacking an effective data traceability mechanism, making it difficult to provide comprehensive and accurate health assessments and extended warranty service optimization suggestions during the extended warranty period of the equipment. Summary of the Invention

[0003] The present application provides a full-process electrical equipment extended warranty data traceability management platform and method, aiming to solve the technical problem that in the existing electrical equipment extended warranty management, it is impossible to timely obtain the equipment health status and repair record information, resulting in inaccurate extended warranty suggestions.

[0004] In the first aspect disclosed in this application, a full-process electrical equipment extended warranty data traceability management platform is provided. The platform includes: an extended warranty time limit obtaining module for obtaining the first electrical equipment extended warranty time limit corresponding to the first electrical equipment; a near-term inspection module for inputting the first electrical equipment extended warranty time limit into an electrical equipment extended warranty near-term inspector to obtain a first electrical equipment extended warranty near-term inspection result; a real-time parameter acquisition module for collecting the real-time parameters of the first electrical equipment and establishing a first electrical equipment status matrix when the first electrical equipment extended warranty near-term inspection result is unqualified; a health assessment module for performing a health assessment on the first electrical equipment according to the first electrical equipment status matrix to obtain a first electrical equipment health coefficient; a maintenance record acquisition module for collecting the maintenance record information of the first electrical equipment within the first electrical equipment extended warranty time limit to obtain a first electrical equipment maintenance record set if the first electrical equipment health coefficient is greater than or equal to the electrical equipment health threshold; and a report sending module for adaptively optimizing the electrical equipment extended warranty package pool according to the first electrical equipment health coefficient and the first electrical equipment maintenance record set, generating a first electrical equipment extended warranty recommendation report, and encrypting and sending the first electrical equipment extended warranty recommendation report to the user of the first electrical equipment.

[0005] In the second aspect disclosed in this application, a full-process electrical equipment extended warranty data traceability management method is provided. The method is implemented through the above-mentioned full-process electrical equipment extended warranty data traceability management platform. The method includes: obtaining the first electrical equipment extended warranty time limit corresponding to the first electrical equipment; inputting the first electrical equipment extended warranty time limit into an electrical equipment extended warranty near-term inspector to obtain a first electrical equipment extended warranty near-term inspection result; collecting the real-time parameters of the first electrical equipment and establishing a first electrical equipment status matrix when the first electrical equipment extended warranty near-term inspection result is unqualified; performing a health assessment on the first electrical equipment according to the first electrical equipment status matrix to obtain a first electrical equipment health coefficient; collecting the maintenance record information of the first electrical equipment within the first electrical equipment extended warranty time limit to obtain a first electrical equipment maintenance record set if the first electrical equipment health coefficient is greater than or equal to the electrical equipment health threshold; adaptively optimizing the electrical equipment extended warranty package pool according to the first electrical equipment health coefficient and the first electrical equipment maintenance record set, generating a first electrical equipment extended warranty recommendation report, and encrypting and sending the first electrical equipment extended warranty recommendation report to the user of the first electrical equipment.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The method obtains a first electrical appliance extended warranty time limit corresponding to a first electrical appliance device based on an extended warranty time limit obtaining module; inputs the first electrical appliance extended warranty time limit into an electrical appliance extended warranty approaching inspection device based on an approaching inspection module to obtain a first electrical appliance extended warranty approaching inspection result; when the first electrical appliance extended warranty approaching inspection result is unqualified, collects real-time parameters of the first electrical appliance device based on a real-time parameter collection module, and establishes a first electrical appliance state matrix; conducts a health assessment on the first electrical appliance device according to the first electrical appliance state matrix based on a health assessment module to obtain a first electrical appliance health coefficient; if the first electrical appliance health coefficient is greater than or equal to an electrical appliance health threshold, collects maintenance record information of the first electrical appliance device within the first electrical appliance extended warranty time limit based on a maintenance record collection module to obtain a first electrical appliance maintenance record set; adaptively optimizes an electrical appliance extended warranty package pool according to the first electrical appliance health coefficient and the first electrical appliance maintenance record set based on a report sending module, generates a first electrical appliance extended warranty recommendation report, and encrypts and sends the first electrical appliance extended warranty recommendation report to the user of the first electrical appliance device; thereby solving the technical problem in the existing electrical appliance device extended warranty management that the health status and maintenance record information of the device cannot be obtained in a timely manner, resulting in inaccurate extended warranty recommendations, achieving the technical effects of improving the accuracy and effectiveness of extended warranty services and extending the service life of the device.

[0008] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically given below. Brief Description of the Drawings

[0009] Figure 1 It is a schematic structural diagram of a full-process electrical appliance device extended warranty data traceability management platform provided by an embodiment of the present application.

[0010] Figure 2 It is a schematic flow diagram of a full-process electrical appliance device extended warranty data traceability management method provided by an embodiment of the present application.

[0011] Description of the reference numerals: Extended warranty time limit obtaining module 11, approaching inspection module 12, real-time parameter collection module 13, health assessment module 14, maintenance record collection module 15, report sending module 16. Detailed Description of the Embodiments

[0012] By providing a full-process electrical appliance device extended warranty data traceability management platform and method in an embodiment of the present application, the technical problem in the existing electrical appliance device extended warranty management that the health status and maintenance record information of the device cannot be obtained in a timely manner, resulting in inaccurate extended warranty recommendations, is solved.

[0013] After introducing the basic principles of the present application, various non-limiting embodiments of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] Embodiment 1, as Figure 1 shown, the embodiment of the present application provides a full-process electrical equipment extended warranty data traceability management platform, and the platform includes:

[0015] An extended warranty time limit obtaining module, configured to obtain a first electrical equipment extended warranty time limit corresponding to a first electrical equipment.

[0016] Specifically, in the extended warranty time limit obtaining module, according to the type, model, and purchase date of the electrical equipment purchased by the user, the extended warranty time limit of the equipment is extracted from the registration information or purchase record of the equipment. The extended warranty time limit is usually provided by the manufacturer or the seller, and is usually based on factors such as the equipment's warranty policy, the extended warranty package selected by the user, and the usage situation of the equipment. This extended warranty time limit refers to the time period during which the manufacturer or the seller promises to provide free or preferential repair services under normal usage conditions of the electrical equipment. By recording this time limit, it can be used as the basis for subsequent health assessment, approaching expiration inspection, extended warranty package optimization, etc., to ensure that the extended warranty service can cover the health cycle of the equipment.

