Data processing method and device of intelligent equipment, equipment and storage medium

Abstract

The invention provides a data processing method and device of intelligent equipment, equipment and a storage medium, and relates to the technical field of intelligent household appliances. The server side receives an evaluation request which is sent by the terminal equipment and contains the identifier of the intelligent equipment, and obtains the previously collected use data and maintenance data of the intelligent equipment from a database according to the identifier of the intelligent equipment; predicting the remaining service life of the intelligent equipment according to the use data and the maintenance data; adjusting values of influence factors in a reference evaluation model based on the use data and the maintenance data to obtain a target evaluation model of the intelligent device; and obtaining a value evaluation scheme of the intelligent equipment and value data of the intelligent equipment corresponding to the value evaluation scheme by using the target evaluation model and the residual service life, and sending the value evaluation scheme and the value data to the terminal equipment. Through the above mode, the prediction accuracy of the value data of the intelligent equipment is improved.

Description

Technical Field

[0001] This application belongs to the field of smart home appliance technology, specifically relating to a data processing method, apparatus, device, and storage medium for a smart device. Background Technology

[0002] Home appliance recycling is an important part of the circular economy, and the valuation process directly affects recycling efficiency and user satisfaction. Current home appliance recycling systems mainly rely on information manually entered by users, such as the functions of smart devices, purchase year, and usage frequency, supplemented by simplified calculation models such as the average market depreciation rate, and offer multiple assessment methods such as mobile phone photo valuation, on-site assessment, and physical store assessment.

[0003] However, the above methods lack consideration of the actual usage of smart devices, resulting in low accuracy of the predicted value data for smart devices and reduced recycling efficiency. Summary of the Invention

[0004] To address the aforementioned problems in the prior art, this application provides a data processing method, apparatus, device, and storage medium for smart devices.

[0005] In a first aspect, this application provides a data processing method for a smart device, the method comprising:

[0006] The system receives an evaluation request from a terminal device containing the identifier of the smart device, and retrieves previously collected usage and maintenance data of the smart device from the database based on the identifier of the smart device.

[0007] Based on the usage data and the maintenance data, predict the remaining lifespan of the smart device;

[0008] Based on the usage data and the maintenance data, the values ​​of the influencing factors in the reference valuation model are adjusted to obtain the target valuation model for the smart device.

[0009] Using the target valuation model and the remaining useful life, a value assessment scheme for the smart device and the value data of the smart device corresponding to the value assessment scheme are obtained, and the value assessment scheme and the value data are sent to the terminal device.

[0010] In one possible implementation, predicting the remaining lifespan of the smart device based on the usage data and the maintenance data includes:

[0011] Based on the identifier of the smart device, query the remaining service life of the smart device, and based on the usage data, determine the completed service life of the smart device;

[0012] Based on the available lifespan and the already used lifespan, the initial remaining lifespan of the smart device is obtained;

[0013] Based on the usage data and the maintenance data, obtain multiple indicator parameters for evaluating the performance of the smart device;

[0014] The initial remaining lifespan is adjusted based on the multiple indicator parameters to obtain the remaining lifespan of the smart device.

[0015] In one possible implementation, the step of adjusting the initial remaining lifespan based on the plurality of index parameters to obtain the remaining lifespan of the smart device includes:

[0016] Obtain the year-based adjustment value and reference weight for each indicator parameter;

[0017] Retrieve device information of the smart device from the database;

[0018] Each reference weight is corrected based on the device information to obtain the target weight corresponding to each indicator parameter;

[0019] The adjustment period is obtained by multiplying each adjustment period value by the corresponding target weight, and the remaining lifespan of the smart device is obtained by combining the multiple adjustment periods with the initial lifespan.

[0020] In one possible implementation, adjusting the values ​​of the influencing factors in the reference valuation model based on the usage data and the maintenance data to obtain the target valuation model for the smart device includes:

[0021] Based on the reference valuation model, obtain the target parameters for each influencing factor corrected in the reference valuation model;

[0022] Based on the functional characteristics of the intelligent device, each target parameter is analyzed, and related parameters are obtained from the usage data and the maintenance data;

[0023] The target valuation model for the smart device is obtained by adjusting the corresponding influencing factors based on the associated parameters.

