Abnormal Judgment Method, Device and Washing Machine for Washing Machine

Abstract

The present application belongs to the technical field of washing machines, and specifically relates to a method, device and washing machine for judging abnormality of washing machines. The present application aims to solve the problem of low management and maintenance efficiency of existing self-service washing machines. The method for judging abnormality of washing machines provided in the present application includes: obtaining user usage data and device data of the washing machine; determining the operation data of the washing machine based on the user usage data and device data; judging whether the washing machine has abnormality based on the operation data; if so, obtaining the location information of the washing machine from the blockchain based on the device identification code of the washing machine, and generating prompt information of the washing machine based on the location information and operation data, so as to realize real-time knowledge of the operation status of each self-service washing machine and improve the management efficiency of the washing machine; at the same time, when it is judged that the washing machine has abnormality, prompt information can be generated based on the location information stored in the blockchain, so as to quickly locate the abnormal washing machine based on the location information, thereby improving the efficiency of washing machine maintenance.

Description

Technical Field

[0001] This application belongs to the technical field of washing machines, and particularly relates to a method and device for judging abnormalities of a washing machine and a washing machine. Background Art

[0002] The appearance of washing machines has brought a lot of convenience to the public. For some mobile populations, such as students and renters, self-service washing machines are favored by the mobile population for their unique characteristics of being economical and affordable.

[0003] Existing self-service washing machines often rely on the application programs or WeChat mini-programs of self-service washing machines. Users scan the QR codes of self-service washing machines to conduct laundry services. For the merchants of self-service washing machines, they cannot effectively understand the usage situation of the washing machines, resulting in low efficiency of the maintenance and management of the washing machines and affecting the user experience. Summary of the Invention

[0004] In order to solve the above problems in the prior art, that is, to solve the problem of low efficiency in the management and maintenance of existing self-service washing machines, this application provides a method and device for judging abnormalities of a washing machine and a washing machine. The method for judging abnormalities of the washing machine determines the operating state of the washing machine through the usage data and device data of the washing machine, and then judges whether the washing machine has any abnormalities, so as to perform maintenance according to the prompt information related to the abnormalities, realizing real-time monitoring of the self-service washing machine, improving the efficiency of the maintenance and management of the self-service washing machine, and improving the user experience.

[0005] In a first aspect, an embodiment of this application provides a method for judging abnormalities of a washing machine, and the method includes:

[0006] Obtain the user usage data and device data of the washing machine, where the user usage data is generated based on a user-side application program; determine the operating data of the washing machine according to the user usage data and device data; judge whether the washing machine has any abnormalities according to the operating data; if so, obtain the location information of the washing machine from the blockchain according to the device identification code of the washing machine, and generate the prompt information of the washing machine according to the location information and operating data.

[0007] Optionally, the user usage data is generated by the user based on the user-side application program by scanning the device identification code set on the washing machine.

[0008] Optionally, the operating data includes the device status. Judging whether the washing machine has any abnormalities according to the operating data includes:

[0009] When the device status of the washing machine is a preset fault status, it is determined that the washing machine has an abnormality.

[0010] Correspondingly, according to the position information and operation data, prompt information of the washing machine is generated, including: generating equipment fault prompt information of the washing machine according to the position information and the preset fault state.

[0011] Optionally, the operation data includes equipment status and income amount. Determining whether there is an abnormality in the washing machine according to the operation data includes:

[0012] When the equipment status of the washing machine is in a normal state and the income amount of the washing machine is lower than a first preset amount, it is determined that there is an abnormality in the washing machine.

[0013] Correspondingly, according to the position information and operation data, prompt information of the washing machine is generated, including: generating a prompt information of too low income of the washing machine according to the position information and the income amount.

[0014] Optionally, the operation data includes equipment status, equipment type and income amount. Determining whether there is an abnormality in the washing machine according to the operation data includes:

[0015] When the equipment status of the washing machine is in a normal state, the equipment type of the washing machine is an infrequently used type, and the income amount of the washing machine is lower than a second preset amount, or when the equipment status of the washing machine is in a normal state, the equipment type of the washing machine is a frequently used type, and the income amount of the washing machine is lower than a third preset amount, it is determined that there is an abnormality in the washing machine, where the third preset amount is higher than the second preset amount.

[0016] Correspondingly, according to the position information and operation data, prompt information of the washing machine is generated, including:

[0017] Generating a prompt information of too low income of the washing machine according to the position information and the income amount.

