WO2021078108A1 - 洗水参数获取方法、设备、系统及存储介质 - Google Patents

洗水参数获取方法、设备、系统及存储介质 Download PDF

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Publication number
WO2021078108A1
WO2021078108A1 PCT/CN2020/122065 CN2020122065W WO2021078108A1 WO 2021078108 A1 WO2021078108 A1 WO 2021078108A1 CN 2020122065 W CN2020122065 W CN 2020122065W WO 2021078108 A1 WO2021078108 A1 WO 2021078108A1
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Prior art keywords
washing
fabric
washed
lotion
water
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PCT/CN2020/122065
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English (en)
French (fr)
Inventor
肖建冰
徐凯
方君
葛波
艾朋飞
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阿里巴巴集团控股有限公司
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Publication of WO2021078108A1 publication Critical patent/WO2021078108A1/zh

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    • D—TEXTILES; PAPER
    • D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
    • D06B—TREATING TEXTILE MATERIALS USING LIQUIDS, GASES OR VAPOURS
    • D06B1/00—Applying liquids, gases or vapours onto textile materials to effect treatment, e.g. washing, dyeing, bleaching, sizing or impregnating
    • D—TEXTILES; PAPER
    • D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
    • D06B—TREATING TEXTILE MATERIALS USING LIQUIDS, GASES OR VAPOURS
    • D06B23/00—Component parts, details, or accessories of apparatus or machines, specially adapted for the treating of textile materials, not restricted to a particular kind of apparatus, provided for in groups D06B1/00 - D06B21/00
    • D—TEXTILES; PAPER
    • D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
    • D06B—TREATING TEXTILE MATERIALS USING LIQUIDS, GASES OR VAPOURS
    • D06B23/00—Component parts, details, or accessories of apparatus or machines, specially adapted for the treating of textile materials, not restricted to a particular kind of apparatus, provided for in groups D06B1/00 - D06B21/00
    • D06B23/20—Arrangements of apparatus for treating processing-liquids, -gases or -vapours, e.g. purification, filtration or distillation
    • D—TEXTILES; PAPER
    • D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
    • D06B—TREATING TEXTILE MATERIALS USING LIQUIDS, GASES OR VAPOURS
    • D06B23/00—Component parts, details, or accessories of apparatus or machines, specially adapted for the treating of textile materials, not restricted to a particular kind of apparatus, provided for in groups D06B1/00 - D06B21/00
    • D06B23/20—Arrangements of apparatus for treating processing-liquids, -gases or -vapours, e.g. purification, filtration or distillation
    • D06B23/205—Arrangements of apparatus for treating processing-liquids, -gases or -vapours, e.g. purification, filtration or distillation for adding or mixing constituents of the treating material
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • This application relates to the field of data processing technology, and in particular to a method, equipment, system and storage medium for obtaining wash water parameters.
  • Washing process a process applied to denim garments, can fade denim garments of primary colors to the desired color through the washing operation.
  • Various aspects of the present application provide a method, equipment, system, and storage medium for obtaining washing water parameters, which are used to improve the washing efficiency and success rate of traditional washing operations, and reduce labor costs.
  • the embodiment of the present application provides a washing device, including: a body, a hollow cavity for accommodating a fabric to be washed and a controller for controlling the washing process are provided on the body; a lotion injection assembly is also provided on the body, and Putting lotion into the hollow cavity under the control of the controller; the body is also provided with a washing component for washing the fabric to be washed under the control of the controller; the body is also provided The data collection component is used to collect the data in the working process and upload it to the server.
  • the embodiment of the present application provides a water washing method, which is suitable for water washing equipment, and the method includes:
  • washing parameters generate enzyme washing instructions and rinsing instructions
  • control the lotion injection component on the washing equipment to put the lotion required for enzyme washing into the hollow cavity containing the fabric to be washed, and control the washing component on the washing equipment to The fabric to be washed is subjected to an enzyme washing operation; after the enzyme washing operation is completed, according to the rinsing instruction, the lotion injection component is controlled to put the lotion required for rinsing into the hollow cavity, and the washing component is controlled to The water fabric to be washed is subjected to a rinsing operation.
  • the embodiment of the present application also provides an order processing method, including:
  • a target washing device for washing the order of water to be washed from at least one washing device that can provide washing operations for the total amount of the fabric
  • the washing water parameter corresponding to the order for the water to be washed is sent to the target washing device, so that the target washing device performs washing operation on the fabric to be washed according to the washing water parameter.
  • An embodiment of the present application also provides a server, including: a memory and a processor;
  • the memory is used to store one or more computer instructions
  • the processor is configured to execute the one or more computer instructions for:
  • a target washing device for washing the order of water to be washed from at least one washing device that can provide washing operations for the total amount of the fabric
  • the washing water parameter corresponding to the order for the water to be washed is sent to the target washing device, so that the target washing device performs washing operation on the fabric to be washed according to the washing water parameter.
  • the embodiments of the present application also provide a computer-readable storage medium storing a computer program.
  • the computer program When executed by one or more processors, the one or more processors are caused to perform actions including the following:
  • a target washing device for washing the order of water to be washed from at least one washing device that can provide washing operations for the total amount of the fabric
  • the washing water parameter corresponding to the order for the water to be washed is sent to the target washing device, so that the target washing device performs washing operation on the fabric to be washed according to the washing water parameter.
  • the washing equipment in the embodiments of the present application includes a body, and the body is provided with a controller, a lotion injection component, a washing water component, and a data collection component;
  • the lotion injection component is used to control the controller Put lotion into the hollow cavity from the bottom;
  • the washing component is controlled by the controller to wash the fabric to be washed;
  • the data collection component is used to collect the data in the working process and upload it to the server; to realize the automatic washing of the washing equipment Water operation reduces labor costs, improves washing efficiency, and has a high degree of intelligence.
  • Fig. 1a is a schematic structural diagram of a water washing system provided by an exemplary embodiment of the application
  • Figure 1b is a schematic structural diagram of another water washing system provided by an exemplary embodiment of the application.
  • FIG. 2 is a schematic structural diagram of a training process of a parameter prediction model provided by an exemplary embodiment of this application;
  • Fig. 3 is a schematic structural diagram of a water washing device provided by an exemplary embodiment of the application.
  • FIG. 4 is a schematic flow diagram of a water washing method provided by an exemplary embodiment of the application.
  • Fig. 5 is a schematic diagram of a jeans washing process provided by an exemplary embodiment of the application.
  • FIG. 6 is a schematic flowchart of an order processing method provided by an exemplary embodiment of this application.
  • FIG. 7 is a schematic flowchart of a method for obtaining washing water parameters according to an exemplary embodiment of the application.
  • FIG. 8 is a training method of a fabric washing parameter prediction model provided by an embodiment of the application.
  • FIG. 9 is a method flowchart of a method for obtaining washing water parameters provided by an embodiment of the application.
  • FIG. 10 is a schematic structural diagram of a server provided by an exemplary embodiment of this application.
  • FIG. 11 is a schematic structural diagram of a server provided by an exemplary embodiment of this application.
  • Fig. 12 is a schematic structural diagram of a model training device provided by an exemplary embodiment of this application.
  • the relationship between the attribute information of the sample fabric before and after washing and the actual washing parameters is learned in advance; according to the attribute information and target attributes of the fabric to be washed Information, obtain the washing parameters required for washing the fabric to be washed; and send the washing parameters to the washing equipment for the washing equipment to perform the washing operation on the fabric to be washed according to the washing parameters, and pass the parameters
  • the prediction model automatically obtains the washing parameters of the fabric to be washed, reducing the influence of manual experience on washing, reducing labor costs, improving washing efficiency and washing success rate.
  • Fig. 1a is a schematic structural diagram of a water washing system provided by an exemplary embodiment of the application.
  • the washing system includes a server 10a, a washing device 10b and a control device 10c.
  • a communication connection is established between the server 10a and the control device 10c
  • the server 10a delivers the obtained washing water parameters to the control device
  • the control device 10c and the washing device 10b establish a communication connection
  • the control device 10c delivers according to the server 10a
  • the washing water parameter generates a control instruction to control the washing equipment to perform washing operations on the fabric to be washed.
  • the server 10a and the control device 10c establish a communication connection through wireless or wired communication.
  • the server 10a may establish a communication connection with the control device 10c using communication methods such as WIFI, Bluetooth, infrared, or the like, or the server 10a may also establish a communication connection with the control device 10c through a mobile network.
  • the network standard of the mobile network can be any of 2G (GSM), 2.5G (GPRS), 3G (WCDMA, TD-SCDMA, CDMA2000, UTMS), 4G (LTE), 4G+ (LTE+), WiMax, etc. .
  • control device 10c and the washing device 10b establish a wireless or wired communication connection, and the control device 10c generates a control instruction according to the washing parameters issued by the server 10a to control the washing device 10b.
  • the server 10a may establish a communication connection with the control device 10c through communication methods such as WIFI, Bluetooth, infrared, or the like, or the server 10a may also establish a communication connection with the control device 10c through a mobile network.
  • the network standard of the mobile network can be any of 2G (GSM), 2.5G (GPRS), 3G (WCDMA, TD-SCDMA, CDMA2000, UTMS), 4G (LTE), 4G+ (LTE+), WiMax, etc. .
  • the server 10a may provide data support, computing services, and some management services for the washing device 10b and the control device 10c.
  • the implementation form of the server 10a is not limited.
  • the server 10a may be a server device such as a conventional server, a cloud server, a cloud host, and a virtual center.
  • the composition of the server equipment mainly includes a processor, a hard disk, a memory, a system bus, etc., and a general type of computer architecture.
  • the server 10a may include one website server or multiple website servers.
  • the server 10a processes the attribute information of the fabric to be washed and the target attribute information corresponding to the washing effect based on a predetermined washing parameter prediction model to obtain the parameters required for washing the fabric to be washed.
  • a parameter prediction model needs to be trained in advance, and the wash parameters of the current wash round are obtained based on the parameter prediction model obtained through training.
  • model training collect the attribute information of the sample fabric before washing, the attribute information of the sample fabric after washing, and the washing parameters of the sample fabric as the sample data set, and mark the sample data set to form a labeled data set. Model training.
  • FIG. 2 is a schematic structural diagram of a training process of a parameter prediction model provided by an exemplary embodiment of this application.
  • the network structure of the parameter prediction model in the embodiment of the present application is constructed by the first deep neural network, the second deep neural network and the convolutional neural network; among them, the non-color attributes of the sample fabric and the sample fabric before washing The non-color attributes after washing are used as the input data of the first deep neural network, and the color attributes of the sample fabric before washing and the color attributes of the sample fabric after washing are used as the input data of the convolutional neural network.
  • the first deep neural network and the convolutional neural network are used as the input data.
  • the output data of the network is combined as the input data of the second deep neural network, and the washing parameters of the sample fabric are used as the output data of the second deep neural network.
  • An achievable way is to use the attribute information of the sample fabric before washing, the attribute information of the sample fabric after washing, and the washing parameters of the sample fabric to perform model training to obtain a parameter prediction model.