[0017] An approaching expiration inspection module, configured to input the first electrical equipment extended warranty time limit into an electrical equipment extended warranty approaching expiration inspector to obtain a first electrical equipment extended warranty approaching expiration inspection result.

[0018] Specifically, in the approaching expiration inspection module, after obtaining the extended warranty time limit information of the first electrical equipment, it is input into the electrical equipment extended warranty approaching expiration inspector. The inspector calculates the corresponding extended warranty approaching expiration parameter according to the difference between the extended warranty time limit and the current date, and then according to the preset approaching expiration threshold, the inspector will evaluate whether the electrical equipment is about to enter the approaching state of the extended warranty period. If the calculation result shows that the extended warranty time limit is too close to the current time or has already approached expiration, the inspector will mark the first electrical equipment extended warranty approaching expiration inspection result of the equipment as unqualified. On the contrary, if the extended warranty time limit is still sufficient, the inspector will mark it as qualified. This first electrical equipment extended warranty approaching expiration inspection result will be used as the basis for subsequent health assessment and repair decision-making to ensure that the extended warranty service can cover the health cycle of the equipment.

[0019] Furthermore, the approaching expiration inspection module includes:

[0020] Expiring Parameter Calculation Module: Calculate the expiring parameter of the first electrical appliance's extended warranty according to the first electrical appliance's extended warranty time limit; Expiring Parameter Input Module: Input the expiring parameter of the first electrical appliance's extended warranty into the electrical appliance extended warranty expiring checker, where the electrical appliance extended warranty expiring checker includes an electrical appliance extended warranty expiring checker operator, and the electrical appliance extended warranty expiring checker operator includes that if the expiring parameter of the first electrical appliance's extended warranty is greater than the electrical appliance extended warranty expiring threshold, the expiring inspection result of the first electrical appliance's extended warranty is qualified, and if the expiring parameter of the first electrical appliance's extended warranty is less than or equal to the electrical appliance extended warranty expiring threshold, the expiring inspection result of the first electrical appliance's extended warranty is unqualified; Expiring Inspection Result Calculation Module: Output the expiring inspection result of the first electrical appliance's extended warranty according to the electrical appliance extended warranty expiring checker operator.

[0021] Preferably, in the expiring inspection module, first calculate the expiring parameter of the extended warranty according to the extended warranty time limit information of the first electrical appliance device and the current date. This parameter represents the time difference between the device and the expiration of the extended warranty time limit, and is obtained by calculating the remaining days between the current date and the extended warranty time limit; Subsequently, input the calculated expiring parameter of the extended warranty into the electrical appliance extended warranty expiring checker built in the expiring inspection module. This checker is responsible for judging whether the device is about to enter the expiring state of the extended warranty, and includes an expiring checker operator. This expiring checker operator judges whether the device is approaching the extended warranty time limit according to the preset expiring threshold. The checker operator executes the following logic: If the expiring parameter is greater than the electrical appliance extended warranty expiring threshold, it means that there is enough time until the extended warranty expires, and it is judged as qualified. If the expiring parameter is less than or equal to the electrical appliance extended warranty expiring threshold, it means that the device is about to enter the extended warranty period, or has approached or exceeded the extended warranty time limit, and it is judged as unqualified; After that, according to the judgment of the checker operator, the expiring inspection module will output the expiring inspection result of the first electrical appliance's extended warranty, that is, qualified or unqualified. This result will be used as the basis for subsequent health assessment, maintenance record collection and extended warranty recommendation report generation, so as to ensure an accurate judgment of the extended warranty status of the device, and then be able to provide users with a timely optimized extended warranty service plan.

[0022] Real-time Parameter Acquisition Module: When the expiring inspection result of the first electrical appliance's extended warranty is unqualified, collect the real-time parameters of the first electrical appliance device and establish a first electrical appliance state matrix.

[0023] Specifically, in the real-time parameter acquisition module, the real-time parameter acquisition module receives and parses the first electrical appliance extended warranty approaching expiration inspection result transmitted by the approaching expiration inspection module. When the parsed first electrical appliance extended warranty approaching expiration inspection result is unqualified, it indicates that the extended warranty period of the device is approaching or has expired, meaning that the device may enter a stage with a relatively high failure risk. Therefore, the real-time parameter acquisition module starts to collect various operating data of the device, that is, the real-time parameter acquisition module activates the built-in sensors or monitoring devices in the device to collect the operating parameters of the first electrical appliance in real time. These parameters include but are not limited to key indicators such as current, voltage, temperature, power consumption, and vibration. Subsequently, all the collected real-time data will be integrated by the real-time parameter acquisition module to form a complete first electrical appliance state matrix. This first electrical appliance state matrix is a multi-dimensional data structure that can reflect the current operating state of the device, including the real-time values of all key parameters. Through these data, the health status of the device can be comprehensively understood. This state matrix will be used as the input data for the subsequent health assessment module to determine whether the device needs to be repaired, whether it is suitable for continued extended warranty, or whether it needs to be overhauled in advance. Through this process, the actual operating state of the device can be obtained in a timely manner during the approaching expiration stage of the extended warranty, providing an accurate basis for precise health assessment and extended warranty decision-making.

[0024] A health assessment module for performing a health assessment on the first electrical appliance device based on the first electrical appliance state matrix to obtain a first electrical appliance health coefficient.

[0025] Specifically, in the health assessment module, after the health assessment module obtains the first electrical appliance state matrix through the real-time acquisition module, it will conduct a detailed analysis of the first electrical appliance state matrix to determine the health deviation situation of the first electrical appliance device, and input the determined deviation situation into the internal electrical appliance health assessment model. This electrical appliance health assessment model is trained based on the electrical appliance health deviation detection sample set and the electrical appliance health assessment sample set. During the health assessment process, the electrical appliance health assessment model will calculate a first electrical appliance health coefficient according to the received health deviation situation and the learned mapping relationship. This coefficient is a quantitative indicator representing the overall health status of the device. The value range of the health coefficient is set between 0 and 1. 0 indicates that the device is completely unhealthy and needs immediate repair or replacement, and 1 indicates that the device is in the best working state. The higher the value, the better the health status of the device. Through this health coefficient, it can provide a basis for the extended warranty decision-making, repair plan, etc. of the device. If the health coefficient is low, it indicates that the device has a relatively high failure risk and may need to be repaired as soon as possible or consider changing the extended warranty package. If the health coefficient is high, it indicates that the device is in a good state and the extended warranty service can continue.