[0018] Optionally, the method for judging the abnormality of the washing machine further includes:

[0019] When the equipment status of the washing machine is in a normal state, the equipment type of the washing machine is an infrequently used type, and the income amount of the washing machine is higher than a fourth preset amount, the position information of the washing machine is obtained from the blockchain according to the equipment identification code of the washing machine, where the fourth preset amount is higher than the second preset amount; the number of newly added washing machines is determined according to the income amount; and a washing machine addition prompt information is generated according to the position information and the number of newly added washing machines.

[0020] Optionally, after determining the operation data of the washing machine according to the user usage data and equipment data, the method further includes:

[0021] Upload the operation data, device data, and user usage data of the washing machine to the blockchain according to the device identification code of the washing machine; and / or, send the operation data of the washing machine to the merchant application program bound to the washing machine, where the merchant application program is bound to the washing machine through the device identification code and barcode of the washing machine.

[0022] In a second aspect, an embodiment of the present application further provides a washing machine abnormality determination device, which includes:

[0023] A usage data acquisition module, configured to acquire user usage data and device data of the washing machine, where the user usage data is generated based on a user-side application program; an operation data determination module, configured to determine the operation data of the washing machine according to the user usage data and the device data; an abnormality determination module, configured to determine whether the washing machine has an abnormality according to the operation data; a prompt information generation module, configured to, if the washing machine has an abnormality, obtain the location information of the washing machine from the blockchain according to the device identification code of the washing machine, and generate prompt information of the washing machine according to the location information and the operation data.

[0024] Optionally, the user usage data is generated by the user based on the user-side application program by scanning the device identification code provided on the washing machine.

[0025] Optionally, the operation data includes the device status, and the abnormality determination module is specifically configured to:

[0026] When the device status of the washing machine is a preset fault status, it is determined that the washing machine has an abnormality.

[0027] Correspondingly, the prompt information generation module is specifically configured to:

[0028] Generate device fault prompt information of the washing machine according to the location information and the preset fault status.

[0029] Optionally, the operation data includes the device status and the income amount, and the abnormality determination module is specifically configured to:

[0030] When the device status of the washing machine is a normal status and the income amount of the washing machine is lower than a first preset amount, it is determined that the washing machine has an abnormality.

[0031] Correspondingly, the prompt information generation module is specifically configured to:

[0032] Generate a prompt information of too low income of the washing machine according to the location information and the income amount.

[0033] Optionally, the operation data includes device status, device type, and revenue amount. The anomaly judgment module is specifically configured to:

[0034] When the device status of the washing machine is normal, the device type of the washing machine is an infrequently used type, and the revenue amount of the washing machine is lower than a second preset amount, or when the device status of the washing machine is normal, the device type of the washing machine is a frequently used type, and the revenue amount of the washing machine is lower than a third preset amount, it is determined that the washing machine has an anomaly, where the third preset amount is higher than the second preset amount.

[0035] Correspondingly, the prompt information generation module is specifically configured to:

[0036] Generate a prompt message indicating that the revenue of the washing machine is too low according to the location information and the revenue amount.

[0037] Optionally, the washing machine anomaly judgment device further includes:

[0038] A second prompt information generation module, configured to, when the device status of the washing machine is normal, the device type of the washing machine is an infrequently used type, and the revenue amount of the washing machine is higher than a fourth preset amount, obtain the location information of the washing machine from the blockchain according to the device identification code of the washing machine, where the fourth preset amount is higher than the second preset amount; determine the number of newly added washing machines according to the revenue amount; and generate a prompt message indicating an increase in the washing machine according to the location information and the number of newly added washing machines.

[0039] Optionally, the washing machine anomaly judgment device further includes:

[0040] A data uploading to blockchain module, configured to upload the operation data, device data, and user usage data of the washing machine to the blockchain according to the device identification code of the washing machine after determining the operation data of the washing machine according to the user usage data and device data; and / or, a data sending module, configured to send the operation data of the washing machine to a merchant application program bound to the washing machine after determining the operation data of the washing machine according to the user usage data and device data, where the merchant application program is bound to the washing machine through the device identification code and barcode of the washing machine.

[0041] In a third aspect, an embodiment of the present application further provides a server, where the server includes a memory and at least one processor; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, so that the at least one processor executes the washing machine anomaly judgment method provided in any embodiment of the present application.