[0024] In one possible implementation, adjusting the corresponding influence factor according to the correlation parameter includes:

[0025] Based on the functional characteristics of the smart device, the correlation degree between each associated parameter and the corresponding target parameter is obtained, and the corresponding correlation weight is obtained according to the correlation degree.

[0026] Based on the numerical range of each associated parameter, obtain the adjustment coefficient for each associated parameter, and adjust the corresponding influence factor based on multiple adjustment coefficients and the associated weights corresponding to each adjustment coefficient.

[0027] In one possible implementation, obtaining a valuation scheme for the smart device using the target valuation model and the remaining useful life includes:

[0028] The target valuation model is transformed into multiple basic relationships;

[0029] Based on the usage data and the maintenance data, parameter description information and valuation description information are generated for each basic relation. The parameter description information is used to indicate the definition of each parameter in each basic relation, and the valuation description information is used to indicate the conditions for setting the influencing factors included in each basic relation.

[0030] A valuation scheme for the intelligent device is generated based on the parameter descriptions and valuation information for each basic relation.

[0031] In one possible implementation, recycling information or value feedback information is obtained based on user feedback information based on the value assessment scheme and value data.

[0032] If the recycling information is obtained, it will be sent to the recycling terminal to prompt the recycling personnel to recycle the smart device;

[0033] If the value feedback information is obtained, the target valuation model is adjusted based on the value feedback information, new value data is obtained and a new value assessment scheme is generated, and the new value assessment scheme and the new value data are sent to the terminal device.

[0034] Secondly, this application provides a data processing and control device for a smart device, comprising: an acquisition module, a prediction module, an adjustment module, and an estimation module, wherein:

[0035] The acquisition module is used to receive an evaluation request sent by the terminal device containing the identifier of the smart device, and to retrieve previously collected usage data and maintenance data of the smart device from the database based on the identifier of the smart device.

[0036] The prediction module is used to predict the remaining lifespan of the smart device based on the usage data and the maintenance data.

[0037] The adjustment module is used to adjust the values ​​of the influencing factors in the reference valuation model based on the usage data and the maintenance data, so as to obtain the target valuation model of the smart device.

[0038] The valuation module is used to obtain a valuation scheme for the smart device and the value data of the smart device corresponding to the valuation scheme by using the target valuation model and the remaining useful life, and to send the valuation scheme and the value data to the terminal device.

[0039] Thirdly, this application also provides an electronic device, comprising: at least one processor and a memory, wherein:

[0040] The memory is used to store computer-executed instructions;

[0041] The at least one processor is configured to execute computer execution instructions stored in the memory, such that the at least one processor performs the method as described in any of the first aspects.

[0042] Fourthly, this application also provides a computer storage medium storing computer execution instructions, which, when executed by a processor, are used to implement the method described in any of the first aspects.

[0043] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, can implement the steps of the scheme recommendation method as described in any of the first aspects.

[0044] This application provides a data processing method, apparatus, device, and storage medium for smart devices. It predicts the remaining service life of the device to be recycled based on its usage and maintenance information, adjusts the influencing factors of a reference valuation model, generates a value assessment scheme and corresponding value data based on the remaining service life and the adjusted target valuation model, and sends this data to the terminal device. This approach comprehensively considers the actual usage of smart devices, improving the accuracy of value data prediction for smart devices. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] Figure 1 A scenario diagram provided for an embodiment of this application;

[0047] Figure 2 A flowchart illustrating a data processing method for a smart device provided in this application embodiment. Figure 1 ;

[0048] Figure 3 A flowchart illustrating a data processing method for a smart device provided in this application embodiment. Figure 2 ;

[0049] Figure 4 A flowchart illustrating a data processing method for a smart device provided in this application embodiment. Figure 3 ;

[0050] Figure 5 This is a schematic diagram of the structure of a data processing control device for a smart device provided in an embodiment of this application;

[0051] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0052] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0055] In this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0056] Current home appliance recycling systems primarily rely on information manually entered by users, such as the functions of smart devices, purchase year, and usage frequency, supplemented by simplified calculation models based on average market depreciation rates. They also offer various assessment methods, including mobile phone photo valuation, on-site evaluation, and in-store evaluation. However, these methods lack consideration of the actual usage of smart devices, resulting in low accuracy in predicted device value data and reduced recycling efficiency.