[0026] Furthermore, the health assessment module includes:

[0027] Deviation detection module: perform health status deviation detection on the first electrical appliance according to the first electrical appliance state matrix to determine the first electrical appliance health deviation detection result; Supervision training module: use the electrical appliance health deviation detection sample set as input information and the electrical appliance health evaluation sample set as output information to perform supervised training on the electrical appliance health evaluation learner, and obtain the electrical appliance health evaluation loss coefficient every time a predetermined number of trainings is completed; Loss comparison module: if the electrical appliance health evaluation loss coefficient is less than the electrical appliance health evaluation loss threshold, generate an electrical appliance health evaluation model; Model calculation module: input the first electrical appliance health deviation detection result into the electrical appliance health evaluation model and output the first electrical appliance health coefficient.

[0028] Preferably, in the health assessment module, by analyzing the state matrix of the first electrical device, it is identified whether there are deviations in the parameters of the device. The health state deviation of the device refers to the difference between its operating parameters and the standard state, which is constructed based on the historical normal parameters of the first electrical device and the historical normal information corresponding to the electrical devices of the same model as the first electrical device. Through this standard state, it can be determined whether there is a significant health state deviation in the device, that is, the difference between the parameters of the device and the corresponding standard state is calculated, and the calculation result is stored to determine the first electrical health deviation detection result; before using the first electrical health deviation detection result for health assessment, it is necessary to perform supervised training on the electrical health assessment learner. This electrical health assessment learner is a model framework constructed based on machine learning algorithms such as support vector regression (SVR), random forest regression, and neural network. During the entire training process, a large number of electrical health deviation detection sample sets are collected as input information. These sample sets include the health deviation situations of various devices under different working conditions. At the same time, corresponding electrical health assessment sample sets are also collected as output information. These sample sets contain the electrical health coefficients corresponding to the health deviations. Then, the collected electrical health deviation detection sample sets and electrical health assessment sample sets are divided into a training set and a validation set. A common division ratio is 80% (training set) and 20% (validation set); taking the electrical health assessment learner constructed by a multi-layer perceptron as an example, this electrical health assessment learner includes an input layer, a hidden layer, and an output layer. The number of nodes in the input layer is equal to the number of input features. The number of nodes and layers in the hidden layer are determined according to actual needs. The activation function uses ReLU. The output layer is a single node corresponding to the health coefficient of the device. Then, the mean squared error (MSE) is used as the loss function to measure the gap between the predicted health coefficient and the actual health coefficient, and the Adam optimizer is selected for training to optimize the weights of the learner to minimize the loss function value; during the training process, the training set is used for forward propagation to calculate the loss, and the backpropagation algorithm is used to adjust the weights until the predetermined number of training epochs is reached; after each round of training (when the number of training times reaches the predetermined number), the validation set is used to evaluate the performance of the electrical health assessment learner, and the loss value of the electrical health assessment learner is calculated through the mean squared error as the electrical health assessment loss coefficient. If the electrical health assessment loss coefficient is greater than or equal to the electrical health assessment loss threshold, it means that the performance of the electrical health assessment learner is not good. At this time, the hyperparameters (such as the learning rate, the number of nodes in the hidden layer, etc.) will be adjusted, and the learner will be retrained. On the contrary, if the electrical health assessment loss coefficient is less than the electrical health assessment loss threshold, it means that the performance of the learner meets the expectations. At this time, the trained electrical health assessment learner will be deployed as the electrical health assessment model;After obtaining the electrical appliance health assessment model, the obtained first electrical appliance health deviation detection result will be input into the trained electrical appliance health assessment model. Based on the input deviation detection result, the electrical appliance health assessment model will calculate the first electrical appliance health coefficient of this electrical appliance. This first electrical appliance health coefficient is a quantitative index used to describe the health status of the electrical appliance. The higher the value, the better the health status of the device, providing a reliable basis for subsequent extended warranty decisions, maintenance services, or equipment management.

[0029] Furthermore, the health assessment module includes:

[0030] The first electrical appliance health standard library construction module: collect the historical normal parameters of the first electrical appliance device to obtain the first electrical appliance health standard library; the second electrical appliance health standard library construction module: collect the corresponding historical normal information of the same model electrical appliances of the first electrical appliance device to obtain the second electrical appliance health standard library; the cleaning and fusion module: perform cleaning and fusion according to the first electrical appliance health standard library and the second electrical appliance health standard library to obtain the third electrical appliance health standard library; the central tendency analysis module: perform central tendency analysis according to the third electrical appliance health standard library to establish the first electrical appliance health standard matrix; the deviation identification module: perform deviation identification on the first electrical appliance state matrix according to the first electrical appliance health standard matrix to generate the first electrical appliance health deviation detection result.

[0031] Optionally, during the electrical appliance health assessment, it is necessary to collect the historical normal parameters of the device to establish standardized health reference data. First, collect the historical normal parameters, that is, collect various parameters of the first electrical appliance device under normal operating conditions. These parameters usually include key indicators of the device such as temperature, voltage, current, power, vibration, etc. These normal parameters reflect the performance of the device in the best working state and are used for the construction of the first electrical appliance health standard library. Subsequently, collect the historical normal information of the same model devices, that is, collect the normal operating data of the same model and similar devices of the first electrical appliance device. Through the historical normal information of these same model devices, a second electrical appliance health standard library can be constructed, which can provide more dimensional health reference data. After that, according to the collected first electrical appliance health standard library and the second electrical appliance health standard library, data cleaning and fusion are carried out. When cleaning the data, it is necessary to remove data that does not meet the requirements such as missing values and outliers to ensure the accuracy and consistency of the data. Then, fuse the two cleaned standard libraries to obtain a third electrical appliance health standard library. This third electrical appliance health standard library contains the normal operating data of multiple devices and serves as a reference basis. Then, perform a central tendency analysis, that is, through statistical analysis of the data in the third electrical appliance health standard library, extract the central tendency of the health data, such as the mean of each parameter, or it can also be the median, standard deviation, etc. of each parameter, which is specifically determined according to actual needs. The central tendency analysis can help understand the typical values of each parameter when the device is operating normally and provide a standard for subsequent deviation detection. Finally, through the results of the central tendency analysis, construct the first electrical appliance health standard matrix. This matrix reflects the standardized values of each parameter of the device in the normal state and provides a basis for subsequent health assessment and deviation identification. By comparing the state matrix of the first electrical appliance with the first electrical appliance health standard matrix, calculate the difference between each parameter and the standard value, and then store these differences to obtain the first electrical appliance health deviation detection result, providing an accurate basis for subsequent health assessment.