  • the parameter prediction model can also be implemented by a neural network model or other numbers of models.
  • the parameter prediction model is a neural network model, collect the attribute information of the sample fabric before washing, the attribute information of the sample fabric after washing, and the washing parameters for washing the sample fabric to wash the neural network model.
  • the water parameter prediction training is used to obtain the parameter prediction model.
  • the embodiment of the present application uses the first deep neural network, the second deep neural network and the convolutional neural network to build three neural network models. Compared with one neural network model, the effect of the parameter prediction model is better.
  • the first parameter prediction model and the second parameter prediction model can be built on the model training device at the same time, wherein the first parameter prediction model is built by a neural network model, and the second parameter prediction model The model is built by three neural network models, and a gating switch is added to the model training equipment to select any one of the first parameter prediction model and the second parameter prediction model for model training.
  • the embodiment of the application implements the gating switch The form is not limited.
  • the attribute information of the sample fabric before washing includes at least one of the following: warp and weft tear strength, tear resistance, abrasion resistance, air permeability, K/S value, compressive rigidity, wrinkle recovery angle, fabric
  • the attribute information of the sample fabric after washing includes at least one of the following: warp and weft tear strength, tear resistance, abrasion resistance, air permeability, K/S Value, compressive rigidity, wrinkle recovery angle, proportion of fabric composition, warp and weft yarn specifications, warp and weft density and color value.
  • a training method of the parameter prediction model is to input the color attribute in the attribute information of the sample fabric before washing and the color attribute in the attribute information of the sample fabric after washing into the convolutional neural network algorithm to obtain the sample fabric
  • the characteristic values of the color attributes before and after washing; the non-color attributes in the attribute information of the sample fabric before washing and the non-color attributes in the attribute information of the sample fabric after washing are input into the first deep neural network algorithm to obtain the sample fabric washing water
  • the characteristic values of the non-color attributes before and after washing; the characteristic values of the color attributes of the sample fabric before and after washing and the characteristic values of the non-color attributes of the sample fabric before and after washing are combined to obtain the characteristic values of the attribute information of the sample fabric before and after washing; the sample fabric is washed
  • the eigenvalues of the attribute information before and after the water are input into the second deep neural network algorithm to establish the mapping relationship between the eigenvalues of the attribute information of the sample fabric before and after washing and the washing parameters to obtain a parameter prediction model.
  • the training device for the parameter prediction model may be the server 10a or another server.
  • the training device for the parameter prediction model needs to deliver the trained parameter prediction model to the server 10a, so that the server 10a can use the parameter prediction model to obtain the washing fabric Wash water parameters of the wash operation.
  • the server 10a After the server 10a obtains the trained parameter prediction model, the server 10a obtains the attribute information of the fabric to be washed and the target attribute information expected to be reached after washing, and compares the attribute information of the fabric to be washed with the target expected to be reached after washing.
  • the attribute information is input into the existing parameter prediction model to obtain the washing parameters required for washing the fabric to be washed, and then the washing parameters are sent to the control device 10c, and the control device 10c controls according to the washing parameters
  • the washing equipment performs washing operations on the fabrics to be washed.
  • the attribute information includes at least one of the following: warp and weft tear strength, tear resistance, abrasion resistance, air permeability, K/S value, compressive rigidity, wrinkle recovery angle, fabric composition ratio, warp and weft yarn Specifications, warp and weft density, color value and fabric weight; target attribute information includes at least one of the following: warp and weft tear strength, tear resistance, abrasion resistance, air permeability, K/S value, compressive rigidity, wrinkle recovery angle, fabric Component proportion, warp and weft yarn specification, warp and weft density and color value.
  • the server 10a acquiring the attribute information of the fabric to be washed includes but is not limited to the following acquisition methods:
  • the server 10a receives the first fabric selection request, the first fabric selection request includes the identification of the fabric to be washed; according to the identification of the fabric to be washed, the attribute information of the fabric to be washed is obtained from the database.
  • the server 10a receives the attribute information of the fabric to be washed detected and reported by the detection device before washing the fabric to be washed.
  • the server 10a receives the second fabric selection request, the second fabric selection request includes the identification of the fabric to be washed; according to the identification of the fabric to be washed, part of the attribute information of the fabric to be washed is obtained from the database; the server 10a receives The detection equipment detects and reports the remaining part of the attribute information of the fabric to be washed before washing the fabric to be washed.
  • the server 10a pre-stores the attribute information of a variety of fabrics, and also includes a display device that establishes a communication connection with the server 10a.
  • the user selects the fabric to be washed through the display device.
  • the display device has an electronic device. On the display screen, the user interacts with the display device through the electronic display screen to perform the selection operation of the fabric to be washed, so that the server 10a can obtain the attribute information of the fabric to be washed.
  • An achievable way is to display the fabric selection interface in response to the interface selection operation, and display images of multiple fabrics on the fabric selection interface; in response to a trigger operation on the image of the selected fabric to be washed, send to the server 10a
  • the first fabric selection request includes the identification of the fabric to be washed; the server 10a obtains the attribute information of the fabric to be washed from the database according to the identification of the fabric to be washed.
  • the attribute information of the fabric to be washed can also be obtained by detecting the actual fabric of the fabric to be washed by various detection devices, for example, the color value of the fabric to be washed is detected by a color measuring instrument, and the color value of the fabric to be washed is obtained by a weight sensor.
  • the weight of the fabric to be washed, etc.; among them, a weight sensor can also be provided on the washing device 10b. After the fabric to be washed is put into the washing device 10b, the washing device 10b obtains the weight of the fabric and uploads it to the server 10a .
  • the server 10a acquires part of the attribute information of the fabric to be washed in the first method, and the server acquires the remaining part of the attribute information in the second method.
  • obtaining the target attribute information expected to be achieved after washing including but not limited to the following methods:
  • Method 1 Receive a second fabric selection request, the second fabric selection request includes the identification of the reference fabric; according to the identification of the reference fabric, the attribute information of the reference fabric is obtained from the database as the target attribute information.
  • the second method is to receive the attribute information detected and reported by the inspection device on the reference fabric as the target attribute information.
  • the server 10a receives the second fabric selection request, and the second fabric selection request includes the identification of the reference fabric; according to the identification of the reference fabric, obtains part of the attribute information of the reference fabric from the database; the server 10a receives that the detection device is performing the inspection on the reference fabric. The remaining part of the attribute information of the reference fabric detected and reported before washing.
  • the server 10a pre-stores the attribute information of a variety of reference fabrics, and also includes a display device that establishes a communication connection with the server 10a.
  • the user selects the reference fabric through the display device.
  • the display device has an electronic display screen.
  • the user interacts with the display device through the electronic display screen to perform the selection operation of the reference fabric, so that the server 10a can obtain the attribute information of the reference fabric.
  • An achievable way is to display the fabric selection interface in response to the interface selection operation, and display images of multiple fabrics on the fabric selection interface; in response to the trigger operation on the image of the selected reference fabric, send the second to the server 10a.
  • the second fabric selection request includes the identification of the reference fabric; the server 10a obtains the attribute information of the reference fabric from the database according to the identification of the reference fabric.
  • the attribute information of the reference fabric can also be obtained by detecting the actual reference fabric by various detection devices, for example, the color value of the reference fabric is detected by a color measuring instrument, and the fabric weight of the reference fabric is obtained by a weight sensor, etc.
  • a weight sensor can also be provided on the washing device 10b, after the reference fabric is put into the washing device 10b, the washing device 10b obtains the weight of the fabric and uploads it to the server 10a.
  • the server 10a obtains part of the attribute information of the reference fabric through the first manner, and the server obtains the remaining part of the property information through the second manner.
  • the server 10a After the server 10a obtains the input data required by the parameter prediction model, it inputs the attribute information and target attribute information of the fabric to be washed into the parameter prediction model to obtain the fabric to be washed.
  • An optional embodiment is that the attribute information and target attribute information of the fabric to be washed are input into the parameter prediction model; within the parameter prediction model, a neural network algorithm is used to extract the characteristic value of the attribute information and the characteristic value of the target attribute information. ; The feature value of the attribute information and the feature value of the target attribute information are matched in the mapping relationship between the original attribute feature and the target attribute feature and the wash parameter to obtain the wash parameter of the fabric to be washed.
  • the attribute information and the target attribute information respectively include: color attributes and non-color attributes.
  • a neural network algorithm is used to extract the characteristic value of the attribute information and the characteristic value of the target attribute information.
  • One possible way is to use convolution
  • the neural network algorithm extracts the characteristic values of the color attributes in the attribute information and the target attribute information respectively;
  • the first deep neural network algorithm is used to extract the characteristic values of the non-color attributes in the attribute information and the target attribute information respectively.
  • the attribute information and the characteristic values of the target attribute information are matched in the mapping relationship between the characteristic values of the attribute information and the target attribute information and the washing parameters to obtain the washing parameters of the fabric to be washed.
  • An achievable way is to input the characteristic values of the attribute information and the target attribute information into the second deep neural network algorithm to obtain the washing parameters of the fabric to be washed; the second deep neural network algorithm reflects the original attribute characteristics and target attributes The mapping relationship between features and wash water parameters.
  • the original attribute feature includes the feature value of the non-color attribute in the attribute information and the feature value of the color attribute in the attribute information
  • the target attribute feature includes the feature value of the non-color attribute in the target attribute information and the feature of the color attribute in the target attribute information.
  • the second deep neural network establishes the feature value of the non-color attribute in the attribute information, the feature value of the color attribute in the attribute information, the feature value of the non-color attribute in the target attribute information, and the feature of the color attribute in the target attribute information
  • the server 10a only recommends the corresponding washing parameters for washing the fabric to be washed for the current round of washing operation, and then sends the washing parameters to the washing device 10b.
  • This application does not limit how the washing device 10b uses the washing parameters to perform washing operations on the fabric to be washed.
  • the washing device 10b performs washing operations on the fabric to be washed according to the washing parameters, including but not limited to the following embodiments:
  • the washing parameters are sent to the control device 10c, and the control device 10c generates control instructions to control the washing according to the washing parameters.
  • the water device 10b automatically performs washing operations on the fabric to be washed.
  • control device may not be included.
  • the server After obtaining the washing parameters required for washing the fabric to be washed, the server sends the washing parameters to the washing device, and the user can use the washing parameters according to the washing parameters.
  • the washing operation is performed manually, or the controller of the washing device may generate a control instruction according to the washing parameters to control the washing device to automatically perform the washing operation on the fabric to be washed.
  • the washing device 10b uses the washing parameters issued by the server 10a to wash the fabric to be washed, which may not achieve the expected washing effect, and the washing may be required during the washing process.
  • the parameters are adjusted, so the actual washing parameters used when the fabric to be washed reaches the standard may be different from the washing parameters issued by the server 10a. Therefore, after each washing operation is completed, the washing device 10b sends the actual washing parameters to the server 10a for the server 10a to update the original parameter prediction model according to the actual washing parameters to obtain the updated parameter prediction model.