[0057] This application provides a data processing method for smart devices. Based on the usage and maintenance information of the device to be recycled, the remaining service life is predicted, and the influencing factors of a reference valuation model are adjusted. Based on the remaining service life and the adjusted target valuation model, a value assessment scheme and corresponding value data are generated and sent to the terminal device. This method comprehensively considers the actual usage of smart devices, improving the accuracy of value data prediction for smart devices.

[0058] Next, the technical solutions shown in this application will be described in detail through specific embodiments. It should be noted that the following embodiments may exist alone or in combination with each other, and the same or similar content will not be described again in different embodiments.

[0059] Figure 1 This is a schematic diagram of a scenario provided for an embodiment of this application. For example... Figure 1 As shown, after receiving the evaluation request from the terminal device, the server obtains the usage and maintenance data of the smart device based on the Internet of Things, and obtains the remaining service life based on the usage and maintenance data. It then adjusts the reference valuation model to obtain the final value assessment scheme and corresponding value data sent to the terminal device.

[0060] Furthermore, based on the value assessment scheme and the corresponding value data, the terminal device sends feedback information to the server. The server, based on the content of the feedback information, instructs the recycling personnel to recycle, or sends a new value assessment scheme and the corresponding new value data.

[0061] Figure 2 A flowchart illustrating a data processing method for a smart device provided in this application embodiment. Figure 1 .like Figure 2 As shown, the method includes:

[0062] S201. Receive an evaluation request sent by the terminal device containing the identifier of the smart device, and retrieve previously collected usage and maintenance data of the smart device from the database based on the identifier of the smart device.

[0063] In this step, the current recycling of smart devices mainly relies on existing home appliance recycling software or platforms. Users subjectively select parameters describing the performance of the smart device according to pre-set options, such as lifespan, condition of parts, and device model. The recycling platform then uses simplified calculation models, such as the average market depreciation rate, to predict the value of the smart device and displays it to the user through the software or platform. However, this method relies on the user's subjective choices and lacks consideration of the actual usage of the smart device. Furthermore, by only showing the user the final value data, the user cannot determine whether the value predicted by the simplified calculation model based on the average market depreciation rate is reasonable. If the user provides feedback on the value data, the predicted value data needs to be updated multiple times based on user feedback, reducing recycling efficiency.

[0064] Furthermore, considering that most existing smart devices are deployed in the Internet of Things (IoT) environment, the actual usage of the smart device can be evaluated based on the data of the smart device stored in the IoT. Therefore, this embodiment obtains the usage data and maintenance data of the smart device through the IoT, and predicts the value data of the smart device based on the above two types of data.

[0065] Specifically, when the server receives an evaluation request from the terminal device, it retrieves the device's usage and maintenance data from the database based on the IoT, according to the smart device's identifier included in the evaluation request. This embodiment does not limit the specific parameters included in the usage and maintenance data; they can be selected according to actual conditions. For example, usage data includes: the number of times the smart device has been run, cumulative running time, energy consumption, fault alarm records, etc. Maintenance data includes: maintenance time, maintenance type, replaced parts, etc.

[0066] S202. Based on usage and maintenance data, predict the remaining lifespan of the smart device.

[0067] In this step, the remaining lifespan of a smart device should normally be obtained based on the difference between its expected and actual lifespan. However, if the smart device is used frequently, or has experienced malfunctions and required repairs, its remaining lifespan will be reduced due to usage frequency and malfunctions. Therefore, directly obtaining the remaining lifespan based on the difference between the expected and actual lifespan is unreasonable and requires comprehensive consideration of the actual usage of the smart device. Therefore, this embodiment predicts the remaining lifespan of the smart device based on the acquired usage and repair data.