[0032] The repair record collection module is used to collect the repair record information of the first electrical appliance device within the first electrical appliance extended warranty period if the first electrical appliance health coefficient is greater than or equal to the electrical appliance health threshold, and obtain the first electrical appliance repair record set.

[0033] Specifically, in the maintenance record collection module, when the calculated first electrical appliance health coefficient is greater than or equal to the set electrical appliance health threshold, it indicates that the health condition of the device is good and it is suitable for continued extended warranty. At this time, the maintenance record information of the device within the first electrical appliance extended warranty period will be collected, that is, all maintenance records related to the device will be extracted from the device's maintenance history database, especially the maintenance information during the extended warranty period. These information usually include detailed records such as the maintenance date, fault type, maintenance content, replaced parts, maintenance cost, etc. By collecting these maintenance records, a first electrical appliance maintenance record set can be generated, which provides more background information for the health condition of the device and helps to evaluate whether further maintenance is required or whether it is suitable for continued extended warranty.

[0034] The report sending module is used to perform adaptive optimization on the electrical appliance extended warranty package pool according to the first electrical appliance health coefficient and the first electrical appliance maintenance record set, generate a first electrical appliance extended warranty recommendation report, and encrypt and send the first electrical appliance extended warranty recommendation report to the user of the first electrical appliance device.

[0035] Specifically, in the report sending module, after collecting the health coefficient and maintenance record set of the first electrical appliance, adaptive optimization will be performed on the electrical appliance extended warranty package pool based on this information. The purpose of this step is to select the most suitable extended warranty package for the device according to the health condition and historical maintenance record of the device. In this process, the electrical appliance extended warranty packages that meet the current situation will be screened out from multiple electrical appliance extended warranty packages according to the first electrical appliance health coefficient and the first electrical appliance maintenance record set to form a candidate extended warranty package pool, and then the extended warranty package fitness parser will be used to evaluate the applicability of different extended warranty packages in the candidate extended warranty package pool to find the extended warranty package with the highest fitness in the candidate extended warranty package pool; through this adaptive optimization, an optimal extended warranty package can be obtained, and then this optimal extended warranty package will be integrated with the first electrical appliance health coefficient and the first electrical appliance maintenance record set to obtain a first electrical appliance extended warranty recommendation report, which details the recommended extended warranty package, extended warranty content, as well as the current health coefficient and maintenance record of the electrical appliance device; subsequently, in order to ensure the security of user information, the report will be encrypted to prevent information leakage, and the encryption can be performed through the AES encryption algorithm; after that, the encrypted extended warranty recommendation report will be sent to the user of the first electrical appliance device. After receiving the report, the user can choose whether to accept the recommended extended warranty package according to the suggestions in the report. This process not only provides a personalized extended warranty plan, but also enhances the intelligence and convenience of device management, thereby extending the service life of the device.

[0036] Furthermore, the report sending module includes:

[0037] Electrical Appliance Extended Warranty Package Pool Module: The electrical appliance extended warranty package pool includes multiple electrical appliance extended warranty packages; Evaluation Constraint Selection Module: Evaluate, constrain, and select the multiple electrical appliance extended warranty packages according to the first electrical appliance health coefficient and the first electrical appliance repair record set to establish a candidate extended warranty package pool; Parser Construction Module: Construct an extended warranty package fitness parser, where the extended warranty package fitness parser includes an extended warranty package fitness parsing function, and the extended warranty package fitness parsing function is: EPS = log EPK [PAW * G(EPA) + PBW * G(EPB)]; where EPS represents the extended warranty package fitness, EPK represents the extended warranty package fitness parsing factor, EPK > 1, PAW represents the warranty period matching weight, G(EPA) represents the normalized warranty period matching coefficient, PBW represents the warranty coverage weight, and G(EPB) represents the normalized warranty coverage coefficient; Maximal Optimization Module: Perform maximal optimization of the extended warranty package fitness for the candidate extended warranty package pool according to the extended warranty package fitness parser to determine the optimized result of the electrical appliance extended warranty package; Data Arrangement Module: Arrange data according to the first electrical appliance health coefficient, the first electrical appliance repair record set, and the optimized result of the electrical appliance extended warranty package to obtain the first electrical appliance extended warranty recommendation report.

[0038] Preferably, an electrical appliance extended warranty package pool is stored in the Report Sending Module. This electrical appliance extended warranty package pool includes multiple electrical appliance extended warranty packages. For example, a basic extended warranty package (1 year, including basic fault repair and replacement of major components), a full-coverage extended warranty package (2 years, covering fault repair, labor, and material costs of all components), a parts extended warranty package (1 year, including repair and replacement of specific components such as batteries and motors), an accidental damage extended warranty package (1 year, covering repair costs caused by accidental damage such as dropping and liquid splashing), an equipment upgrade extended warranty package (2 years, including equipment technology upgrade, fault repair, and software update), a full warranty / return-to-factory repair package (1 year, including equipment return-to-factory repair, transportation costs, and replacement of components), etc.; According to the first electrical appliance health coefficient and the first electrical appliance repair record set, the Report Sending Module will evaluate, constrain, and select these extended warranty packages. The purpose of this step is to calculate the warranty period matching coefficient and the warranty coverage coefficient of each extended warranty package by analyzing the health coefficient and repair history of the equipment, and compare these coefficients with the corresponding constraints to screen out the extended warranty packages suitable for the current condition of the equipment and form a candidate extended warranty package pool. In this way, it can be ensured that the recommended extended warranty package can meet the actual needs of the equipment; Subsequently, an extended warranty package fitness parser is constructed. The function of this parser is to evaluate the fitness of each package in the candidate extended warranty package pool according to the specific situation of the equipment and find the package that best meets the equipment's needs. The extended warranty package fitness parser includes an extended warranty package fitness parsing function, and the formula of this function is: EPS = log EPK[PAW * G(EPA) + PBW * G(EPB)]; where EPS represents the fitness of the extended warranty package. The higher the fitness, the more suitable the extended warranty package is for the current electrical equipment. EPK represents the fitness analysis factor of the extended warranty package, which is used to control the scale of the entire fitness calculation and can adjust the sensitivity of the model. EPK > 1. PAW represents the weight of the extended warranty period match, which is determined based on actual needs and combined with expert decision-making. G(EPA) represents the normalized extended warranty period match coefficient, which is the degree of match between the remaining extended warranty time limit of the equipment currently and the extended warranty time limit provided by the extended warranty package. PBW represents the weight of the extended warranty coverage, and the determination method is the same as that of the extended warranty period match weight. G(EPB) represents the normalized extended warranty coverage coefficient, which is the degree of match between the protection scope provided by the extended warranty package and the maintenance requirements of the equipment. Through this analysis function, the fitness of each candidate package will be calculated based on the health coefficient and maintenance records of the equipment, so as to evaluate the applicability of each extended warranty package. Then, an optimization search for maximizing the fitness of the candidate extended warranty package pool is carried out. The purpose is to select the extended warranty package with the highest fitness value according to the fitness analysis function, so as to find an optimal extended warranty package, so that the package can best meet the needs of the equipment in terms of extended warranty period match and extended warranty coverage, so as to provide the best extended warranty service. Finally, based on the first electrical health coefficient, the first electrical maintenance record set and the optimization result of the electrical extended warranty package, data collation will be carried out to generate the first electrical extended warranty recommendation report. This report not only details the recommended extended warranty package and extended warranty service content, but also includes the current health coefficient and maintenance records of the equipment. The extended warranty package recommendations provided in the report are personalized and can provide customized extended warranty services according to the health status and maintenance history of the equipment, so as to provide accurate and intelligent extended warranty recommendations for users, help extend the service life of the equipment, and improve the user experience.