[0042] Fourthly, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the washing machine anomaly determination method provided in any embodiment of the present application.

[0043] Those skilled in the art can understand that the washing machine anomaly determination method, device, and washing machine provided in the embodiments of the present application determine the operating data of the washing machine through the user usage data and device data of the washing machine, and automatically determine whether there is an anomaly in the washing machine based on the operating data. If there is an anomaly, the location information of the washing machine is obtained through the blockchain, and a prompt message is generated based on the location information and the operating data, so that relevant personnel can quickly locate the abnormal washing machine and the cause of the anomaly according to the prompt message, improving the management and maintenance efficiency of self-service washing machines. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The preferred embodiments of the washing machine anomaly determination method, device, and washing machine of the present application will be described below with reference to the accompanying drawings. The accompanying drawings are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present application. The drawings are as follows:

[0045] Figure 1 It is an application scenario diagram of the washing machine anomaly determination method provided in an embodiment of the present application;

[0046] Figure 2 It is a flowchart of the washing machine anomaly determination method provided in an embodiment of the present application;

[0047] Figure 3 It is a flowchart of the washing machine anomaly determination method provided in another embodiment of the present application;

[0048] Figure 4 It is a schematic structural diagram of the washing machine anomaly determination device provided in an embodiment of the present application;

[0049] Figure 5 It is a schematic structural diagram of the server provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0051] The following uses specific embodiments to elaborate in detail on the technical solution of this application and how the technical solution of this application solves the above technical problems. The following several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be elaborated in some embodiments. The embodiments of this application will be described below in conjunction with the accompanying drawings.

[0052] The application scenarios of the embodiments of this application are explained below:

[0053] Figure 1 It is an application scenario diagram of the washing machine anomaly judgment method provided by the embodiments of this application. As Figure 1 shown, the washing machine anomaly judgment method provided by the embodiments of this application can run on a server. The self-service washing machine 110 is a shared washing machine, mainly set in places such as university dormitories or apartments to facilitate students and tenants to use the washing machine. There is often a payment code 111 set on the existing self-service washing machine 110 to facilitate users to pay for the laundry service. Or users can determine the laundry mode of the self-service washing machine 110 and make payments by installing a dedicated application (APP) or WeChat mini-program on the user terminal 120 to implement the corresponding laundry service. The merchant of the self-service washing machine 110 can view the income situation of each self-service washing machine 110 bound to it through the merchant application in the merchant terminal 130.

[0054] However, in the above management method of the self-service washing machine, for the merchants of the self-service washing machine, they cannot timely and effectively understand the specific operation situation of the self-service washing machine. They often need to conduct regular on-site inspections to determine the operation situation of each self-service washing machine, resulting in low management and maintenance efficiency of the self-service washing machine.

[0055] In response to the above problems, the main idea of the washing machine anomaly judgment method provided by the embodiments of this application is: determine the operation situation of the washing machine through the user usage data and device data of the washing machine. When it is determined that the washing machine is abnormal according to the operation situation, quickly locate the abnormal washing machine through the location information stored in the blockchain for corresponding management or maintenance, improving the management and maintenance efficiency of the washing machine. At the same time, obtaining location information based on the blockchain improves the security of information storage.

[0056] Figure 2 It is a flowchart of the washing machine anomaly judgment method provided by the embodiments of this application. As Figure 2 shown, the washing machine anomaly judgment method includes the following steps:

[0057] Step S201, obtain the user usage data and device data of the washing machine.

[0058] Among them, the user usage data is generated based on the user-side application, and the device data is the data corresponding to each hardware of the washing machine, such as the engine speed, the output data of the water level sensor, the status of the water inlet, etc. The washing machine can be a self-service washing machine, and a self-service washing machine is usually a fully automatic washing machine.

[0059] Specifically, the user usage data of the washing machine can be obtained based on the user-side application or the WeChat mini-program. The user usage data can include user information, washing mode, payment amount, washing date or time, clothing weight, clothing type and other data.

[0060] Specifically, the device data of the washing machine can be obtained based on each sensor or detection module of the washing machine. The sensors of the washing machine can include a water level sensor, a photosensitive sensor, a cloth quantity sensor, a speed sensor, etc. The detection module can include the detection circuits corresponding to each circuit of the washing machine to determine whether there is a fault in the circuit of the washing machine.