[0068] Specifically, the initial remaining lifespan of the smart device is obtained. Then, based on usage and maintenance data, multiple performance metrics are acquired to evaluate the smart device's performance, and correction parameters are obtained for each metric. The importance of the function corresponding to each metric in the smart device is analyzed, and the correction parameters are adjusted according to the importance. Finally, the initial remaining lifespan is corrected using the adjusted correction parameters for each metric to obtain the predicted remaining lifespan of the smart device.

[0069] S203. Adjust the values ​​of the influencing factors in the reference valuation model based on usage data and maintenance data to obtain the target valuation model for the intelligent equipment.

[0070] In this step, once the simplified calculation model based on the average market depreciation rate is determined, it will not be adjusted when predicting the value data of different smart devices. However, different types of smart devices, as well as different models of the same type, have different functional characteristics. Therefore, the same usage and maintenance information will have different impacts on the functional characteristics of different smart devices. Thus, a fixed calculation model cannot be used. For example, smart surveillance and smart TVs: smart surveillance is related to home security and privacy, while smart TVs have a weaker connection to home security. Therefore, although the usage and maintenance data of smart surveillance and smart TVs are similar, the aforementioned two types of data have a greater impact on the value data of smart surveillance than on the value data of smart TVs.

[0071] Therefore, this embodiment employs an adjustable reference valuation model, that is, adjusting the values ​​of the influencing factors in the reference valuation model. Specifically, based on the target parameters associated with each influencing factor in the reference valuation model, the impact of usage information and maintenance information on that influencing factor is determined, thereby obtaining adjustment coefficients, and the influencing factor is adjusted according to these adjustment coefficients. After adjusting each influencing factor in the reference valuation model according to the aforementioned method, the final target valuation model used to predict the value data of smart devices is obtained.

[0072] S204. Using the target valuation model and remaining useful life, obtain the value assessment scheme for the smart device and the value data of the smart device corresponding to the value assessment scheme, and send the value assessment scheme and value data to the terminal device.

[0073] In this step, considering the importance users place on the recycling value data of smart devices, and in order to reduce the difficulty of the recycling process, this embodiment abandons the traditional recycling method that only displays value data, and provides a transparent value assessment scheme.

[0074] Furthermore, this embodiment primarily obtains the target valuation model by adjusting the influencing factors in the reference valuation model. Then, based on the remaining useful life, the target valuation model, and the target parameters required by the target valuation model, the value data of the smart device is obtained. Therefore, when explaining the rationality of the predicted value data in this embodiment to the user, it is necessary to combine the target valuation model and the adjusted influencing factors for explanation. Generally, the formulas for predicting value data contained in the target valuation model are long or complex. To facilitate user understanding, this embodiment transforms them into multiple basic relationships and explains each basic relationship.

[0075] Specifically, the target valuation model is transformed into multiple basic relationships;

[0076] Based on the usage data and the maintenance data, parameter description information and valuation description information are generated for each basic relation. The parameter description information is used to indicate the definition of each parameter in each basic relation, and the valuation description information is used to indicate the conditions for setting the influencing factors included in each basic relation.

[0077] A valuation scheme for the intelligent device is generated based on the parameter descriptions and valuation information for each basic relation.

[0078] This embodiment does not impose any limitations on the format of the valuation description information and parameter description information; they can be selected according to the actual situation. For example, when the text content in the valuation description information is determined to be complex, corresponding charts can be generated based on the text content to help users understand the valuation description information.

[0079] Furthermore, from the perspective of protecting user privacy, the usage and maintenance information of smart devices obtained through the Internet of Things may not be comprehensive. Users may be dissatisfied with the received value assessment scheme and value data. Therefore, the server needs to receive feedback information sent by users based on the value assessment scheme and the value data, and process it accordingly. Specifically:

[0080] Recycling information or value feedback information is obtained based on user feedback information based on the aforementioned value assessment scheme and value data;

[0081] If the recycling information is obtained, it will be sent to the recycling terminal to prompt the recycling personnel to recycle the smart device;

[0082] If the value feedback information is obtained, the target valuation model is adjusted based on the value feedback information, new value data is obtained and a new value assessment scheme is generated, and the new value assessment scheme and the new value data are sent to the terminal device.