[0039] Furthermore, the report sending module includes:

[0040] Package traversal module: Traverse the multiple electrical extended warranty packages and extract the first electrical extended warranty package; Remaining life prediction module: Predict the remaining life of the first electrical equipment according to the first electrical health coefficient to determine the predicted remaining life of the first electrical equipment; Matching degree evaluation module: Evaluate the matching degree of the extended warranty service period within the first electrical extended warranty package according to the predicted remaining life of the first electrical equipment to obtain the first extended warranty period matching coefficient; Extended warranty coverage evaluation module: Evaluate the extended warranty coverage of the first electrical extended warranty package according to the first electrical maintenance record set to determine the first extended warranty coverage coefficient; Candidate extended warranty package adding module: If the first extended warranty period matching coefficient meets the extended warranty period match constraint, and the first extended warranty coverage coefficient meets the extended warranty coverage constraint, set the first electrical extended warranty package as the first candidate extended warranty package and add the first candidate extended warranty package to the candidate extended warranty package pool.

[0041] Optionally, when selecting multiple electrical appliance extended warranty packages, first traverse all the extended warranty packages in the extended warranty package pool, and use the first traversed electrical appliance extended warranty package as the first electrical appliance extended warranty package; subsequently, use the health coefficient of the first electrical appliance to predict the remaining life of the device. The health coefficient reflects the current health status of the device and is usually a value between 0 and 1. By inputting the first electrical appliance health coefficient, the current usage duration of the electrical appliance device, and the electrical appliance device type number into a pre-constructed life prediction model, estimate the remaining life of the device (i.e., the time the device can still work normally in the current state), and use the estimation result as the predicted remaining life of the first electrical appliance. Among them, the life prediction model is an integrated model, which includes multiple electrical appliance device life prediction channels internally. Each electrical appliance device life prediction channel has a device type number, and each electrical appliance device life prediction channel can be constructed based on a multi-layer perceptron. The construction method is the same as the foregoing, and all are carried out through steps such as forward propagation, loss calculation, backpropagation, and parameter optimization; then, according to the predicted remaining life of the first electrical appliance, evaluate the matching degree of the extended warranty service period in the selected first electrical appliance extended warranty package. The purpose of this step is to check whether the extended warranty service period is sufficient to cover the remaining life of the device. When calculating the extended warranty period matching coefficient, by comparing the predicted remaining life of the first electrical appliance and the extended warranty period provided in the extended warranty package (for example, 2 years, 3 years, etc.), obtain the first extended warranty period matching coefficient. Suppose the predicted remaining life is Y years and the extended warranty service period is X years, then the first extended warranty period matching coefficient G(EPA) can be calculated in the following way: If the value of G(EPA) is close to 1, it means that the matching degree between the extended warranty time limit and the remaining life is relatively high. If it is less than 1, it means that the extended warranty time limit cannot fully cover the remaining life of the device; then, evaluate the first extended warranty coverage of the first electrical appliance extended warranty package according to the repair record set of the first electrical appliance. The repair record set contains the device's historical faults and repair records. Based on these records, evaluate whether the extended warranty package can cover the possible fault types of the device. When calculating the first extended warranty coverage coefficient, compare the first electrical appliance repair record set of the device and the coverage range provided by the extended warranty package. If the extended warranty package covers the common fault types of the device and can meet the possible repair needs of the device, the extended warranty coverage coefficient G(EPB) will be relatively high; finally, if the calculated first extended warranty period matching coefficient and the first extended warranty coverage coefficient respectively meet the preset constraint conditions, that is, the first extended warranty period matching coefficient is greater than or equal to the extended warranty period matching constraint and the first extended warranty coverage coefficient is greater than or equal to the extended warranty coverage constraint, it means that this extended warranty package can better meet the extended warranty needs of the device. At this time, the first electrical appliance extended warranty package will be set as the first candidate extended warranty package and added to the candidate extended warranty package pool. Repeat this process until all the packages in the electrical appliance extended warranty package pool are checked for subsequent decision-making use.

[0042] Furthermore, the report sending module includes:

[0043] A fault type identification module: identifying the fault type according to the first electrical appliance maintenance record set to determine the distribution of the first electrical appliance maintenance fault types; a protection scope feature identification module: identifying the protection scope features according to the first electrical appliance extended warranty package to obtain the first package protection scope distribution; an intersection calculation module: using the intersection of the first electrical appliance maintenance fault type distribution and the first package protection scope distribution as the first extended warranty coverage distribution; a coverage evaluation module: evaluating the coverage of the first package protection scope distribution according to the first extended warranty coverage distribution to obtain the first extended warranty coverage coefficient.