[0061] Further, the user can scan the QR code set on the washing machine in the laundry room or the washing machine room through the user-side application or the WeChat mini-program, and then enter the page corresponding to the washing machine in this washing machine room to determine the washing mode, determine the water consumption, and pay the relevant amount and other operations. The application or the WeChat mini-program can then determine the target washing machine according to the user's operations and the running conditions of each washing machine in the laundry room, and can also notify the user of the number of the target washing machine through relevant prompts, such as voice prompts, so as to wash the user's clothes through the target washing machine. If the QR code scanned by the user is the QR code set on the washing machine, the washing machine corresponding to the QR code is directly determined as the target washing machine.

[0062] Further, the user usage data and device data of the washing machine can be uploaded to the cloud server in real time, and then sent to the merchant application terminal of the washing machine merchant for display through the cloud server. The user usage data and device data of the washing machine can also be uploaded to the blockchain for storage. When the merchant deletes the user usage data or device data stored in the cloud server through the merchant terminal, the deleted data can be viewed through the blockchain.

[0063] Optionally, the user usage data is generated by the user based on the user-side application by scanning the device identification code set on the washing machine.

[0064] Among them, the device identification code can be set at any position of the washing machine, specifically it can be the upper left corner, upper right corner, center or other positions on the front of the washing machine. The device identification code can be a two-dimensional code, bar code or other identification codes that can be recognized by the user through the user terminal. The user scans the device identification code through the relevant application or WeChat mini-program on the user terminal, and can enter the business page corresponding to the washing machine. The user can select the settings of appropriate washing machine modes, water consumption, washing times and other parameters from this page, and can also make a payment through this page. After the payment is successful, the user can use this washing machine to wash clothes.

[0065] By setting the device code instead of the payment code, the security of user payment is improved, effectively preventing the replacement of the payment code and causing losses to the user.

[0066] Step S202, determine the operation data of the washing machine according to the user usage data and device data.

[0067] Among them, the operation data can be used to represent that the state of the washing machine is normal operation or abnormal operation. The operation data can include the total income amount, average income amount, device status and device type of the washing machine in a preset time period. The device type can be divided into common types and uncommon types, and the device status can include normal status and abnormal status.

[0068] Specifically, the device status and device type of the washing machine can be determined according to the device data, and the income amount of the washing machine in a preset time period can be determined according to the user usage data. Among them, the preset time period can be one week, 15 days or other time periods.

[0069] Furthermore, the device type can be determined according to the opening frequency in the device data. When the opening frequency of the washing machine is greater than the preset frequency, it is determined that the device type of the washing machine is a common type. When the device data of the washing machine contains fault data, it is determined that the device status of the washing machine is abnormal.

[0070] Optionally, after determining the operation data of the washing machine according to the user usage data and device data, the method further includes:

[0071] According to the device identification code of the washing machine, upload the operation data, device data and user usage data of the washing machine to the blockchain; and / or send the operation data of the washing machine to the merchant application program bound to the washing machine, where the merchant application program is bound to the washing machine through the device identification code and bar code of the washing machine.

[0072] Among them, the bar code can be set at the lower right corner at the back of the washing machine and can be a 22-digit bar code. The bar code can include the production date, model, batch number, etc. of the washing machine.

[0073] Specifically, the merchant of the washing machine can pre-bind each washing machine under its name in the merchant application program through the device identification code and the barcode, so as to facilitate the access to the operation data of each washing machine.

[0074] Step S203, determine whether the washing machine has an abnormality according to the operation data.

[0075] Specifically, it can be determined whether the washing machine has an abnormality according to the device type, income amount and device status of the washing machine. When the device status of the washing machine is an abnormal status, it is determined that the washing machine has an abnormality, and the abnormality type can be a device abnormality. When the device status is a normal status, the preset income range of the washing machine is determined according to the device type of the washing machine. When the income amount of the washing machine exceeds the preset income range, it is determined that the washing machine has an abnormality, and the abnormality type can be an income abnormality.

[0076] Furthermore, the income abnormality can include two types: too low income abnormality and too high income abnormality. The preset income range includes an income lower limit and an income upper limit. When the income amount of the washing machine is less than the income lower limit, it is determined that the washing machine has an abnormality, and the abnormality type is a too low income abnormality; when the income amount of the washing machine is higher than the income upper limit, it is determined that the washing machine has an abnormality, and the abnormality type is a too high income abnormality.