  • the actual washing water parameters generated by the washing operation are fed back to the server, so that the server can update the original parameter prediction model according to the actual washing water parameters, and improve the accuracy of the prediction result of the parameter prediction model.
  • Fig. 1b is a schematic structural diagram of another water washing system provided by an exemplary embodiment of the application.
  • the water washing system includes: a first server 20a, a second server 20b, a water washing device 20c, and a control device 20d.
  • the first server 20a is a device that provides training services for the parameter prediction model
  • the second server 20b is a device that obtains washing parameters for washing the fabric to be washed.
  • the first server 20a sends the trained parameter prediction model to the second server 20b, and the second server 20b compares the attribute information of the fabric to be washed with the attribute information of the fabric to be washed according to the attribute information of the fabric to be washed and the target attribute information expected to be reached after washing.
  • the target attribute information is input to the parameter prediction model, and the washing parameters required for washing the fabric to be washed are obtained.
  • the second server 20b sends the washing parameters to the control device 20d, and the control device 20d controls the washing device 20c to perform washing operations on the fabric to be washed according to the washing parameters.
  • the description of using the parameter prediction model to obtain the washing parameters for washing the fabric to be washed may refer to the foregoing embodiments, and this embodiment will not be repeated.
  • the relationship between the attribute information of the sample fabric before and after washing and the actual washing parameters is learned in advance; according to the attribute information and target attribute information of the fabric to be washed, the to-be-washed fabric is obtained.
  • the washing parameters of the fabric to be washed can reduce the influence of manual experience on washing water, reduce labor costs, improve washing efficiency and washing success rate.
  • the washing equipment in the embodiments of the present application includes a body, and the body is provided with a controller, a lotion injection component, a washing water component, and a data collection component; the lotion injection component , Used to put lotion into the hollow cavity under the control of the controller; washing component, used to wash the fabric to be washed under the control of the controller; data collection component, used to collect the data in the working process and upload it To the server, the automatic washing operation of the washing equipment is realized, the labor cost is reduced, the washing efficiency is improved, and the degree of intelligence is high.
  • FIG. 3 is a schematic structural diagram of a water washing device provided by an exemplary embodiment of the application.
  • the water washing device includes a body on which a hollow cavity for accommodating the fabric to be washed and a controller for controlling the washing process are provided;
  • the body is also provided with a lotion injection component for putting lotion into the hollow cavity under the control of the controller;
  • a washing component is also provided on the body for washing the fabric to be washed under the control of the controller;
  • the body is also provided with a data collection component for collecting data in the working process and uploading it to the server.
  • the washing device of the embodiment of the present application includes a body, and the body is provided with a controller, a lotion injection component, a washing water component, and a data collection component;
  • the lotion injection component is used in the controller Put lotion into the hollow cavity under control;
  • the washing component performs washing operation on the fabric to be washed under the control of the controller;
  • the data collection component is used to collect the data in the working process and upload it to the server; realize the automation of the washing equipment Washing operation reduces labor costs, improves washing efficiency, and has a high degree of intelligence.
  • the lotion injection component includes a lotion injection port set on the body and a flow sensor set in the lotion injection port; the lotion injection port is connected to the hollow cavity and can be opened or closed by receiving instructions from the controller.
  • the lotion is put in the body; the flow sensor is used to detect the amount of lotion in the hollow cavity and report the amount of lotion to the controller; the overall structure of the washing device in the embodiment of the application is simple and compact, with a high degree of intelligence, and reduces labor Cost, improve the efficiency of washing water.
  • the data collection component of the embodiment of the present application includes a weight sensor arranged at the bottom of the hollow cavity; the weight sensor detects the weight of the fabric placed in the hollow cavity and uploads it to the server for the server to determine according to the weight and send it to the controller Washing parameters.
  • a weight sensor is added to the washing device in the embodiment of the present application, and the weight of the fabric is automatically uploaded to the server after the fabric to be washed is put into the hollow cavity.
  • the position of the weight sensor is reasonably set, which improves the automation degree of the equipment.
  • the lotion injection port of the washing equipment includes at least one of the following: an enzyme lotion injection port, a bleach lotion injection port, and a water injection port; each injection port is provided with a flow sensor, which is used separately It is used to detect the input amount of enzyme lotion, bleach lotion and water; the controller controls the corresponding enzyme lotion when the input amount of enzyme lotion, bleach lotion and water reported by each flow sensor reaches the required amount of washing water. The mouth, the bleach lotion injection port and the water injection port are closed.
  • the lotion injection port of the washing equipment includes an enzyme lotion injection port, a bleach lotion injection port, and a water injection port.
  • the enzyme lotion injection port, bleach lotion injection port, and water injection port are respectively A movable baffle is provided, and the baffle can be moved to realize the opening and closing of the lotion injection port.
  • the controller realizes the purpose of automatic and quantitative injection of lotion by controlling the opening and closing of the enzyme lotion injection port, the bleaching lotion injection port, and the baffle of the water injection port, with a high degree of intelligence.
  • the enzyme lotion injection port, the bleaching lotion injection port and the water injection port are arranged side by side on the top of one side of the body.
  • the washing device further includes a water temperature setting component, a rotation speed setting component, and a timing component arranged on the top of the body; the water temperature setting component is used to adjust the temperature of the lotion in the hollow cavity under the control of the controller.
  • the timing component is used to adjust the washing water speed under the control of the controller; the timing component is used to adjust the duration of each process under the control of the controller.
  • the washing water parameters include at least one of the following: process flow sequence, water temperature, rotation speed, time length of each washing water process, and lotion input amount.
  • process flow sequence In each process flow of washing water, the washing operation is performed according to the parameters under the process flow.
  • Fig. 4 is a schematic flow diagram of a water washing method provided by an exemplary embodiment of the application. As shown in Figure 4, the method includes:
  • S401 Generate an enzyme washing instruction and a rinsing instruction according to the washing water parameters
  • the washing device receives the washing parameters of the server shampoo, where the washing parameters are predicted according to the attribute information of the fabric to be washed and the target attribute information corresponding to the washing effect, or customized according to the clothes The washing effect required by the party is calculated.
  • the washing device further includes: at least one of a water temperature setting component, a rotation speed setting component, and a timing component.
  • the method further includes at least one of the following operations: setting the temperature of the lotion in the hollow cavity during the enzyme washing or rinsing operation by the water temperature setting component;
  • the setting component sets the washing speed of the washing water component;
  • the timing component is used to set the length of time for the washing water component to perform each enzyme washing or rinsing process.
  • the lotion injection component includes a lotion injection port and a flow sensor set in the lotion injection port; according to an enzyme washing instruction or a rinsing instruction, the lotion injection component on the washing equipment is Put the lotion required for enzyme washing or rinsing into the hollow cavity.
  • One achievable way is to control the opening of the lotion injection port to put the enzyme washing or rinsing into the hollow cavity according to the enzyme washing instruction or rinsing instruction. Obtain the amount of lotion in the hollow cavity detected by the flow sensor, and control the lotion injection port to close when the amount of lotion reaches the amount required for enzyme washing or rinsing.
  • Fig. 5 is a schematic diagram of a jeans washing process provided by an exemplary embodiment of the application. As shown in Figure 5, the jeans washing process is divided into an enzyme washing process and a rinsing process. The complete flow of the water washing process of this application will be described below in conjunction with FIG. 5.
  • the color meter obtains the color values of the reference jeans and the jeans to be washed; then, uploads the weight of the jeans to be washed on the server to obtain the washing parameters of the jeans to be washed, and then the controller according to The washing parameters generate enzyme washing instructions, add lotion, adjust the water temperature, speed and time, and start the enzyme washing process; then, after the enzyme washing process is completed, the jeans to be washed will be dehydrated; after that, it is judged whether the result is consistent with the predicted result, and if so, Enter the subsequent rinsing process, if otherwise, readjust the parameters and return to the enzyme washing process.
  • the controller In the rinsing process, the controller generates an enzyme wash instruction according to the washing water parameters, adds lotion, adjusts the water temperature, speed and time, and starts the rinsing process; after the rinsing process is completed, the jeans to be washed are dried, and then judges whether it is compatible with The prediction results are consistent, if the washing process is completed, if otherwise, the parameters are readjusted and returned to the rinsing process.
  • the washing equipment of the embodiment of the present application can be equipped with personalized equipment in the factory according to different washing water requirements, and automatically allocate different access orders.
  • device of you can set up equipment with different washing capacity.
  • the washing effect equipment when the factory puts in an order, first determines the washing effect of the batch order, and assigns the batch order to the equipment that can wash out the washing effect of the batch order for washing.
  • the washing equipment in the embodiments of the present application includes a body, and the body is additionally provided with a controller and a lotion injection port; the lotion injection port is provided with a flow sensor; the flow sensor is used to detect the lotion
  • the amount of injection can be automatically and quantitatively injected into the hollow cavity through the lotion injection port.
  • the controller controls the lotion injection port to close when the amount of lotion reported by the flow sensor reaches the required amount of washing water, and the washing equipment realizes washing.
  • the automatic quantitative delivery of the agent reduces labor costs, improves the efficiency of washing water, and has a high degree of intelligence.
  • FIG. 6 is a schematic flowchart of an order processing method provided by an exemplary embodiment of this application. As shown in Figure 6, the order processing method includes:
  • S602 Determine the target washing equipment for washing the order to be washed from at least one washing equipment that can provide washing operations for the total amount of fabric;
  • S603 Send the washing water parameters corresponding to the order for the water to be washed to the target washing equipment, so that the target washing equipment performs washing operations on the fabric to be washed according to the washing water parameters.
  • the washing water equipment in the intelligent chemical factory can be divided into different types of equipment according to the different washing water capacity. After obtaining the total amount of fabrics for the order to be washed, determine the target washing equipment for washing the order to be washed from at least one washing device that can provide washing operations for the total amount of fabrics; and use the water to be washed. The washing parameters corresponding to the order are sent to the target washing equipment, so that the target washing equipment can wash the fabric to be washed according to the washing parameters.
  • some embodiments of this application also provide a washing water parameter acquisition method.
  • the washing water parameter acquisition method provided in this application can be applied to the above washing water system and washing equipment , but not limited to the washing water system and washing equipment provided in the above embodiments.
  • the above-mentioned washing device can automatically perform the washing operation according to the washing parameters provided in the embodiments of the present application, or automatically perform the washing operation according to the washing parameters provided in other ways.
  • the washing water parameters may be set in the manner of manual participation, and the washing water parameters may be issued to the washing water device provided in the embodiment of the present application, so that the washing water device automatically performs the washing operation according to the washing water parameters.
  • FIG. 7 is a schematic flowchart of a method for acquiring washing water parameters according to an exemplary embodiment of the application. As shown in Figure 7, the method includes:
  • the execution subject of this application may be multiple servers with data support, computing services, and some management services.
  • the implementation form of the server is not limited.
  • the server may be a conventional server. , Cloud server, cloud host, virtual center and other server equipment.
  • the composition of the server equipment mainly includes a processor, a hard disk, a memory, a system bus, etc., and a general type of computer architecture.