[0044] Optionally, in the report sending module, the fault type of the device is identified according to the first electrical appliance maintenance record set, that is, the components and component problems repaired in the first electrical appliance maintenance record set are traversed, and the traversed components are classified according to the component problems, and the proportion of each fault type in the first electrical appliance maintenance record set is recorded. For example, voltage instability of the power supply, short circuit of the circuit board, inability of the battery to charge, looseness of the wiring terminal, etc. are classified as electrical faults, inability of the motor to rotate, gear wear, bearing wear, belt breakage, etc. are classified as mechanical faults, operating system crash, embedded software error, hardware driver damage, etc. are classified as software faults. By summarizing the classified fault types, the distribution of the first electrical appliance maintenance fault types is determined; subsequently, the specific components and service contents covered are extracted from the protection scope in the first electrical appliance extended warranty package, such as partial electrical component protection, mechanical component protection, etc., and the covered components are classified according to the service contents to determine the first package protection scope distribution; then, the intersection operation is performed on the maintenance fault type distribution and the extended warranty package protection scope distribution to find the fault types and corresponding components that can be covered by the package protection scope. For example, the electrical component protection in the first package protection scope distribution includes the power supply, battery, circuit board, relay, the mechanical component protection includes the motor, gear, bearing, belt, and the electrical faults in the first electrical appliance maintenance fault type distribution include the power supply, circuit board, wiring terminal, the mechanical faults include the motor, belt, cylinder, and the software faults include the operating system, embedded software. Then, the fault types and corresponding components that can be covered by the package protection scope are electrical faults (power supply, circuit board) and mechanical faults (motor, belt). By organizing the calculated intersection results, the first extended warranty coverage distribution is obtained. This first extended warranty coverage distribution reflects the actual protection ability of the extended warranty package for various faults of the device; then, the coverage of the first electrical appliance extended warranty package protection scope distribution is evaluated according to the first extended warranty coverage distribution. This process will evaluate the coverage of the extended warranty package for different fault types and reflect the degree to which the package meets the device maintenance requirements. The specific calculation method is: Where Ci is the coverage of the i-th type of failure, that is, the ratio of the components involved in each guarantee in the first extended warranty coverage distribution to the components involved in each guarantee in the first package guarantee scope, P i is the proportion of the i-th type of failure in the maintenance records, that is, the proportion of the occurrence of this type of failure in the first electrical appliance maintenance record set; the calculated first extended warranty coverage coefficient measures the coverage degree of the extended warranty package for the equipment failure types, and the higher the value, the more failure coverage guarantees the extended warranty package can provide, so as to ensure that the electrical equipment can be effectively repaired and supported during the extended warranty period.

[0045] In summary, the full-process electrical equipment extended warranty data traceability management platform provided by the embodiments of the present application has the following technical effects:

[0046] The extended warranty time limit obtaining module is used to obtain the first electrical appliance extended warranty time limit corresponding to the first electrical appliance; the approaching expiration inspection module is used to input the first electrical appliance extended warranty time limit into the electrical appliance extended warranty approaching expiration checker to obtain the first electrical appliance extended warranty approaching expiration inspection result; the real-time parameter acquisition module is used to collect the real-time parameters of the first electrical appliance and establish the first electrical appliance state matrix when the first electrical appliance extended warranty approaching expiration inspection result is unqualified; the health assessment module is used to perform a health assessment on the first electrical appliance according to the first electrical appliance state matrix to obtain the first electrical appliance health coefficient; the maintenance record acquisition module is used to collect the maintenance record information of the first electrical appliance within the first electrical appliance extended warranty time limit to obtain the first electrical appliance maintenance record set if the first electrical appliance health coefficient is greater than or equal to the electrical appliance health threshold; the report sending module is used to perform adaptive optimization on the electrical appliance extended warranty package pool according to the first electrical appliance health coefficient and the first electrical appliance maintenance record set, generate the first electrical appliance extended warranty recommendation report, and encrypt and send the first electrical appliance extended warranty recommendation report to the user of the first electrical appliance. Through the above steps, the technical problem that the health status and maintenance record information of the equipment cannot be obtained in time in the existing electrical equipment extended warranty management, resulting in inaccurate extended warranty recommendations, is solved. By collecting equipment parameters and health assessment in real time, a personalized extended warranty recommendation report is generated, achieving the technical effects of improving the accuracy and effectiveness of the extended warranty service and extending the service life of the equipment.

[0047] Embodiment 2, based on the same inventive concept as the full-process electrical equipment extended warranty data traceability management platform in the foregoing embodiment, as Figure 2 shown, the embodiments of the present application provide a full-process electrical equipment extended warranty data traceability management method, and the method includes:

[0048] Obtain the first electrical appliance extended warranty time limit corresponding to the first electrical appliance device; input the first electrical appliance extended warranty time limit into the electrical appliance extended warranty approaching expiration checker to obtain the first electrical appliance extended warranty approaching expiration inspection result; when the first electrical appliance extended warranty approaching expiration inspection result is unqualified, collect the real-time parameters of the first electrical appliance device, and establish a first electrical appliance state matrix; perform a health assessment on the first electrical appliance device according to the first electrical appliance state matrix to obtain a first electrical appliance health coefficient; if the first electrical appliance health coefficient is greater than or equal to the electrical appliance health threshold, collect the maintenance record information of the first electrical appliance device within the first electrical appliance extended warranty time limit to obtain a first electrical appliance maintenance record set; perform adaptive optimization on the electrical appliance extended warranty package pool according to the first electrical appliance health coefficient and the first electrical appliance maintenance record set, generate a first electrical appliance extended warranty recommendation report, and encrypt and send the first electrical appliance extended warranty recommendation report to the user of the first electrical appliance device.

[0049] Further, performing adaptive optimization on the electrical appliance extended warranty package pool according to the first electrical appliance health coefficient and the first electrical appliance maintenance record set to generate a first electrical appliance extended warranty recommendation report includes:

[0050] The electrical appliance extended warranty package pool includes multiple electrical appliance extended warranty packages; perform evaluation constraint selection on the multiple electrical appliance extended warranty packages according to the first electrical appliance health coefficient and the first electrical appliance maintenance record set to establish a candidate extended warranty package pool; construct an extended warranty package fitness parser, where the extended warranty package fitness parser includes an extended warranty package fitness parsing function, and the extended warranty package fitness parsing function is: EPS = log EPK [PAW * G(EPA) + PBW * G(EPB)]; where EPS represents the extended warranty package fitness, EPK represents the extended warranty package fitness parsing factor, EPK > 1, PAW represents the extended warranty period matching weight, G(EPA) represents the normalized extended warranty period matching coefficient, PBW represents the extended warranty coverage weight, and G(EPB) represents the normalized extended warranty coverage coefficient; perform extended warranty package fitness maximization optimization on the candidate extended warranty package pool according to the extended warranty package fitness parser to determine the electrical appliance extended warranty package optimization result; perform data collation according to the first electrical appliance health coefficient, the first electrical appliance maintenance record set, and the electrical appliance extended warranty package optimization result to obtain the first electrical appliance extended warranty recommendation report.