[0077] Step S204, if so, obtain the location information of the washing machine from the blockchain according to the device identification code of the washing machine, and generate a prompt message for the washing machine according to the location information and the operation data.

[0078] Among them, the location information of the washing machine can be the geographical location information of the washing machine, and its form can be coordinates, longitude and latitude, etc.

[0079] Since the merchants of self-service washing machines often set up laundries in multiple places in schools or apartments to meet the laundry needs of users in various locations. Therefore, when the device has an abnormality, it is necessary to provide the merchant with the location information of the abnormal washing machine, so that the merchant can quickly locate the location of the washing machine and perform corresponding inspections and maintenance.

[0080] Specifically, the corresponding relationship between the device identification code and the location information of each washing machine can be stored in the blockchain, so as to obtain the location information of the washing machine from the blockchain according to the corresponding relationship and the device identification code of the washing machine.

[0081] Specifically, the prompt message can also include the abnormality type of the washing machine, the washing machine number, etc.

[0082] Optionally, the operation data includes the device status. Determining whether there is an abnormality in the washing machine according to the operation data includes:

[0083] When the device status of the washing machine is a preset fault status, it is determined that there is an abnormality in the washing machine; correspondingly, according to the location information and the operation data, a prompt message for the washing machine is generated, including: generating a device fault prompt message for the washing machine according to the location information and the preset fault status.

[0084] Among them, the preset fault status can be any one of the fault statuses in the pre-set fault status table, such as unable to start, the agitator cannot rotate normally, the motor rotates abnormally, the timer fails, the water inlet is too slow, the drainage is too slow, the water level detection fails, etc.

[0085] Exemplarily, when a fault occurs in the washing machine, such as unable to start, the prompt message can be "The washing machine No. A032 cannot start, and its location is: the second laundry room in Building 11 of XX University". When there is no fault in the washing machine device, but the income amount of this washing machine is much lower than expected, the prompt message can be "The income of washing machine No. B099 is too low, and its location is: the laundry room in Unit 2 of Building 14 of XX Apartment".

[0086] The method for judging the abnormality of the washing machine provided by the embodiment of the present application determines the operation data of the washing machine through the user usage data and device data of the washing machine, and automatically judges whether there is an abnormality in the washing machine based on this operation data. If there is an abnormality, the location information of the washing machine is obtained through the blockchain, and a prompt message is generated based on this location information and the operation data, so that relevant personnel can quickly locate the abnormal washing machine and the cause of the abnormality according to this prompt message, improving the management and maintenance efficiency of the self-service washing machine.

[0087] Figure 3 is a flowchart of the method for judging the abnormality of the washing machine provided by another embodiment of the present application. This embodiment further refines steps S203 and S204 on the basis of Figure 2 the embodiment shown. As Figure 3 shown, the method for judging the abnormality of the washing machine provided by this embodiment includes the following steps:

[0088] Step S301, obtaining the clothing data of the laundry to be washed of the target user.

[0089] Among them, the user usage data is generated by the user based on the user-side application program by scanning the device identification code set on the washing machine.

[0090] Step S302, determining the operation data of the washing machine according to the user usage data and the device data.

[0091] Among them, the operating data includes the device status and the income amount, and the operating data may also include the device type.

[0092] Specifically, the income amount can be the daily income amount, the weekly income amount, or the income amount for other preset time periods, and can also be the average income amount for the preset time period. The device type can include two types: common type and uncommon type. The device status can include two types: normal status and abnormal status.

[0093] Step S303, when the device status of the washing machine is in the normal status and the income amount of the washing machine is lower than the first preset amount, it is determined that the washing machine has an abnormality.

[0094] Among them, when the device status is in the normal status, it means that all the hardware modules of the washing machine are in the normal status and no faults have occurred. The first preset amount can be set by the merchant himself or determined by the historical income amount.

[0095] Specifically, it can be determined that the washing machine has an abnormality when the total income amount within the preset time period of the washing machine is lower than the first preset amount, or when the daily income amount of the washing machine is lower than the first preset amount and lasts for the set number of days. Among them, the set number of days can be two days, three days, or other values.

[0096] Furthermore, when the device status of the washing machine is in the normal status and the income amount of the washing machine is lower than the first preset amount, it is determined that the abnormal type of the washing machine is the first low-income abnormal type.

[0097] Step S304, when the device status of the washing machine is in the normal status, the device type of the washing machine is the uncommon type, and the income amount of the washing machine is lower than the second preset amount, or when the device status of the washing machine is in the normal status, the device type of the washing machine is the common type, and the income amount of the washing machine is lower than the third preset amount, it is determined that the washing machine has an abnormality.