  • the server includes one web server or multiple web servers.
  • the attribute information of the fabric to be washed and the target attribute information corresponding to the washing effect are processed to obtain the fabric to be washed.
  • the required parameters e.g., a parameter prediction model needs to be trained in advance, and the wash parameters of the current wash round are obtained based on the parameter prediction model obtained through training.
  • model training collect the attribute information of the sample fabric before washing, the attribute information of the sample fabric after washing, and the washing parameters of the sample fabric as the sample data set, and mark the sample data set to form a labeled data set. Model training.
  • the training process of the parameter prediction model and an implementation structure of the parameter prediction model obtained by training can be seen in FIG. 2.
  • the network structure of the parameter prediction model in the embodiment of the present application is constructed by the first deep neural network, the second deep neural network and the convolutional neural network; among them, the non-color attributes of the sample fabric and the sample fabric before washing The non-color attributes after washing are used as the input data of the first deep neural network, and the color attributes of the sample fabric before washing and the color attributes of the sample fabric after washing are used as the input data of the convolutional neural network.
  • the first deep neural network and the convolutional neural network are used as the input data.
  • the output data of the network is combined as the input data of the second deep neural network, and the washing parameters of the sample fabric are used as the output data of the second deep neural network.
  • An achievable way is to use the attribute information of the sample fabric before washing, the attribute information of the sample fabric after washing, and the washing parameters of the sample fabric to perform model training to obtain a parameter prediction model.
  • the attribute information of the sample fabric before washing includes at least one of the following: warp and weft tear strength, tear resistance, abrasion resistance, air permeability, K/S value, compressive rigidity, wrinkle recovery angle, fabric
  • the attribute information of the sample fabric after washing includes at least one of the following: warp and weft tear strength, tear resistance, abrasion resistance, air permeability, K/S Value, compressive rigidity, wrinkle recovery angle, proportion of fabric composition, warp and weft yarn specifications, warp and weft density and color value.
  • a training method of the parameter prediction model is to input the color attribute in the attribute information of the sample fabric before washing and the color attribute in the attribute information of the sample fabric after washing into the convolutional neural network algorithm to obtain the sample fabric
  • the characteristic values of the color attributes before and after washing; the non-color attributes in the attribute information of the sample fabric before washing and the non-color attributes in the attribute information of the sample fabric after washing are input into the first deep neural network algorithm to obtain the sample fabric washing water
  • the characteristic values of the non-color attributes before and after washing; the characteristic values of the color attributes of the sample fabric before and after washing and the characteristic values of the non-color attributes of the sample fabric before and after washing are combined to obtain the characteristic values of the attribute information of the sample fabric before and after washing; the sample fabric is washed
  • the eigenvalues of the attribute information before and after the water are input into the second deep neural network algorithm to establish the mapping relationship between the eigenvalues of the attribute information of the sample fabric before and after washing and the washing parameters to obtain a parameter prediction model.
  • the training device of the parameter prediction model may be a server that obtains wash water parameters, or may be another server.
  • the training device of the parameter prediction model needs to deliver the trained parameter prediction model to the server that obtains the washing water parameters, so that the server that obtains the washing water parameters can use the parameters
  • the prediction model obtains the washing parameters for washing the washed fabric.
  • the attribute information of the fabric to be washed and the target attribute information expected to be reached after washing are obtained, and the attribute information of the fabric to be washed and the target attribute information expected to be reached after washing are input into
  • the washing parameters required for washing the fabric to be washed are obtained, and then the washing parameters are sent to the control equipment, and the control equipment controls the washing equipment to be washed according to the washing parameters The fabric is washed.
  • the attribute information includes at least one of the following: warp and weft tear strength, tear resistance, abrasion resistance, air permeability, K/S value, compressive rigidity, wrinkle recovery angle, fabric composition ratio, warp and weft yarn Specifications, warp and weft density, color value and fabric weight; target attribute information includes at least one of the following: warp and weft tear strength, tear resistance, abrasion resistance, air permeability, K/S value, compressive rigidity, wrinkle recovery angle, fabric Component proportion, warp and weft yarn specification, warp and weft density and color value.
  • determining the washing effect of the fabric to be washed includes but is not limited to the following methods:
  • the first way is to obtain a picture of the target fabric in response to the photographing operation as the washing effect of the fabric to be washed.
  • Method two in response to the image import operation, obtain the image of the target fabric from the image library as the washing effect of the fabric to be washed.
  • obtaining the attribute information of the fabric to be washed includes but is not limited to the following methods:
  • the first method of obtaining is to receive a second fabric selection request, the second fabric selection request includes the identification of the fabric to be washed; according to the identification of the fabric to be washed, the attribute information of the fabric to be washed is obtained from the database.
  • the second method of obtaining is to receive the attribute information of the fabric to be washed detected and reported by the detecting device before washing the fabric to be washed.
  • the second fabric selection request includes the identification of the fabric to be washed; according to the identification of the fabric to be washed, part of the attribute information of the fabric to be washed is obtained from the database; The remaining part of the attribute information of the fabric to be washed, which is detected before washing and reported.
  • the server pre-stores the attribute information of a variety of fabrics, and also includes a display device that establishes a communication connection with the server.
  • the user selects the fabric to be washed through the display device.
  • the display device has an electronic display screen.
  • the user interacts with the display device through the electronic display screen to perform the selection operation of the fabric to be washed, so as to obtain the attribute information of the fabric to be washed.
  • An achievable way is to display the fabric selection interface in response to the interface selection operation, and display images of multiple fabrics on the fabric selection interface; in response to the trigger operation on the image of the selected fabric to be washed, send the first to the server 2.
  • the second fabric selection request includes the identification of the fabric to be washed; the server obtains the attribute information of the fabric to be washed from the database according to the identification of the fabric to be washed.
  • the attribute information of the fabric to be washed can also be obtained by detecting the actual fabric of the fabric to be washed by various detection devices, for example, the color value of the fabric to be washed is detected by a color measuring instrument, and the color value of the fabric to be washed is obtained by a weight sensor.
  • the weight of the fabric to be washed, etc.; among them, a weight sensor can also be set on the washing device. After the fabric to be washed is put into the washing device, the washing device obtains the weight of the fabric and uploads it to the server.
  • the first method is used to obtain part of the attribute information of the fabric to be washed, and the server uses the second method to obtain the remaining part of the attribute information.
  • obtaining the target attribute information expected to be achieved after washing including but not limited to the following methods:
  • Manner 1 Receive a first fabric selection request, the first fabric selection request includes the identification of the reference fabric; according to the identification of the reference fabric, the attribute information of the reference fabric is obtained from the database as the target attribute information.
  • the second method is to receive the attribute information detected and reported by the inspection device on the reference fabric as the target attribute information.
  • Method 3 The server receives the second fabric selection request, the second fabric selection request includes the identification of the reference fabric; according to the identification of the reference fabric, obtains part of the attribute information of the reference fabric from the database; the server receives that the detection device is washing the reference fabric The remaining part of the attribute information of the reference fabric that was previously detected and reported.
  • the server pre-stores the attribute information of a variety of reference fabrics, and also includes a display device that establishes a communication connection with the server.
  • the user selects the reference fabric through the display device.
  • the display device has an electronic display screen.
  • the selection operation of the reference fabric is performed through the interaction of the electronic display screen and the display device, so that the server can obtain the attribute information of the reference fabric.
  • An achievable way is to display the fabric selection interface in response to the interface selection operation, and display images of multiple fabrics on the fabric selection interface; in response to the trigger operation on the image of the selected reference fabric, send the first fabric to the server
  • the first fabric selection request includes the identification of the reference fabric; the server obtains the attribute information of the reference fabric from the database according to the identification of the reference fabric.
  • the attribute information of the reference fabric can also be obtained by detecting the actual reference fabric by various detection devices, for example, the color value of the reference fabric is detected by a color measuring instrument, and the fabric weight of the reference fabric is obtained by a weight sensor, etc. ; Among them, a weight sensor can also be set on the washing machine. After the reference fabric is put into the washing machine, the washing machine obtains the weight of the fabric and uploads it to the server.
  • the server obtains part of the attribute information of the reference fabric through the first method, and the server obtains the remaining part of the attribute information through the second method.
  • the attribute information and target attribute information of the fabric to be washed are input into the parameter prediction model to obtain the fabric to be washed.
  • the required wash water parameters An optional embodiment is that the attribute information and target attribute information of the fabric to be washed are input into the parameter prediction model; within the parameter prediction model, a neural network algorithm is used to extract the characteristic value of the attribute information and the characteristic value of the target attribute information. ; The feature value of the attribute information and the feature value of the target attribute information are matched in the mapping relationship between the original attribute feature and the target attribute information feature and the wash parameter to obtain the wash parameter of the fabric to be washed.
  • the attribute information and the target attribute information respectively include: color attributes and non-color attributes.
  • a neural network algorithm is used to extract the characteristic value of the attribute information and the characteristic value of the target attribute information.
  • One possible way is to use convolution
  • the neural network algorithm extracts the characteristic values of the color attributes in the attribute information and the target attribute information respectively;
  • the first deep neural network algorithm is used to extract the characteristic values of the non-color attributes in the attribute information and the target attribute information respectively.
  • the attribute information and the characteristic values of the target attribute information are matched in the mapping relationship between the characteristic values of the attribute information and the target attribute information and the washing parameters to obtain the washing parameters of the fabric to be washed.
  • An achievable way is to input the characteristic values of the attribute information and the target attribute information into the second deep neural network algorithm to obtain the washing parameters of the fabric to be washed; the second deep neural network algorithm reflects the original attribute characteristics and target attributes The mapping relationship between features and wash water parameters.
  • the original attribute feature includes the feature value of the non-color attribute in the attribute information and the feature value of the color attribute in the attribute information
  • the target attribute feature includes the feature value of the non-color attribute in the target attribute information and the feature of the color attribute in the target attribute information.
  • the second deep neural network establishes the feature value of the non-color attribute in the attribute information, the feature value of the color attribute in the attribute information, the feature value of the non-color attribute in the target attribute information, and the feature of the color attribute in the target attribute information
  • the corresponding wash parameters for washing the fabric to be washed are recommended only for the current wash operation, and then the wash parameters are issued to the washing equipment.
  • This application does not limit how the washing equipment uses the washing parameters to perform washing operations on the fabrics to be washed.
  • the washing equipment performs washing operations on the fabrics to be washed according to the washing parameters, including but not limited to the following embodiments:
  • the washing parameters are sent to the control device 10c, and the control device 10c generates control instructions to control the washing according to the washing parameters.
  • the water device 10b automatically performs washing operations on the fabric to be washed.
  • control device may not be included.
  • the server After obtaining the washing parameters required for washing the fabric to be washed, the server sends the washing parameters to the washing device, and the user can use the washing parameters according to the washing parameters.
  • the washing operation is performed manually, or the controller of the washing device may generate a control instruction according to the washing parameters to control the washing device to automatically perform the washing operation on the fabric to be washed.