[0051] Further, performing evaluation constraint selection on the multiple electrical appliance extended warranty packages according to the first electrical appliance health coefficient and the first electrical appliance maintenance record set to establish a candidate extended warranty package pool includes:

[0052] Traverse the multiple electrical appliance extended warranty packages, and extract the first electrical appliance extended warranty package; predict the remaining life of the first electrical appliance device according to the first electrical appliance health coefficient, and determine the predicted remaining life of the first electrical appliance; evaluate the matching degree of the extended warranty service period within the first electrical appliance extended warranty package according to the predicted remaining life of the first electrical appliance, and obtain the first extended warranty period matching coefficient; evaluate the extended warranty coverage of the first electrical appliance extended warranty package according to the first electrical appliance repair record set, and determine the first extended warranty coverage coefficient; if the first extended warranty period matching coefficient meets the extended warranty period matching constraint, and the first extended warranty coverage coefficient meets the extended warranty coverage constraint, set the first electrical appliance extended warranty package as the first candidate extended warranty package, and add the first candidate extended warranty package to the candidate extended warranty package pool.

[0053] Further, evaluating the extended warranty coverage of the first electrical appliance extended warranty package according to the first electrical appliance repair record set to determine the first extended warranty coverage coefficient includes:

[0054] Identify the failure type according to the first electrical appliance repair record set to determine the distribution of the first electrical appliance repair failure types; identify the protection scope characteristics according to the first electrical appliance extended warranty package to obtain the first package protection scope distribution; use the intersection of the first electrical appliance repair failure type distribution and the first package protection scope distribution as the first extended warranty coverage distribution; evaluate the coverage of the first package protection scope distribution according to the first extended warranty coverage distribution to obtain the first extended warranty coverage coefficient.

[0055] Further, evaluating the health of the first electrical appliance device according to the first electrical appliance state matrix to obtain the first electrical appliance health coefficient includes:

[0056] Detect the health status deviation of the first electrical appliance device according to the first electrical appliance state matrix to determine the first electrical appliance health deviation detection result; use the electrical appliance health deviation detection sample set as the input information and the electrical appliance health evaluation sample set as the output information to perform supervised training on the electrical appliance health evaluation learning machine, and obtain the electrical appliance health evaluation loss coefficient every time a predetermined number of trainings is performed; if the electrical appliance health evaluation loss coefficient is less than the electrical appliance health evaluation loss threshold, generate an electrical appliance health evaluation model; input the first electrical appliance health deviation detection result into the electrical appliance health evaluation model to output the first electrical appliance health coefficient.

[0057] Further, detecting the health status deviation of the first electrical appliance device according to the first electrical appliance state matrix to determine the first electrical appliance health deviation detection result includes:

[0058] Collect the historical normal parameters of the first electrical device to obtain the first electrical health standard library; collect the historical normal information corresponding to the electrical devices of the same model as the first electrical device to obtain the second electrical health standard library; perform cleaning and fusion based on the first electrical health standard library and the second electrical health standard library to obtain the third electrical health standard library; perform central tendency analysis based on the third electrical health standard library to establish the first electrical health standard matrix; perform deviation identification on the first electrical state matrix according to the first electrical health standard matrix to generate the first electrical health deviation detection result.

[0059] Further, input the first electrical device's extended warranty time limit into the electrical device extended warranty approaching expiration checker to obtain the first electrical device extended warranty approaching expiration inspection result, including:

[0060] Calculate the first electrical device extended warranty approaching expiration parameter according to the first electrical device's extended warranty time limit; input the first electrical device extended warranty approaching expiration parameter into the electrical device extended warranty approaching expiration checker, where the electrical device extended warranty approaching expiration checker includes an electrical device extended warranty approaching expiration inspection operator, and the electrical device extended warranty approaching expiration inspection operator includes that if the first electrical device extended warranty approaching expiration parameter is greater than the electrical device extended warranty approaching expiration threshold, the first electrical device extended warranty approaching expiration inspection result is qualified, and if the first electrical device extended warranty approaching expiration parameter is less than or equal to the electrical device extended warranty approaching expiration threshold, the first electrical device extended warranty approaching expiration inspection result is unqualified; output the first electrical device extended warranty approaching expiration inspection result according to the electrical device extended warranty approaching expiration inspection operator.

[0061] Any step of the method described above can be stored as computer instructions or programs in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor to implement any one of the methods in the embodiments of the present application, without further limitation here.

[0062] Further, the first or second mentioned above may not only represent an order relationship, but may also represent a certain specific concept, and / or refer to the selection of multiple elements individually or in whole. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A full-process electrical equipment extended warranty data traceability management platform, characterized in that: The platform includes: An extended warranty time limit obtaining module, used to obtain a first electrical appliance extended warranty time limit corresponding to the first electrical appliance; An expiration inspection module is used to input the first electrical appliance extended warranty time limit into an electrical appliance extended warranty expiration inspection device to obtain a first electrical appliance extended warranty expiration inspection result; A real-time parameter collection module, used for collecting real-time parameters of the first electrical equipment and establishing a first electrical equipment state matrix when the first electrical equipment extended warranty near-expiry inspection result is unqualified; A health assessment module, configured to perform a health assessment on the first electrical device according to the first electrical device state matrix to obtain a first electrical device health coefficient; A maintenance record collection module, configured to collect maintenance record information of the first electrical device within the first electrical device extended warranty period to obtain a first electrical device maintenance record set if the first electrical device health coefficient is greater than or equal to an electrical device health threshold; The report sending module is used to adaptively optimize the appliance extended warranty package pool according to the first appliance health coefficient and the first appliance maintenance record set, generate a first appliance extended warranty recommendation report, and encrypt and send the first appliance extended warranty recommendation report to the user of the first appliance device.