[0098] Among them, the third preset amount is higher than the second preset amount.

[0099] Specifically, the second preset amount and the third preset amount can be set by the merchant himself or determined by the historical income amounts of each washing machine with the same device type as the washing machine.

[0100] Furthermore, when the device status of the washing machine is in the normal status, the device type of the washing machine is the uncommon type, and the income amount of the washing machine is lower than the second preset amount, it is determined that the abnormal type of the washing machine is the second low-income abnormal type. When the device status of the washing machine is in the normal status, the device type of the washing machine is the common type, and the income amount of the washing machine is lower than the third preset amount, it is determined that the abnormal type of the washing machine is the third low-income abnormal type.

[0101] Step S305: Generate a low-income prompt message for the washing machine based on the location information and the income amount.

[0102] Among them, the low-income prompt message may include the abnormal type, the income amount, and the location information of the washing machine. It may also include the serial number of the washing machine.

[0103] Exemplarily, the low-income prompt message may be: The income of washing machine C026 is too low. The average daily income in the recent week is lower than 500 yuan, and the location is: the laundry room of the first apartment in University A.

[0104] Step S306: When the device status of the washing machine is normal, the device type of the washing machine is an infrequently used type, and the income amount of the washing machine is higher than the fourth preset amount, obtain the location information of the washing machine from the blockchain according to the device identification code of the washing machine.

[0105] Among them, the fourth preset amount is higher than the second preset amount. The fourth preset amount may be the income upper limit of the infrequently used type of washing machine.

[0106] Specifically, when the washing machine is an infrequently used type of washing machine, but its daily income amount is higher than its corresponding income upper limit, that is, the fourth preset amount, it indicates that the number of washing machines in the corresponding area of the current washing machine is insufficient, and a certain number of washing machines need to be added to this area. Therefore, it is necessary to obtain the location information corresponding to this washing machine from the blockchain.

[0107] Step S307: Determine the number of new washing machines to be added according to the income amount.

[0108] Specifically, according to the location information, the target laundry room can be obtained, and then the income amounts of each washing machine in the target laundry room can be obtained, and then the number of new washing machines to be added can be determined according to each income amount.

[0109] Step S308: Generate a washing machine addition prompt message according to the location information and the number of new washing machines to be added.

[0110] Specifically, the washing machine addition prompt message includes the number of new washing machines to be added and the location information. It may also include the model of the new washing machines.

[0111] Exemplarily, the washing machine addition prompt message may be: The third laundry room in University X needs to add 3 A-model washing machines.

[0112] Furthermore, the above prompt messages such as the washing machine addition prompt message, the equipment failure prompt message, and the low-income prompt message can also be sent to the merchant client for display through the merchant client.

[0113] In this embodiment, the operation data of the washing machine is determined based on the user usage data and device data of the washing machine, and the operation data can be displayed in real time, facilitating the merchant to understand the operation status of the washing machine. When the device status in the operation data is in a normal state, the abnormal type of the washing machine is automatically judged based on the income amount and device type in the operation data, and the location information of the washing machine is obtained through the blockchain. Based on the location information, operation data and abnormal type, corresponding prompt information is generated, so that relevant personnel can quickly locate the abnormal washing machine and the cause of the abnormality according to the prompt information, improving the management and maintenance efficiency of the self-service washing machine. At the same time, when the income of the washing machine is too high, the merchant can also be reminded to add washing machines through the corresponding prompt information to meet the laundry needs of users and increase the merchant's income.

[0114] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes various media that can store program codes such as ROM, RAM, magnetic disk or optical disc.

[0115] Figure 4 is a schematic structural diagram of a washing machine abnormality judgment device provided by an embodiment of the present application, as Figure 4 shown, the washing machine abnormality judgment device includes: a usage data acquisition module 410, an operation data determination module 420, an abnormality judgment module 430, and a prompt information generation module 440.

[0116] Among them, the usage data acquisition module 410 is used to acquire the user usage data and device data of the washing machine, wherein the user usage data is generated based on a user-side application program; the operation data determination module 420 is used to determine the operation data of the washing machine according to the user usage data and device data; the abnormality judgment module 430 is used to judge whether the washing machine has an abnormality according to the operation data; the prompt information generation module 440 is used to, if the washing machine has an abnormality, obtain the location information of the washing machine from the blockchain according to the device identification code of the washing machine, and generate the prompt information of the washing machine according to the location information and operation data.