[0083] This embodiment does not impose any limitations on the recycling method of the smart device, which can be determined according to the user's selection. For example, when it is determined that the information obtained from the feedback is recycling information, the server sends recycling confirmation information containing the recycling method to the terminal device. Based on the feedback information from the terminal device based on the recycling confirmation information, the server determines the recycling method selected by the user and recycles the smart device according to that recycling method.

[0084] The method by which recycling personnel recycle smart devices can be determined based on the actual situation, without any restrictions. For example, when it is determined that the information obtained from the feedback is recycling information, recycling selection information can be sent to the user's terminal, allowing the user to choose their preferred recycling method from multiple recycling options. These recycling methods include, but are not limited to, online valuation and confirmation of recycling, and scheduling door-to-door recycling.

[0085] This application provides a data processing method for smart devices. Based on the usage and maintenance information of the device to be recycled, the remaining service life is predicted, and the influencing factors of a reference valuation model are adjusted. A value assessment scheme and corresponding value data are generated based on the remaining service life and the adjusted target valuation model, and then sent to the terminal device. This method comprehensively considers the actual usage of the smart device, improving the accuracy of value data prediction. Simultaneously, it displays the value data and its basis, increasing the transparency of the valuation process and improving the recycling efficiency of smart devices.

[0086] Figure 3 A flowchart illustrating a data processing method for a smart device provided in this application embodiment. Figure 2 This embodiment details the steps involved in predicting the remaining lifespan of a smart device based on usage and maintenance data. Figure 3 As shown, the method includes:

[0087] S301. Based on the smart device's identifier, query the smart device's usable lifespan; based on usage data, determine the smart device's current usable lifespan; and based on the usable lifespan and current usable lifespan, obtain the smart device's initial remaining lifespan.

[0088] In this step, before predicting the remaining lifespan of the smart device, it is necessary to first obtain the original initial remaining lifespan. Specifically, the factory-set lifespan of the smart device is retrieved based on its identifier. The already used lifespan of the smart device is obtained based on usage data, and the initial remaining lifespan of the smart device is obtained based on the difference between the remaining lifespan and the already used lifespan.

[0089] S302. Based on usage data and maintenance data, obtain multiple indicator parameters for evaluating the performance of smart devices, and obtain the corresponding annual correction value and reference weight for each indicator parameter.

[0090] In this step, the remaining lifespan of the smart device is mainly related to its performance. In other words, the better the current performance of the smart device, the longer its remaining lifespan; the worse the current performance, the shorter its remaining lifespan. Therefore, this embodiment evaluates the performance of the smart device based on usage data and maintenance data, adjusts the initial remaining lifespan based on the evaluated performance, and obtains the remaining lifespan.

[0091] Specifically, based on the functional characteristics of smart devices, the usage data of smart devices is analyzed to obtain at least one corresponding indicator parameter and its corresponding reference weight. Simultaneously, based on the functional characteristics of smart devices, the maintenance data of smart devices is analyzed to obtain at least one corresponding indicator parameter and its corresponding reference weight. This embodiment does not impose any limitations on the selection of indicator parameters. For example, indicator parameters may include: usage frequency, usage mode, failure rate, wear level, etc.

[0092] S303. Obtain device information of smart devices from the database, adjust each reference weight according to the device information, and obtain the target weight corresponding to each indicator parameter.

[0093] In this step, because different types or models of smart devices have different functional characteristics, their performance has a different impact on their lifespan. In other words, the performance metrics used to evaluate smart devices also have different effects. For example, smart surveillance devices are highly relevant to home security, so a higher failure rate is required when recycling them; that is, the failure rate has a higher target weight when determining the remaining lifespan of a smart surveillance device. On the other hand, smart TVs are used to play video content and have a lower relevance to home security, so a lower failure rate is required when recycling smart TVs; that is, the failure rate has a lower target weight when determining the remaining lifespan of a smart TV.

[0094] The lifespan correction parameters and reference weights for each of the aforementioned indicator parameters are determined through analysis of multiple types of smart devices; that is, they are fixed. To make the remaining lifespan obtained based on the indicator parameter corrections more accurate, this embodiment further corrects the reference weights for each indicator parameter according to the functional characteristics of the smart device.