  • the washing equipment uses the washing parameters issued by the server to wash the fabric to be washed, which may not achieve the expected washing effect, and the washing parameters may need to be adjusted during the washing process. Therefore, the actual washing parameters used when the fabric to be washed reaches the standard may be different from the washing parameters issued by the server. Therefore, after each washing operation is completed, the washing device sends the actual washing parameters to the server for the server to update the original parameter prediction model according to the actual washing parameters to obtain the updated parameter prediction model. In the embodiment of the application, the actual washing water parameters generated by the washing operation are fed back to the server to update the original parameter prediction model, thereby improving the accuracy of parameter prediction.
  • washing water parameters of the present application based on machine learning, the relationship between the attribute information of the sample fabric before and after washing and the actual washing parameters is learned in advance; according to the attribute information of the fabric to be washed and the target attribute information, it is obtained Wash the washing parameters required for washing the fabric to be washed; and send the washing parameters to the washing equipment, so that the washing equipment can wash the fabric to be washed according to the washing parameters, and automatically use the parameter prediction model Obtain the washing parameters of the fabric to be washed, reduce the influence of manual experience on washing, reduce labor costs, improve washing efficiency and washing success rate.
  • FIG. 8 is a training method of a fabric washing parameter prediction model provided by an embodiment of the application, and the method includes:
  • S802 Perform washing parameter prediction training using the attribute information of the sample fabric before washing, the attribute information of the sample fabric after washing, and the washing parameters for washing the sample fabric to obtain a parameter prediction model.
  • FIG. 9 is a method flowchart of a method for obtaining washing water parameters provided by an embodiment of the application. As shown in FIG. 9, the method includes:
  • a picture of the target fabric is acquired. Including but not limited to the following two ways:
  • Method one in response to the photographing operation, obtain a picture of the target fabric
  • Method two in response to the image import operation, obtain the image of the target fabric from the image library.
  • obtaining the attribute information of the fabric to be washed includes:
  • Method 1 In response to the operation of inputting the attribute information of the fabric to be washed in the attribute information input item on the human-computer interaction interface, obtain the attribute information of the fabric to be washed;
  • Method 2 In response to the selection operation of the fabric to be washed on the human-computer interaction interface, the attribute information of the selected washed fabric is obtained from the fabric database.
  • the washing parameters required for washing the fabric to be washed are displayed.
  • FIG. 10 is a schematic structural diagram of a server provided by an exemplary embodiment of this application.
  • the data processing device includes: a memory 1001 and a processor 1002.
  • the data processing device also includes necessary components such as a communication component 1003 and a power supply component 1004.
  • the memory 1001 is used to store computer programs, and can be configured to store various other data to support operations on the data processing device. Examples of such data include instructions for any application or method operating on the data processing device.
  • the memory 1001 can be implemented by any type of volatile or non-volatile storage device or their combination, 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 memory, flash memory, magnetic disk or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable Programmable read only memory
  • PROM programmable read only memory
  • ROM read only memory
  • magnetic memory magnetic memory
  • flash memory magnetic disk or optical disk.
  • the communication component 1003 is used for sending the washing water parameters to the washing water equipment.
  • the processor 1002 can execute computer instructions stored in the memory 1001 to determine the washing effect of the fabric to be washed; obtain attribute information of the fabric to be washed; The attribute information of the fabric and the target attribute information corresponding to the washing effect are processed to obtain washing parameters required for washing the fabric to be washed.
  • the processor 1002 determines the washing effect of the fabric to be washed, which is specifically used to: in response to a photographing operation, obtain a picture of the target fabric as the washing effect of the fabric to be washed; or, in response to an image import operation, Obtain the picture of the target fabric from the image library as the washing effect of the fabric to be washed.
  • the processor 1002 may also be used to: In the picture, extract the target attribute information corresponding to the washing effect.
  • the processor 1002 when the processor 1002 obtains the attribute information of the fabric to be washed, it is specifically configured to: receive a first fabric selection request, where the first fabric selection request includes the identification of the fabric to be washed; and according to the identification of the fabric to be washed, Obtain the attribute information of the fabric to be washed from the database; or, receive the attribute information of the fabric to be washed detected and reported by the detection device before washing the fabric to be washed.
  • the processor 1002 when it obtains the attribute information of the fabric to be washed, it may also be used to: send the washing parameters to the washing device for the washing device to perform washing operations on the fabric to be washed according to the washing parameters; Alternatively, the washing water parameter is sent to the control device, so that the control device controls the washing device to perform a washing operation on the fabric to be washed according to the washing water parameter.
  • the processor 1002 processes the attribute information of the fabric to be washed and the target attribute information corresponding to the washing effect based on a predetermined washing parameter prediction model to obtain the washing water required for washing the fabric to be washed.
  • the parameters it is specifically used to: input the attribute information and target attribute information of the fabric to be washed into the parameter prediction model; inside the parameter prediction model, use neural network algorithms to extract the characteristic values of the attribute information and the target attribute information respectively; The feature value of the attribute information and the feature value of the target attribute information are matched in the mapping relationship between the original attribute feature, the target attribute feature and the washing parameter to obtain the washing parameter of the fabric to be washed.
  • the attribute information and the target attribute information respectively include: color attributes and non-color attributes; when the processor 1002 uses a neural network algorithm to extract the characteristic values of the attribute information and the target attribute information, it is specifically used to: The product neural network algorithm extracts the characteristic values of the color attributes in the attribute information and the target attribute information respectively; the first deep neural network algorithm is used to extract the characteristic values of the non-color attributes in the attribute information and the target attribute information respectively.
  • the processor 1002 matches the characteristic values of the attribute information and the target attribute information in the mapping relationship between the characteristic values of the attribute information and the target attribute information and the washing parameters to obtain the washing parameters of the fabric to be washed, Specifically used: input the attribute information and the characteristic value of the target attribute information into the second deep neural network algorithm to obtain the washing parameters of the fabric to be washed; the second deep neural network algorithm reflects the original attribute characteristics, target attribute characteristics and washing water Parameter mapping relationship.
  • the attribute information includes at least one of the following: warp and weft tear strength, tear resistance, abrasion resistance, air permeability, K/S value, compressive rigidity, wrinkle recovery angle, fabric composition ratio, warp and weft yarn specifications, Warp and weft density, color value and fabric weight; target attribute information includes at least one of the following: warp and weft tear strength, tear resistance, abrasion resistance, air permeability, K/S value, compressive rigidity, wrinkle recovery angle, fabric composition Ratio, warp and weft yarn specification, warp and weft density and color value.
  • the processor 1002 sends the washing water parameters to the washing water device, it may also be used to:
  • the actual washing parameters are the washing parameters actually used when the washing equipment washes the fabric to be washed to reach the standard.
  • the processor 1002 may also be used to:
  • the processor 1002 uses the attribute information of the sample fabric before washing, the attribute information of the sample fabric after washing, and the washing parameters of the sample fabric to perform model training to obtain a parameter prediction model, which is specifically used for: Input the color attribute in the attribute information of the sample fabric before washing and the color attribute in the attribute information of the sample fabric after washing into the convolutional neural network algorithm to obtain the characteristic value of the color attribute of the sample fabric before and after washing; wash the sample fabric
  • the non-color attributes in the previous attribute information and the non-color attributes in the attribute information of the sample fabric after washing are input into the first deep neural network algorithm to obtain the characteristic values of the non-color attributes of the sample fabric before and after washing;
  • the characteristic values of the color attributes and the characteristic values of the non-color attributes of the sample fabric before and after washing are input into the second deep neural network algorithm to establish the mapping relationship between the original attribute characteristics, the target attribute characteristics and the washing parameters to obtain a parameter prediction model.
  • the embodiment of the present application also provides a computer-readable storage medium storing a computer program.
  • the computer-readable storage medium stores a computer program
  • the computer program is executed by one or more processors
  • the one or more processors are caused to execute each step in the method embodiment shown in FIG. 7.
  • FIG. 11 is a schematic structural diagram of a server provided by an exemplary embodiment of this application.
  • the data processing device includes: a memory 1101 and a processor 1102.
  • the data processing device also includes necessary components such as a communication component 1103 and a power supply component 1104.
  • the memory 1101 is used to store computer programs, and can be configured to store various other data to support operations on the data processing device. Examples of such data include instructions for any application or method operating on the data processing device.
  • the memory 1101 can be implemented by any type of volatile or non-volatile storage device or their combination, 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 memory, flash memory, magnetic disk or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable Programmable read only memory
  • PROM programmable read only memory
  • ROM read only memory
  • magnetic memory magnetic memory
  • flash memory magnetic disk or optical disk.
  • the communication component 1103 is used to communicate with other devices.
  • the processor 1102 can execute computer instructions stored in the memory 1101 to obtain the total amount of fabrics to be washed in an order; determine the water to be washed from at least one washing device that can provide washing operations for the total amount of fabrics Order the target washing equipment for washing water; send the washing parameters corresponding to the order to be washed to the target washing equipment, so that the target washing equipment can wash the fabric to be washed according to the washing parameters.
  • the embodiment of the present application also provides a computer-readable storage medium storing a computer program.
  • the computer-readable storage medium stores a computer program
  • the computer program is executed by one or more processors
  • the one or more processors are caused to execute each step in the method embodiment shown in FIG. 6.
  • Fig. 12 is a schematic structural diagram of a model training device provided by an exemplary embodiment of this application.
  • the data processing device includes: a memory 1201 and a processor 1202.
  • the data processing device also includes necessary components such as a communication component 1203 and a power supply component 1204.
  • the memory 1201 is used to store computer programs, and can be configured to store various other data to support operations on the data processing device. Examples of such data include instructions for any application or method operating on the data processing device.
  • the memory 1201 can be implemented by any type of volatile or non-volatile storage device or their combination, 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 memory, flash memory, magnetic disk or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable Programmable read only memory
  • PROM programmable read only memory
  • ROM read only memory
  • magnetic memory magnetic memory
  • flash memory magnetic disk or optical disk.
  • the communication component 1203 is used to communicate with other devices.
  • the processor 1202 can execute computer instructions stored in the memory 1201 to obtain the attribute information of the sample fabric before washing, the attribute information of the sample fabric after washing, and the washing parameters for washing the sample fabric; using the sample Perform washing parameter prediction training on the attribute information of the fabric before washing, the attribute information of the sample fabric after washing, and the washing parameters of the sample fabric to obtain a parameter prediction model.
  • the embodiment of the present application also provides a computer-readable storage medium storing a computer program.
  • the computer-readable storage medium stores a computer program
  • the computer program is executed by one or more processors
  • the one or more processors are caused to execute each step in the method embodiment shown in FIG. 8.
  • Fig. 12 is a schematic structural diagram of a model training device provided by an exemplary embodiment of this application.
  • the data processing device includes: a memory 1201 and a processor 1202.
  • the data processing device also includes necessary components such as a communication component 1203 and a power supply component 1204.
  • the memory 1201 is used to store computer programs, and can be configured to store various other data to support operations on the data processing device. Examples of such data include instructions for any application or method operating on the data processing device.