[0117] Optionally, the user usage data is generated by the user based on the user-side application program by scanning the device identification code set on the washing machine.

[0118] Optionally, the operation data includes a device status, and the abnormality judgment module 430 is specifically used for:

[0119] When the device state of the washing machine is a preset fault state, it is determined that the washing machine has an abnormality.

[0120] Correspondingly, the prompt message generation module 440 is specifically configured to:

[0121] Generate a device fault prompt message for the washing machine according to the position information and the preset fault state.

[0122] Optionally, the operation data includes the device state and the income amount. The abnormality judgment module 430 is specifically configured to:

[0123] When the device state of the washing machine is a normal state and the income amount of the washing machine is lower than a first preset amount, it is determined that the washing machine has an abnormality.

[0124] Correspondingly, the prompt message generation module 440 is specifically configured to:

[0125] Generate an income - too - low prompt message for the washing machine according to the position information and the income amount.

[0126] Optionally, the operation data includes the device state, the device type, and the income amount. The abnormality judgment module 430 is specifically configured to:

[0127] When the device state of the washing machine is a normal state, the device type of the washing machine is an infrequently - used type, and the income amount of the washing machine is lower than a second preset amount, or when the device state of the washing machine is a normal state, the device type of the washing machine is a frequently - used type, and the income amount of the washing machine is lower than a third preset amount, it is determined that the washing machine has an abnormality, where the third preset amount is higher than the second preset amount.

[0128] Correspondingly, the prompt message generation module 440 is specifically configured to:

[0129] Generate an income - too - low prompt message for the washing machine according to the position information and the income amount.

[0130] Optionally, the washing machine abnormality judgment device further includes:

[0131] A second prompt message generation module, configured to, when the device state of the washing machine is a normal state, the device type of the washing machine is an infrequently - used type, and the income amount of the washing machine is higher than a fourth preset amount, obtain the position information of the washing machine from the blockchain according to the device identification code of the washing machine, where the fourth preset amount is higher than the second preset amount; determine the number of newly added washing machines according to the income amount; and generate a washing - machine - addition prompt message according to the position information and the number of newly added washing machines.

[0132] Optionally, the washing machine anomaly determination device further includes:

[0133] A data uploading module to upload the operation data, device data, and user usage data of the washing machine to the blockchain according to the device identification code of the washing machine after determining the operation data of the washing machine based on the user usage data and device data; and / or, a data sending module to send the operation data of the washing machine to the merchant application bound to the washing machine after determining the operation data of the washing machine based on the user usage data and device data, where the merchant application is bound to the washing machine through the device identification code and barcode of the washing machine.

[0134] The washing machine anomaly determination device provided by the embodiments of the present application can execute the washing machine anomaly determination method provided by any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0135] Figure 5 is a schematic structural diagram of a server provided by an embodiment of the present application, as Figure 5 shown, the server includes: a memory 510 and at least one processor 520.

[0136] Wherein, the computer program is stored in the memory 510 and is configured to be executed by the processor 520 to implement the washing machine anomaly determination method provided by any one of the corresponding embodiments of the present application. Figures 2 - 3 The washing machine anomaly determination method provided by any one of the corresponding embodiments of the present application.

[0137] Wherein, the memory 510 and the processor 520 are connected through a bus 530.

[0138] Related descriptions can be understood by referring to the relevant descriptions and effects corresponding to the steps of Figures 2 - 3 and will not be elaborated here.

[0139] The present application also provides a readable storage medium, in which an execution instruction is stored. When at least one processor of the voice interaction device executes the execution instruction, when the computer execution instruction is executed by the processor, the washing machine anomaly determination method in the above embodiments is implemented.

[0140] The present application also provides a program product, which includes an executable instruction, and the executable instruction is stored in a readable storage medium. At least one processor of the washing machine can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction to enable the voice interaction device to implement the washing machine anomaly determination method provided by the above various embodiments.

[0141] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or modules can be in electrical, mechanical, or other forms.

[0142] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, in each embodiment of the present application, the functional modules can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0144] The above-mentioned integrated modules implemented in the form of software functional modules can be stored in a computer-readable storage medium. The above-mentioned software functional modules are stored in a storage medium, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (English: processor) to execute some steps of the methods in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (English: Read-Only Memory, abbreviated as: ROM), random access memories (English: Random Access Memory, abbreviated as: RAM), magnetic disks, or optical discs that can store program codes.