2. A full-process electrical equipment extended warranty data tracing management platform as claimed in claim 1, characterized in that: The report sending module comprises: Electrical appliance extended warranty package pool module: the electrical appliance extended warranty package pool includes multiple electrical appliance extended warranty packages; An evaluation constraint selection module: performing evaluation constraint selection on the plurality of appliance extended warranty packages according to the first appliance health coefficient and the first appliance maintenance record set, and establishing a candidate extended warranty package pool; Parser construction module: construct an extended warranty package fitness parser, wherein the extended warranty package fitness parser includes an extended warranty package fitness parsing function, and the extended warranty package fitness parsing function is: EPS=log EPK [PAW*G(EPA)+PBW*G(EPB)]; Among them, EPS represents the fitness of the extended warranty package, EPK represents the fitness analysis factor of the extended warranty package, EPK>1, PAW represents the extended warranty period matching weight, G(EPA) represents the normalized extended warranty period matching coefficient, PBW represents the extended warranty coverage weight, and G(EPB) represents the normalized extended warranty coverage coefficient; Maximization optimization module: performs maximization optimization of the fitness of the extended warranty packages on the candidate extended warranty package pool according to the extended warranty package fitness analyzer, and determines the optimization result of the electrical appliance extended warranty package; Data sorting module: sorting data according to the first appliance health coefficient, the first appliance maintenance record set and the appliance extended warranty package optimization result to obtain the first appliance extended warranty recommendation report.

3. A full-process electrical equipment extended warranty data tracing management platform as claimed in claim 2, characterized in that: The report sending module comprises: Package traversal module: traverses the multiple appliance extended warranty packages and extracts the first appliance extended warranty package; Remaining life prediction module: predicting the remaining life of the first electrical device according to the first electrical device health coefficient, and determining the predicted remaining life of the first electrical device; Matching evaluation module: performs matching evaluation on the extended warranty service period in the extended warranty package of the first appliance according to the predicted remaining life of the first appliance, and obtains a first extended warranty period matching coefficient; An extended warranty coverage evaluation module: evaluating the extended warranty coverage of the first appliance extended warranty package according to the first appliance maintenance record set, and determining a first extended warranty coverage coefficient; Candidate extended warranty package adding module: If the first extended warranty period matching coefficient satisfies the extended warranty period matching constraint, and the first extended warranty coverage coefficient satisfies the extended warranty coverage constraint, the first appliance extended warranty package is set as the first candidate extended warranty package, and the first candidate extended warranty package is added to the candidate extended warranty package pool.

4. A full-process electrical equipment extended warranty data tracing management platform as claimed in claim 3, characterized in that: The report sending module comprises: A fault type identification module: identifying the fault type according to the first electrical appliance maintenance record set, and determining the distribution of the first electrical appliance maintenance fault type; A protection range feature identification module: identifies the protection range features according to the first appliance extended warranty package, and obtains the protection range distribution of the first package; An intersection calculation module: taking the intersection of the first electrical appliance repair fault type distribution and the first package coverage distribution as the first extended warranty coverage distribution; Coverage evaluation module: perform coverage evaluation on the first package protection scope distribution according to the first extended warranty coverage distribution, and obtain the first extended warranty coverage coefficient.

5. A full-process electrical equipment extended warranty data tracing management platform as claimed in claim 1, characterized in that: The health assessment module includes: Deviation detection module: performs health state deviation detection on the first electrical device according to the first electrical device state matrix to determine a health deviation detection result of the first electrical device; Supervised training module: using the appliance health deviation detection sample set as input information and the appliance health assessment sample set as output information, supervised training is performed on the appliance health assessment learner, and the appliance health assessment loss coefficient is obtained after each predetermined number of trainings; Loss comparison module: if the appliance health assessment loss coefficient is less than the appliance health assessment loss threshold, generate an appliance health assessment model; Model calculation module: inputs the first appliance health deviation detection result into the appliance health assessment model, and outputs the first appliance health coefficient.

6. A full-process electrical equipment extended warranty data tracing management platform as claimed in claim 5, characterized in that: The health assessment module includes: A first electrical appliance health standard library construction module: collecting historical normal parameters of the first electrical appliance to obtain a first electrical appliance health standard library; A second electrical appliance health standard library construction module: collecting historical normal information corresponding to electrical appliances of the same model as the first electrical device to obtain a second electrical appliance health standard library; Cleaning and fusion module: cleaning and fusion according to the first electrical appliance health standard library and the second electrical appliance health standard library to obtain a third electrical appliance health standard library; Central tendency analysis module: performing central tendency analysis according to the third electrical appliance health standard library to establish a first electrical appliance health standard matrix; Deviation identification module: performs deviation identification on the first electrical appliance state matrix according to the first electrical appliance health standard matrix, and generates the first electrical appliance health deviation detection result.

7. A full-process electrical equipment extended warranty data tracing management platform as claimed in claim 1, characterized in that: The clinical test module includes: A near-expiry parameter calculation module: calculates near-expiry parameters of the first electrical appliance according to the first electrical appliance extended warranty time limit; The expiration parameter input module is used to input the first electrical appliance extended warranty expiration parameter into the electrical appliance extended warranty expiration detector, wherein the electrical appliance extended warranty expiration detector includes an electrical appliance extended warranty expiration verification operator, and the electrical appliance extended warranty expiration verification operator includes: if the first electrical appliance extended warranty expiration parameter is greater than the electrical appliance extended warranty expiration threshold, the first electrical appliance extended warranty expiration verification result is qualified; if the first electrical appliance extended warranty expiration parameter is less than or equal to the electrical appliance extended warranty expiration threshold, the first electrical appliance extended warranty expiration verification result is unqualified; The near-expiry inspection result calculation module is used to output the near-expiry inspection result of the first electrical appliance extended warranty according to the near-expiry inspection operator of the electrical appliance extended warranty.

8. A full-process electrical equipment extended warranty data traceability management method, characterized in that: Based on the implementation of a full-process electrical equipment extended warranty data traceability management platform according to any one of claims 1 to 7, the method comprises: Obtain the first electrical appliance extended warranty period corresponding to the first electrical appliance; Inputting the first electrical appliance extended warranty time limit into an electrical appliance extended warranty expiration tester to obtain a first electrical appliance extended warranty expiration test result; When the first electrical appliance extended warranty near-expiry inspection result is unqualified, collecting real-time parameters of the first electrical appliance and establishing a first electrical appliance state matrix; Performing a health assessment on the first electrical device according to the first electrical device state matrix to obtain a first electrical device health coefficient; If the health coefficient of the first electrical appliance is greater than or equal to the health threshold of the electrical appliance, collecting maintenance record information of the first electrical appliance within the first electrical appliance extended warranty period to obtain a first electrical appliance maintenance record set; The appliance extended warranty package pool is adaptively optimized according to the first appliance health coefficient and the first appliance maintenance record set, a first appliance extended warranty recommendation report is generated, and the first appliance extended warranty recommendation report is encrypted and sent to the user of the first appliance device.

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