[0095] Specifically, the device information of the smart device is obtained from the database based on the smart device's identifier. The functional characteristics of the smart device are analyzed based on the device information. The reference weights corresponding to each indicator parameter are adjusted based on the analysis results to obtain the corresponding target weights.

[0096] S304. Obtain the adjustment period based on the product of each adjustment period value and the corresponding target weight, and obtain the remaining service life of the smart device based on multiple adjustment periods and the initial service life.

[0097] In this step, after obtaining the annual adjustment value and target weight corresponding to each indicator parameter, the adjustment period for each indicator parameter is first obtained; that is, the adjustment period for the corresponding indicator parameter is obtained by multiplying the annual adjustment value and the target weight. Then, the remaining service life of the smart device is obtained based on the multiple adjustment periods and the pre-obtained initial service life.

[0098] This application provides a data processing method for smart devices. The method calculates the initial remaining service life of the smart device, obtains performance evaluation parameters based on usage and maintenance information, acquires corresponding lifespan correction parameters and reference weights, analyzes the functional characteristics of the smart device to adjust the reference weights, obtains a target weight, and then calculates the remaining service life based on the target weight, lifespan correction parameters, and initial remaining service life. By combining the above methods with the actual usage and functional characteristics of the smart device to predict the remaining service life, the predicted remaining service life becomes more accurate.

[0099] Figure 4 A flowchart illustrating a data processing method for a smart device provided in this application embodiment. Figure 3 This embodiment provides a detailed explanation of the steps for adjusting the values ​​of the influence factors in the reference valuation model. For example... Figure 4 As shown, the method includes:

[0100] S401. Based on the reference valuation model, obtain the target parameters for each influencing factor in the reference valuation model. Based on the functional characteristics of the smart device, analyze each target parameter and obtain the associated parameters from the usage data and the maintenance data.

[0101] In this step, the reference estimation model contains a relational expression used to obtain value data, which itself includes influencing factors related to the target parameter. Therefore, when adjusting the influencing factors in the reference estimation model, it is necessary to determine the target parameter associated with each influencing factor in the reference estimation model, and adjust the values ​​of the corresponding influencing factors based on the impact of smart device usage data and maintenance data on this target parameter.

[0102] Specifically, based on the reference valuation model, the target parameters for each influencing factor are determined in the reference valuation model, and based on the functional characteristics of the smart device, each target parameter is analyzed, and related parameters are obtained from usage data and maintenance data.

[0103] For example, the reference valuation model A is: (Remaining useful life + a × Usage frequency + b × Repair loss) × Original value, where a is the first influencing factor and b is the second influencing factor. The usage data of the smart washing machine includes: Number of uses: 400 times, Usage time: 10 months, Energy efficiency rating: Level 3, and Number of fault alarms: 60 times. The maintenance data includes: Number of repairs: 20 times, Repair type: Cleaning, Replacement parts type, Replacement parts information: Filter, Circuit board.

[0104] The target parameter 'a' associated with the first influencing factor 'a' is 'usage frequency', and the target parameter 'b' associated with the second influencing factor 'b' is 'repair damage value'. Based on the functional characteristics of the washing machine, the correlation between 'usage frequency' and information such as the number of times and duration of operation of the smart washing machine is analyzed, and the correlation between 'repair damage value' and the number of repairs and repair operations of the smart washing machine is analyzed. Therefore, the correlation parameters for 'usage frequency' obtained from the usage data are: number of uses: 400 times, usage time: 10 months; the correlation parameters for 'repair damage value' obtained from the repair data are: number of repairs: 20 times, replaced parts information: filter, circuit board.

[0105] S402. Based on the functional characteristics of the smart device, obtain the correlation degree between each associated parameter and the corresponding target parameter, and obtain the corresponding correlation weight according to the correlation degree.

[0106] In this step, similar to calculating the remaining lifespan of the smart device, when adjusting each influencing factor, it is also necessary to determine the degree of influence of each associated parameter on the target parameter, so as to make the adjusted values ​​of the influencing factors more reasonable.