[0145] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific implementation manners. Without departing from the principle of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present application.

Claims

1. A method for judging abnormalities of a washing machine, characterized in that, the method includes: obtaining user usage data and device data of the washing machine, wherein the user usage data is generated based on a user-side application; determining the operation data of the washing machine according to the user usage data and the device data; judging whether there is an abnormality in the washing machine according to the operation data; if so, obtaining the location information of the washing machine from the blockchain according to the device identification code of the washing machine, and generating a prompt message for the washing machine according to the location information and the operation data; the operation data includes device status, device type, and income amount. Judging whether there is an abnormality in the washing machine according to the operation data includes: when the device status of the washing machine is in a normal state, the device type of the washing machine is an infrequently used type, and the income amount of the washing machine is lower than a second preset amount, or when the device status of the washing machine is in a normal state, the device type of the washing machine is a frequently used type, and the income amount of the washing machine is lower than a third preset amount, it is determined that there is an abnormality in the washing machine, wherein the third preset amount is higher than the second preset amount; correspondingly, generating a prompt message for the washing machine according to the location information and the operation data includes: generating a prompt message for too low income of the washing machine according to the location information and the income amount; further includes: when the device status of the washing machine is in a normal state, the device type of the washing machine is an infrequently used type, and the income amount of the washing machine is higher than a fourth preset amount, obtaining the location information of the washing machine from the blockchain according to the device identification code of the washing machine, wherein the fourth preset amount is higher than the second preset amount; determining the number of newly added washing machines according to the income amount; generating a prompt message for the increase of the washing machine according to the location information and the number of newly added washing machines.

2. The method according to claim 1, characterized in that, the user usage data is generated by the user based on the user-side application by scanning the device identification code set on the washing machine.

3. The method according to claim 1, characterized in that, judging whether there is an abnormality in the washing machine according to the operation data includes: when the device status of the washing machine is in a preset fault state, it is determined that there is an abnormality in the washing machine; correspondingly, generating a prompt message for the washing machine according to the location information and the operation data includes: generating a device fault prompt message for the washing machine according to the location information and the preset fault state.

4. The method according to any one of claims 1-3, characterized in that, after determining the operation data of the washing machine according to the user usage data and the device data, it further includes: uploading the operation data, device data, and user usage data of the washing machine to the blockchain according to the device identification code of the washing machine.

5. A device for judging abnormalities of a washing machine, characterized in that, the device includes: A data acquisition module is used to acquire user usage data and device data of a washing machine, wherein the user usage data is generated based on a user-side application program; An operation data determination module is used to determine the operation data of the washing machine according to the user usage data and the device data; An abnormality judgment module is used to judge whether the washing machine has an abnormality according to the operation data; A prompt information generation module is used to, if the washing machine has an abnormality, obtain the location information of the washing machine from the blockchain according to the device identification code of the washing machine, and generate prompt information of the washing machine according to the location information and the operation data; The operation data includes device status, device type, and income amount. The abnormality judgment module is specifically used to determine that the washing machine has an abnormality when the device status of the washing machine is a normal status, the device type of the washing machine is an infrequently used type, and the income amount of the washing machine is lower than a second preset amount, or when the device status of the washing machine is a normal status, the device type of the washing machine is a frequently used type, and the income amount of the washing machine is lower than a third preset amount, wherein the third preset amount is higher than the second preset amount; The prompt information generation module is specifically used to generate a low-income prompt information of the washing machine according to the location information and the income amount; A second prompt information generation module is used to, when the device status of the washing machine is a normal status, the device type of the washing machine is an infrequently used type, and the income amount of the washing machine is higher than a fourth preset amount, obtain the location information of the washing machine from the blockchain according to the device identification code of the washing machine, wherein the fourth preset amount is higher than the second preset amount; determine the number of newly added washing machines according to the income amount; and generate a washing machine addition prompt information according to the location information and the number of newly added washing machines.

6. A server, characterized in that, the server includes a memory and at least one processor; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, so that the at least one processor executes the washing machine abnormality judgment method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, computer execution instructions are stored in the computer-readable storage medium, and when a processor executes the computer execution instructions, the washing machine abnormality judgment method according to any one of claims 1-4 is implemented.

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