  • the memory 1201 can be implemented by any type of volatile or non-volatile storage device or their combination, 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 memory, flash memory, magnetic disk or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable Programmable read only memory
  • PROM programmable read only memory
  • ROM read only memory
  • magnetic memory magnetic memory
  • flash memory magnetic disk or optical disk.
  • the communication component 1203 is used to communicate with other devices.
  • the processor 1202 can execute computer instructions stored in the memory 1201 to display a human-computer interaction interface in response to the interface display operation; respond to the acquisition operation of the washing effect of the fabric to be washed on the human-computer interaction interface, Obtain the picture of the target fabric as the washing effect of the fabric to be washed; in response to the attribute information input operation on the human-computer interaction interface, obtain the attribute information of the fabric to be washed; respond to the operation of predicting the washing parameters on the interface , To obtain the washing parameters required for washing the fabric to be washed.
  • the embodiment of the present application also provides a computer-readable storage medium storing a computer program.
  • the computer-readable storage medium stores a computer program
  • the computer program is executed by one or more processors
  • the one or more processors are caused to execute each step in the method embodiment shown in FIG. 9.
  • the communication components in Figures 10-12 are configured to facilitate wired or wireless communication between the device where the communication component is located and other devices.
  • the device where the communication component is located can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination of them.
  • the communication component receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component further includes near field communication (NFC) technology, radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, etc. Promote short-range communications.
  • NFC near field communication
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the power supply components in Figures 10-12 above provide power for various components of the equipment where the power supply component is located.
  • the power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device where the power supply component is located.
  • the relationship between the attribute information of the sample fabric before and after washing and the actual washing parameters is learned in advance; according to the attribute information and target attribute information of the fabric to be washed, the fabric to be washed is obtained.
  • the required washing parameters and send the washing parameters to the washing equipment, so that the washing equipment performs washing operations on the fabrics to be washed according to the washing parameters, and automatically obtains the fabrics that need to be washed through the parameter prediction model Washing water parameters reduce the influence of manual experience on washing water, reduce labor costs, improve washing water efficiency and washing water success rate.
  • the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing equipment to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction device.
  • the device implements the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.
  • These computer program instructions can also be loaded on a computer or other programmable data processing equipment, so that a series of operation steps are executed on the computer or other programmable equipment to produce computer-implemented processing, so as to execute on the computer or other programmable equipment.
  • the instructions provide steps for implementing the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.
  • the computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
  • processors CPUs
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • the memory may include non-permanent memory in a computer readable medium, random access memory (RAM) and/or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer readable media.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash memory
  • Computer-readable media include permanent and non-permanent, removable and non-removable media, and information storage can be realized by any method or technology.
  • the information can be computer-readable instructions, data structures, program modules, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage, Magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices. According to the definition in this article, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

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Abstract

本申请实施例提供一种洗水参数获取方法、设备、系统及存储介质。在本申请的一些实施例中，本申请实施例洗水设备包括机体，机体上设有控制器、洗剂投放组件、洗水组件与数据采集组件；洗剂投放组件，用于在控制器控制下向中空腔体内投入洗剂；洗水组件在控制器控制下对待洗水面料进行洗水操作；数据采集组件，用于采集工作过程中的数据并上传至服务器；实现洗水设备的自动化洗水操作，降低人工成本，提高洗水效率，智能化程度高。

Description

洗水参数获取方法、设备、系统及存储介质
本申请要求2019年10月21日递交的申请号为201911002154.3、发明名称为“洗水参数获取方法、设备、系统及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及数据处理技术领域，尤其涉及一种洗水参数获取方法、设备、系统及存储介质。
背景技术
洗水工艺，一种应用在牛仔服装上的工艺，通过洗水操作，能够把原色的牛仔服装褪色到想要的颜色。
传统洗水设备在洗水操作中需要大量的人工操作，为了达到目标效果，在洗水前以及洗水过程中都需要人工不停的参与及中断检查洗水效果，期间费时费力，且存在较高的失败率。传统洗水设备智能化程度低，洗水效率低下。
发明内容
本申请的多个方面提供一种洗水参数获取方法、设备、系统及存储介质，用以提高传统洗水操作的洗水效率和成功率，以及降低人工成本。