[0107] Specifically, the functional characteristics of the smart device are analyzed to determine the correlation degree between each associated parameter and the corresponding target parameter, and the correlation weight corresponding to each associated parameter is determined according to the correlation degree range to which the correlation degree belongs.

[0108] S403. Based on the numerical range of each associated parameter, obtain the adjustment coefficient of each associated parameter, and adjust the corresponding influence factor based on multiple adjustment coefficients and the associated weight of each adjustment coefficient.

[0109] In this step, once the correlation weight corresponding to each correlation parameter is determined, the corresponding influence factor can be adjusted according to the correlation parameter.

[0110] Specifically, based on the value of each correlation parameter, the numerical range to which that value belongs is obtained, and then the adjustment coefficient corresponding to that correlation parameter is obtained based on the numerical range. The product of each adjustment coefficient and its corresponding correlation weight is obtained, and then the corresponding influence factor is adjusted based on multiple products.

[0111] This application provides a data processing method for smart devices. Based on the target parameters associated with influencing factors in a reference valuation model, the method obtains associated parameters from usage and maintenance data, and acquires association weights based on the correlation between the associated parameters and the target parameters. The method then adjusts the corresponding influencing factors according to the adjustment coefficients and association weights of the associated parameters to obtain the target valuation model. By setting a matching target valuation model based on the actual usage of the smart device, the obtained value data becomes more reasonable.

[0112] Figure 5 This is a schematic diagram of the structure of a data processing control device for a smart device provided in an embodiment of this application. Figure 5 As shown, the data processing and control device of the intelligent device includes: an acquisition module 501, a prediction module 502, an adjustment module 503, and an estimation module 504, wherein:

[0113] The acquisition module 501 is used to receive an evaluation request sent by the terminal device containing the identifier of the smart device, and to retrieve previously collected usage data and maintenance data of the smart device from the database based on the identifier of the smart device.

[0114] The prediction module 502 is used to predict the remaining service life of the smart device based on the usage data and the maintenance data.

[0115] The adjustment module 503 is used to adjust the values ​​of the influencing factors in the reference valuation model based on the usage data and the maintenance data, so as to obtain the target valuation model of the smart device.

[0116] The valuation module 504 is used to obtain a valuation scheme for the smart device and the value data of the smart device corresponding to the valuation scheme by using the target valuation model and the remaining useful life, and to send the valuation scheme and the value data to the terminal device.

[0117] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 60 includes at least one processor 601 and a memory 602. Optionally, the intelligent device 60 also includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0118] The memory 602 is used to store computer-executed instructions;

[0119] The at least one processor 601 is configured to execute computer execution instructions stored in the memory 602, causing the at least one processor 601 to perform the method as described in any of the preceding descriptions.

[0120] At least one processor 601 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0121] Optionally, in specific implementations, the processor 601 and memory 602 are implemented independently. In this case, the processor 601 and memory 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0122] Optionally, in a specific implementation, if the processor 601 and the memory 602 are integrated on a single chip, the processor 601 and the memory 602 can communicate through an internal interface.

[0123] This application also provides a computer storage medium storing computer execution instructions, which, when executed by a processor, implement the aforementioned technical solution.

[0124] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The computer-readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0125] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Alternatively, the readable storage medium can be an integral part of the processor. Both the processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the control device of a garment handling apparatus.

[0126] The division of units described herein is merely a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

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

[0128] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0129] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0130] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0131] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A data processing method for a smart device, characterized in that, The method includes: The system receives an evaluation request from a terminal device containing the identifier of the smart device, and retrieves previously collected usage and maintenance data of the smart device from the database based on the identifier of the smart device. Based on the usage data and the maintenance data, predict the remaining lifespan of the smart device; Based on the usage data and the maintenance data, the values ​​of the influencing factors in the reference valuation model are adjusted to obtain the target valuation model for the smart device. Using the target valuation model and the remaining useful life, a value assessment scheme for the smart device and the value data of the smart device corresponding to the value assessment scheme are obtained, and the value assessment scheme and the value data are sent to the terminal device.

2. The method according to claim 1, characterized in that, The step of predicting the remaining lifespan of the smart device based on the usage data and the maintenance data includes: Based on the identifier of the smart device, query the remaining service life of the smart device, and based on the usage data, determine the completed service life of the smart device; Based on the available lifespan and the already used lifespan, the initial remaining lifespan of the smart device is obtained; Based on the usage data and the maintenance data, obtain multiple indicator parameters for evaluating the performance of the smart device; The initial remaining lifespan is adjusted based on the multiple indicator parameters to obtain the remaining lifespan of the smart device.

3. The method according to claim 2, characterized in that, The step of correcting the initial remaining lifespan based on the multiple indicator parameters to obtain the remaining lifespan of the smart device includes: Obtain the year-based adjustment value and reference weight for each indicator parameter; Retrieve device information of the smart device from the database; Each reference weight is corrected based on the device information to obtain the target weight corresponding to each indicator parameter; The adjustment period is obtained by multiplying each adjustment period value by the corresponding target weight, and the remaining lifespan of the smart device is obtained by combining the multiple adjustment periods with the initial lifespan.

4. The method according to claim 3, characterized in that, The step of adjusting the values ​​of influencing factors in the reference valuation model based on the usage data and the maintenance data to obtain the target valuation model for the smart device includes: Based on the reference valuation model, obtain the target parameters for each influencing factor corrected in the reference valuation model; Based on the functional characteristics of the intelligent device, each target parameter is analyzed, and related parameters are obtained from the usage data and the maintenance data; The target valuation model for the smart device is obtained by adjusting the corresponding influencing factors based on the associated parameters.

5. The method according to claim 4, characterized in that, The step of adjusting the corresponding influence factor according to the correlation parameter includes: Based on the functional characteristics of the smart device, the correlation degree between each associated parameter and the corresponding target parameter is obtained, and the corresponding correlation weight is obtained according to the correlation degree. Based on the numerical range of each associated parameter, obtain the adjustment coefficient for each associated parameter, and adjust the corresponding influence factor based on multiple adjustment coefficients and the associated weights corresponding to each adjustment coefficient.

6. The method according to any one of claims 1-5, characterized in that, The method of obtaining a value assessment scheme for the smart device using the target valuation model and the remaining useful life includes: The target valuation model is transformed into multiple basic relationships; Based on the usage data and the maintenance data, parameter description information and valuation description information are generated for each basic relation. The parameter description information is used to indicate the definition of each parameter in each basic relation, and the valuation description information is used to indicate the conditions for setting the influencing factors included in each basic relation. A valuation scheme for the intelligent device is generated based on the parameter descriptions and valuation information for each basic relation.

7. The method according to claim 1, characterized in that, After sending the value assessment scheme and the value data to the terminal device, the method further includes: Recycling information or value feedback information is obtained based on user feedback information based on the aforementioned value assessment scheme and value data; If the recycling information is obtained, it will be sent to the recycling terminal to prompt the recycling personnel to recycle the smart device; If the value feedback information is obtained, the target valuation model is adjusted based on the value feedback information, new value data is obtained and a new value assessment scheme is generated, and the new value assessment scheme and the new value data are sent to the terminal device.

8. A data processing and control device for an intelligent device, characterized in that, include: The module includes an acquisition module, a prediction module, an adjustment module, and a valuation module, among which: The acquisition module is used to receive an evaluation request sent by the terminal device containing the identifier of the smart device, and to retrieve previously collected usage data and maintenance data of the smart device from the database based on the identifier of the smart device. The prediction module is used to predict the remaining lifespan of the smart device based on the usage data and the maintenance data. The adjustment module is used to adjust the values ​​of the influencing factors in the reference valuation model based on the usage data and the maintenance data, so as to obtain the target valuation model of the smart device. The valuation module is used to obtain a valuation scheme for the smart device and the value data of the smart device corresponding to the valuation scheme by using the target valuation model and the remaining useful life, and to send the valuation scheme and the value data to the terminal device.

9. An electronic device, characterized in that, include: At least one processor and memory, wherein: The memory is used to store computer-executed instructions; The at least one processor is configured to execute computer execution instructions stored in the memory, such that the at least one processor performs the method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer execution instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 7.

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