本申请实施例提供一种洗水设备，包括：机体，机体上设置有容纳待洗水面料的中空腔体以及控制洗水工序的控制器；所述机体上还设置有洗剂投放组件，用于在控制器控制下向所述中空腔体内投入洗剂；所述机体上还设置有洗水组件，用于在控制器控制下对待洗水面料进行洗水操作；所述机体上还设置有数据采集组件，用于采集工作过程中的数据并上传至服务器。
本申请实施例提供一种洗水方法，适用于洗水设备，所述方法包括：
根据洗水参数，生成酵素洗指令和漂洗指令；
根据酵素洗指令，控制所述洗水设备上的洗剂投放组件向容纳待洗水面料的中空腔体内投入酵素洗所需的洗剂，并控制所述洗水设备上的洗水组件对所述待洗水面料进行酵素洗操作；在酵素洗操作完成后，根据漂洗指令，控制所述洗剂投放组件向所述中空腔体内投入漂洗所需的洗剂，并控制所述洗水组件对所述待洗水面料进行漂洗操作。
本申请实施例还提供一种订单处理方法，包括：
获取待洗水订单的面料总量；
从能够为所述面料总量提供洗水操作的至少一台洗水设备中确定对所述待洗水订单进行洗水的目标洗水设备；
将与该待洗水订单相应的洗水参数发送至目标洗水设备，以供目标洗水设备根据所述洗水参数对待洗水面料进行洗水操作。
本申请实施例还提供一种服务器，包括：存储器和处理器；
所述存储器，用于存储一条或多条计算机指令；
所述处理器，用于执行所述一条或多条计算机指令以用于：
获取待洗水订单的面料总量；
从能够为所述面料总量提供洗水操作的至少一台洗水设备中确定对所述待洗水订单进行洗水的目标洗水设备；
将与该待洗水订单相应的洗水参数发送至目标洗水设备，以供目标洗水设备根据所述洗水参数对待洗水面料进行洗水操作。
本申请实施例还提供一种存储有计算机程序的计算机可读存储介质，当所述计算机程序被一个或多个处理器执行时，致使所述一个或多个处理器执行包括以下的动作：
获取待洗水订单的面料总量；
从能够为所述面料总量提供洗水操作的至少一台洗水设备中确定对所述待洗水订单进行洗水的目标洗水设备；
将与该待洗水订单相应的洗水参数发送至目标洗水设备，以供目标洗水设备根据所述洗水参数对待洗水面料进行洗水操作。
在本申请的一些实施例中，本申请实施例洗水设备包括机体，机体上设有控制器、洗剂投放组件、洗水组件与数据采集组件；洗剂投放组件，用于在控制器控制下向中空腔体内投入洗剂；洗水组件在控制器控制下对待洗水面料进行洗水操作；数据采集组件，用于采集工作过程中的数据并上传至服务器；实现洗水设备的自动化洗水操作，降低人工成本，提高洗水效率，智能化程度高。
附图说明
此处所说明的附图用来提供对本申请的进一步理解，构成本申请的一部分，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。在附图中：
图1a为本申请一示例性实施例提供的一种洗水系统的结构示意图；
图1b为本申请示例性实施例提供的另一种洗水系统的结构示意图；
图2为本申请示例性实施例提供的一种参数预测模型训练过程的结构示意图；
图3为本申请示例性实施例提供的一种洗水设备的结构示意图；
图4为本申请示例性实施例提供的一种洗水方法的流示意图；
图5为本申请示例性实施例提供的一种牛仔裤洗水工艺流程的示意图；
图6为本申请示例性实施例提供的一种订单处理方法的流程示意图；
图7为本申请示例性实施例提供的一种洗水参数获取方法的流程示意图；
图8为本申请实施例提供的一种面料的洗水参数预测模型的训练方法；
图9为本申请实施例提供的一种洗水参数获取方法的方法流程图；
图10为本申请一示例性实施例提供的一种服务器的结构示意图；
图11为本申请一示例性实施例提供的一种服务器的结构示意图；
图12为本申请一示例性实施例提供的一种模型训练设备的结构示意图。
具体实施方式
为使本申请的目的、技术方案和优点更加清楚，下面将结合本申请具体实施例及相应的附图对本申请技术方案进行清楚、完整地描述。显然，所描述的实施例仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
目前，洗水操作中的酵洗和漂洗需要大量的人工操作，为了达到目标效果，洗水前和洗水过程中都需要人工不停的调整洗水参数并中断检查洗水效果，期间费时费力，同时也存在较高的失败率。
针对上述存在的问题，在本申请一些示例性实施例中，基于机器学习，预先学习样本面料在洗水前后的属性信息与实际洗水参数的关系；根据待洗水面料的属性信息和目标属性信息，得到对待洗水面料进行洗水所需的洗水参数；并将该洗水参数下发至洗水设备，以供洗水设备根据洗水参数对待洗水面料进行洗水操作，通过参数预测模型自动获得需要对待洗水面料的洗水参数，降低人工经验对洗水的影响，降低人工成本，提高洗水效率以及洗水的成功率。
以下结合附图，详细说明本申请各实施例提供的技术方案。
图1a为本申请一示例性实施例提供的一种洗水系统的结构示意图。如图1a所示，该洗水系统包括：服务器10a，洗水设备10b和控制设备10c。其中，服务器10a和控制设备10c之间建立通信连接，服务器10a将得到的洗水参数下发至控制设备，控制设备10c与洗水设备10b之间建立通信连接，控制设备10c根据服务器10a下发的洗水参数生成控制指令控制洗水设备对待洗水面料进行洗水操作。
在本实施例中，服务器10a和控制设备10c通过无线或有线建立通信连接。可选地，服务器10a可采用WIFI、蓝牙、红外等通信方式与控制设备10c建立通信连接，或者，服务器10a也可以通过移动网络与控制设备10c建立通信连接。其中，移动网络的网络制式可以为2G(GSM)、2.5G(GPRS)、3G(WCDMA、TD-SCDMA、CDMA2000、UTMS)、4G(LTE)、4G+(LTE+)、WiMax等中的任意一种。
在本实施例中，控制设备10c与洗水设备10b通过无线或有线建立通信连接，控制设备10c根据服务器10a下发的洗水参数生成控制指令，对洗水设备10b进行控制，可选地，服务器10a可采用WIFI、蓝牙、红外等通信方式与控制设备10c建立通信连接，或者，服务器10a也可以通过移动网络与控制设备10c建立通信连接。其中，移动网络的网络制式可以为2G(GSM)、2.5G(GPRS)、3G(WCDMA、TD-SCDMA、CDMA2000、UTMS)、4G(LTE)、4G+(LTE+)、WiMax等中的任意一种。
在本实施例中，服务器10a可以为洗水设备10b和控制设备10c提供数据支持、计算服务以及一些管理服务。在本实施例中，并不限定服务器10a的实现形态，例如服务器10a可以是常规服务器、云服务器、云主机、虚拟中心等服务器设备。其中，服务器设备的构成主要包括处理器、硬盘、内存、系统总线等，和通用的计算机架构类型。服务器10a可以包含一台网站服务器，也可以包含多台网站服务器。
在本实施例中，服务器10a基于预先确定的洗水参数预测模型，对待洗水面料的属性信息和洗水效果对应的目标属性信息进行处理，得到对待洗水面料进行洗水所需的参数。可选地，需要预先训练得到参数预测模型，基于训练得到的参数预测模型获取当前洗水轮次的洗水参数。在模型训练前，采集样本面料洗水前的属性信息和样本面料洗水后的属性信息以及样本面料进行洗水的洗水参数作为样本数据集，并对样本数据集进行标注形成标注数据集进行模型训练。
图2为本申请示例性实施例提供的一种参数预测模型训练过程的结构示意图。如图2所示，本申请实施例参数预测模型的网络结构由第一深度神经网络、第二深度神经网 络和卷积神经网络搭建而成；其中，样本面料洗水前非颜色属性和样本面料洗水后非颜色属性作为第一深度神经网络的输入数据，样本面料洗水前颜色属性和样本面料洗水后颜色属性作为卷积神经网络的输入数据，将第一深度神经网络和卷积神经网络的输出数据合并后作为第二深度神经网络的输入数据，样本面料洗水参数作为第二深度神经网络的输出数据。一种可实现的方式为，利用样本面料洗水前的属性信息和样本面料洗水后的属性信息以及对样本面料进行洗水的洗水参数进行模型训练，得到参数预测模型。
在本申请的另一实施例中，参数预测模型也可以由一个神经网络模型或者其他数量的模型实现。其中，在参数预测模型为一个神经网络模型的情形下，采集样本面料洗水前的属性信息和样本面料洗水后的属性信息以及对样本面料进行洗水的洗水参数对神经网络模型进行洗水参数预测训练，得到参数预测模型。
本申请实施例采用第一深度神经网络、第二深度神经网络和卷积神经网络搭建三个神经网络模型搭建，相比由一个神经网络模型搭建而言，参数预测模型的效果更佳。
在本申请的另一实施例中，可以在模型训练设备上同时搭建第一参数预测模型和第二参数预测模型，其中，第一参数预测模型由一个神经网络模型搭建而成，第二参数预测模型由三个神经网络模型搭建而成，在模型训练设备上增加选通开关选取第一参数预测模型和第二参数预测模型中的任一个进行模型训练，本申请实施例对选通开关的实现形式不作限定。
在上述实施例中，样本面料洗水前的属性信息包括以下至少一种：经纬撕裂强度、撕破性、耐磨性能、透气性、K/S值、抗压刚性、折皱回复角、面料成份占比、经纬纱规格、经纬密度、颜色值和面料重量；样本面料洗水后的属性信息包括以下至少一种：经纬撕裂强度、撕破性、耐磨性能、透气性、K/S值、抗压刚性、折皱回复角、面料成份占比、经纬纱规格、经纬密度和颜色值。
可选地，参数预测模型的一种训练方式为，将样本面料洗水前的属性信息中的颜色属性和样本面料洗水后的属性信息中的颜色属性输入卷积神经网络算法，得到样本面料洗水前后颜色属性的特征值；将样本面料洗水前的属性信息中的非颜色属性和样本面料洗水后的属性信息中的非颜色属性输入第一深度神经网络算法，得到样本面料洗水前后非颜色属性的特征值；将样本面料洗水前后颜色属性的特征值和样本面料洗水前后非颜色属性的特征值进行合并，得到样本面料洗水前后属性信息的特征值；将样本面料洗水前后属性信息的特征值输入第二深度神经网络算法，建立样本面料洗水前后属性信息的 特征值与洗水参数的映射关系，得到参数预测模型。
在本实施例中，参数预测模型的训练设备可以为服务器10a，也可以为另一台服务器。当参数预测模型的训练设备为另一台服务器时，参数预测模型的训练设备需要将训练好的参数预测模型下发至服务器10a中，以供服务器10a利用该参数预测模型获取对洗水面料进行洗水操作的洗水参数。
在服务器10a获取到训练好的参数预测模型后，服务器10a获取待洗水面料的属性信息和期望洗水后达到的目标属性信息，将待洗水面料的属性信息和期望洗水后达到的目标属性信息输入至已有的参数预测模型中，得到对待洗水面料进行洗水所需的洗水参数，之后将洗水参数洗水参数下发至控制设备10c，控制设备10c根据洗水参数控制洗水设备对待洗水面料进行洗水操作。
在上述实施例中，属性信息包括以下至少一种：经纬撕裂强度、撕破性、耐磨性能、透气性、K/S值、抗压刚性、折皱回复角、面料成份占比、经纬纱规格、经纬密度、颜色值和面料重量；目标属性信息包括以下至少一种：经纬撕裂强度、撕破性、耐磨性能、透气性、K/S值、抗压刚性、折皱回复角、面料成份占比、经纬纱规格、经纬密度和颜色值。
在上述实施例中，服务器10a获取待洗水面料的属性信息包括但不限于以下几种获取方式：
获取方式一，服务器10a接收第一面料选择请求，第一面料选择请求包括待洗水面料的标识；根据待洗水面料的标识，从数据库中获取待洗水面料的属性信息。
获取方式二，服务器10a接收检测设备在对待洗水面料进行洗水前检测并上报的待洗水面料的属性信息。
获取方式三，服务器10a接收第二面料选择请求，第二面料选择请求包括待洗水面料的标识；根据待洗水面料的标识，从数据库中获取待洗水面料的部分属性信息；服务器10a接收检测设备在对待洗水面料进行洗水前检测并上报的待洗水面料的剩余部分属性信息。
在上述获取方式一中，服务器10a预先存储多种面料的属性信息，还包括与服务器10a建立通信连接的显示设备，用户通过显示设备进行待洗水面料的选择操作，其中，显示设备具有一电子显示屏，用户通过电子显示屏与显示设备交互进行待洗水面料的选择操作，以供服务器10a获取待洗水面料的属性信息。一种可实现的方式为，响应于界 面选择操作，展示面料选择界面，面料选择界面上展示多种面料的图像；响应于对被选择的待洗水面料的图像的触发操作，向服务器10a发送第一面料选择请求，第一面料选择请求中包括待洗水面料的标识；服务器10a根据待洗水面料的标识，从数据库中获取待洗水面料的属性信息。
在上述获取方式二中，待洗水面料的属性信息还可以通过各种检测设备通过对待洗水面料实物进行检测获得，例如，通过测色仪检测待洗水面料的颜色值，通过重量传感器获取待洗水面料的面料重量等；其中，也可以在洗水设备10b上设置重量传感器，在将待洗水面料放入洗水设备10b后，洗水设备10b获取到面料重量，上传至服务器10a。
在上述获取方式三中，服务器10a通过上述方式一获取待洗水面料的部分属性信息，服务器通过上述方式二获取剩余部分属性信息。
同理，获取期望洗水后达到的目标属性信息，包括但不限于以下几种方式：
方式一，接收第二面料选择请求，第二面料选择请求包括参考面料的标识；根据参考面料的标识，从数据库中获取参考面料的属性信息，作为目标属性信息。
方式二，接收检测设备对参考面料进行检测并上报的属性信息，作为目标属性信息。
方式三，服务器10a接收第二面料选择请求，第二面料选择请求包括参考面料的标识；根据参考面料的标识，从数据库中获取参考面料的部分属性信息；服务器10a接收检测设备在对参考面料进行洗水前检测并上报的参考面料的剩余部分属性信息。
在上述方式一中，服务器10a预先存储多种参考面料的属性信息，还包括与服务器10a建立通信连接的显示设备，用户通过显示设备进行参考面料的选择操作，其中，显示设备具有一电子显示屏，用户通过电子显示屏与显示设备交互进行参考面料的选择操作，以供服务器10a获取参考面料的属性信息。一种可实现的方式为，响应于界面选择操作，展示面料选择界面，面料选择界面上展示多种面料的图像；响应于对被选择的参考面料的图像的触发操作，向服务器10a发送第二面料选择请求，第二面料选择请求中包括参考面料的标识；服务器10a根据参考面料的标识，从数据库中获取参考面料的属性信息。
在上述方式二中，参考面料的属性信息还可以通过各种检测设备通过对参考面料实物进行检测获得，例如，通过测色仪检测参考面料的颜色值，通过重量传感器获取参考面料的面料重量等；其中，也可以在洗水设备10b上设置重量传感器，在将参考面料放 入洗水设备10b后，洗水设备10b获取到面料重量，上传至服务器10a。
在上述方式三中，服务器10a通过上述方式一获取参考面料的部分属性信息，服务器通过上述方式二获取剩余部分属性信息。
结合图2所示的参数预测模型的网络结构，服务器10a获取到参数预测模型所需的输入数据后，将待洗水面料的属性信息和目标属性信息输入参数预测模型，得到对待洗水面料进行洗水所需的洗水参数。一种可选的实施例为，将待洗水面料的属性信息和目标属性信息输入参数预测模型；在参数预测模型内部，利用神经网络算法分别提取属性信息的特征值和目标属性信息的特征值；将属性信息的特征值和目标属性信息的特征值，在原始属性特征和目标属性特征与洗水参数映射关系中进行匹配，得到待洗水面料的洗水参数。
可选地，属性信息和目标属性信息分别包括：颜色属性和非颜色属性，利用神经网络算法分别提取属性信息的特征值和目标属性信息的特征值，一种可实现的方式为，利用卷积神经网络算法分别提取属性信息和目标属性信息中的颜色属性的特征值；利用第一深度神经网络算法分别提取属性信息和目标属性信息中的非颜色属性的特征值。
可选地，将属性信息和目标属性信息的特征值，在属性信息和目标属性信息的特征值与洗水参数映射关系中进行匹配，得到待洗水面料的洗水参数。一种可实现的方式为，将属性信息和目标属性信息的特征值，输入第二深度神经网络算法，得到待洗水面料的洗水参数；第二深度神经网络算法反映原始属性特征、目标属性特征与洗水参数映射关系。原始属性特征包括属性信息中的非颜色属性的特征值和属性信息中的颜色属性的特征值，目标属性特征包括目标属性信息中的非颜色属性的特征值和目标属性信息中的颜色属性的特征值，第二深度神经网络建立属性信息中的非颜色属性的特征值、属性信息中的颜色属性的特征值、目标属性信息中的非颜色属性的特征值和目标属性信息中的颜色属性的特征值与洗水参数之间的映射关系。
在上述实施例中，服务器10a仅针对当前轮次的洗水操作推荐出相应的对待洗水面料进行洗水的洗水参数，之后将该洗水参数下发至洗水设备10b。本申请对洗水设备10b如何利用该洗水参数对待洗水面料进行洗水操作不作限定，洗水设备10b根据该洗水参数对待洗水面料进行洗水操作包括但不限于下列实施方式：
结合图1a的系统架构，服务器10a在获取到对待洗水面料进行洗水所需的洗水参数后，将该洗水参数发送至控制设备10c，控制设备10c根据洗水参数生成控制指令控制 洗水设备10b自动对待洗水面料进行洗水操作。
在另一种系统架构中，可以不包括控制设备，服务器在获取到对待洗水面料进行洗水所需的洗水参数后，将洗水参数发送至洗水设备，用户可以根据该洗水参数人工执行洗水操作，也可以由洗水设备的控制器根据该洗水参数生成控制指令控制洗水设备自动对待洗水面料进行洗水操作。
需要补充说明的是，洗水设备10b利用服务器10a下发的洗水参数对待洗水面料进行洗水操作，可能并不会达到预期的洗水效果，可能在洗水过程中需要对该洗水参数作出调整，因此待洗水面料洗水至达标时所使用的实际洗水参数可能与服务器10a下发的洗水参数有所不同。因此，在每次洗水操作完成后，洗水设备10b将实际洗水参数发送至服务器10a以供服务器10a根据实际洗水参数对原有的参数预测模型进行更新，以获得更新后的参数预测模型。本申请实施例将洗水操作所产生的实际洗水参数反馈至服务器，这样服务器可以根据实际洗水参数对原有的参数预测模型进行更新，提高参数预测模型的预测结果的准确性。
图1b为本申请示例性实施例提供的另一种洗水系统的结构示意图，如图1b所示，该洗水系统包括：第一服务器20a，第二服务器20b，洗水设备20c和控制设备20d。其中，第一服务器20a是为参数预测模型提供训练服务的设备，第二服务器20b为获取对待洗水面料进行洗水的洗水参数的设备。第一服务器20a将训练好的参数预测模型发送至第二服务器20b，第二服务器20b根据待洗水面料的属性信息和期望洗水后达到的目标属性信息，将待洗水面料的属性信息和目标属性信息输入参数预测模型，得到对待洗水面料进行洗水所需的洗水参数。第二服务器20b将洗水参数发送至控制设备20d，控制设备20d根据洗水参数控制洗水设备20c对待洗水面料进行洗水操作。在本实施例中，利用参数预测模型获取对待洗水面料进行洗水的洗水参数的描述可参见前述各实施例，本实施例不再赘述。
在本申请上述洗水系统实施例中，基于机器学习，预先学习样本面料在洗水前后的属性信息与实际洗水参数的关系；根据待洗水面料的属性信息和目标属性信息，得到对待洗水面料进行洗水所需的洗水参数；并将该洗水参数下发至洗水设备，以供洗水设备根据洗水参数对待洗水面料进行洗水操作，通过参数预测模型自动获得需要对待洗水面料的洗水参数，降低人工经验对洗水的影响，降低人工成本，提高洗水效率以及洗水的成功率。
目前，洗水设备的智能化程度低，洗水效率低下。针对上述存在的技术问题，在本申请的一些实施例中，本申请实施例洗水设备包括机体，机体上设有控制器、洗剂投放组件、洗水组件与数据采集组件；洗剂投放组件，用于在控制器控制下向中空腔体内投入洗剂；洗水组件，用于在控制器控制下对待洗水面料进行洗水操作；数据采集组件，用于采集工作过程中的数据并上传至服务器，实现洗水设备的自动化洗水操作，降低人工成本，提高洗水效率，智能化程度高。
除上述提供的洗水系统之外，本申请一些实施例还提供一种洗水设备，本申请所提供的洗水设备可应用于上述的洗水系统，但并不限于上述实施例提供的洗水系统。图3为本申请示例性实施例提供的一种洗水设备的结构示意图，该洗水设备包括：机体，机体上设置有容纳待洗水面料的中空腔体以及控制洗水工序的控制器；机体上还设置有洗剂投放组件，用于在控制器控制下向中空腔体内投入洗剂；机体上还设置有洗水组件，用于在控制器控制下对待洗水面料进行洗水操作；机体上还设置有数据采集组件，用于采集工作过程中的数据并上传至服务器。在本申请洗水设备实施例中，本申请实施例洗水设备包括机体，机体上设有控制器、洗剂投放组件、洗水组件与数据采集组件；洗剂投放组件，用于在控制器控制下向中空腔体内投入洗剂；洗水组件在控制器控制下对待洗水面料进行洗水操作；数据采集组件，用于采集工作过程中的数据并上传至服务器；实现洗水设备的自动化洗水操作，降低人工成本，提高洗水效率，智能化程度高。
结合图3，洗剂投放组件包括机体上设置的洗剂投放口和洗剂投放口内设置的流量传感器；洗剂投放口与中空腔体贯通设置，接收控制器指令能够开启或闭合以向中空腔体内投入洗剂；流量传感器，用于检测中空腔体内的洗剂投入量，并将洗剂投入量上报至控制器；本申请实施例洗水设备整体结构简单紧凑，智能化程度高，降低人工成本，提高洗水效率。
进一步，本申请实施例数据采集组件包括设置于中空腔体底部的重量传感器；重量传感器检测放入中空腔体内的面料的重量，并上传至服务器，以供服务器根据重量确定并向控制器下发洗水参数。本申请实施例洗水装置增设重量传感器，在待洗水面料放入中空腔体后自动将面料的重量上传至服务器，重量传感器的位置设置合理，提高设备自动化程度。
在本实施例中，洗水设备的洗剂投放口包括以下至少一种投放口：酵素洗剂投放 口、漂白洗剂投放口和水投放口；每种投放口内分别设有流量传感器，分别用于检测酵素洗剂、漂白洗剂和水的投入量；控制器在各流量传感器上报的酵素洗剂、漂白洗剂和水的投入量达到洗水要求的投入量时控制对应的酵素洗剂投放口、漂白洗剂投放口和水投放口关闭。一种可实现的方式为，洗水设备的洗剂投放口包括酵素洗剂投放口、漂白洗剂投放口和水投放口，酵素洗剂投放口、漂白洗剂投放口和水投放口处分别设有能够移动的挡板，挡板能够移动实现洗剂投放口的开启关闭。控制器通过控制酵素洗剂投放口、漂白洗剂投放口和水投放口的挡板的开启关闭实现自动定量投放洗剂的目的，智能化程度高。优选地，酵素洗剂投放口、漂白洗剂投放口和水投放口并排设于机体一侧的顶部。
在本实施例中，该洗水设备还包括设置于机体顶部的水温设定组件、转速设定组件和定时组件；水温设定组件用于在控制器控制下调节中空腔体内洗剂的温度。定时组件用于在控制器控制下调节洗水转速；定时组件用于在控制器控制下调节每种工艺的时长。
在本申请实施例中，洗水参数包括以下至少一种：工艺流程顺序，水温，转速，每种洗水工序的时长和洗剂投入量。在洗水的各工艺流程中按照该工艺流程下的参数进行洗水操作。
图4为本申请示例性实施例提供的一种洗水方法的流示意图。如图4所示，该方法包括：
S401：根据洗水参数，生成酵素洗指令和漂洗指令；
S402：根据酵素洗指令，控制洗水设备上的洗剂投放组件向容纳待洗水面料的中空腔体内投入酵素洗所需的洗剂，并控制洗水设备上的洗水组件对待洗水面料进行酵素洗操作；
S403：在酵素洗操作完成后，根据漂洗指令，控制洗剂投放组件向中空腔体内投入漂洗所需的洗剂，并控制洗水组件对待洗水面料进行漂洗操作。
在本实施例中，洗水设备接收服务器洗发的洗水参数，其中，洗水参数是根据待洗水面料的属性信息和洗水效果对应的目标属性信息预测得到的，或者是根据衣物定制方要求的洗水效果计算获得的。
在本实施例中，洗水设备还包括：水温设定组件、转速设定组件和定时组件中至少一个组件。在控制洗水组件对待洗水面料进行酵素洗或漂洗操作之前，方法还包括以下 至少一种操作：通过水温设定组件设置中空腔体内的洗剂在酵素洗或漂洗操作中的温度；通过转速设定组件设置洗水组件的洗水转速；通过定时组件设置洗水组件在执行每种酵素洗或漂洗工序的时长。
在本实施例中，洗剂投放组件包括洗剂投放口和洗剂投放口内设置的流量传感器；根据酵素洗指令或漂洗指令，控制洗水设备上的洗剂投放组件向容纳待洗水面料的中空腔体内投入酵素洗或漂洗所需的洗剂，一种可实现的方式为，根据酵素洗指令或漂洗指令，控制洗剂投放口开启以向中空腔体内投入酵素洗或漂洗所需的洗剂；获取流量传感器检测到的中空腔体内的洗剂投入量，并在洗剂投放量达到酵素洗或漂洗要求的投入量时控制洗剂投放口关闭。
图5为本申请示例性实施例提供的一种牛仔裤洗水工艺流程的示意图。如图5所示，该牛仔裤洗水工艺流程分为酵素洗工艺和漂洗工艺。下面结合图5对本申请洗水工艺完整流程进行说明。
首先，初始化机器参数；接着，测色仪获取参考牛仔裤和待洗水牛仔裤的颜色值；接着，上传服务器待洗水牛仔裤重量，获取对待洗水牛仔裤洗水的洗水参数，接着，控制器根据洗水参数生成酵素洗指令，加入洗剂，调整水温、转速和时间，开始酵素洗流程；接着，酵素洗流程完成后，对待洗水牛仔裤进行脱水；之后，判断是否与预测结果一致，若是则进入后续漂洗流程，若否则重新调整参数返回至酵素洗流程。在漂洗流程中，控制器根据洗水参数生成酵素洗指令，加入洗剂，调整水温、转速和时间，开始漂洗流程；在漂洗流程完成后，对待洗水牛仔裤进行烘干，之后，判断是否与预测结果一致，若是完成洗水工艺，若否则重新调整参数返回至漂洗流程。
需要说明的是，相比于传统的洗水设备型号单一，本申请实施例的洗水设备可以根据不同的洗水需求，在工厂内配置个性化的的设备，以及自动为接入订单分配不同的设备。例如，可以设置不同洗水容量的设备，当工厂接入订单时，自动匹配满足接入订单洗水容量的设备，将通过生产线输入至匹配中洗水设备进行洗水操作；也可以为设置不同洗水效果的设备，当工厂接入订单时，首先确定该批次订单的洗水效果，将该批次订单分配至能够洗水出该批次的订单的洗水效果的设备进行洗水。
进一步说明，基于上述洗水系统、洗水设备和洗水参数获取的实施例，可以按照上述设计思路结合现有染色领域的技术，完全能够提供一套自动化程度较高的染色系统、染色设备和染色参数获取的方法，关于此部分内容在此不再赘述。
在本申请的上述洗水设备实施例中，本申请实施例洗水设备包括机体，机体上增设有控制器以及洗剂投放口；洗剂投放口内设置有流量传感器；流量传感器用于检测洗剂投入量，通过洗剂投放口能够向中空腔体内自动定量投入洗剂，控制器在流量传感器上报的洗剂投入量达到洗水要求的投入量时控制洗剂投放口闭合，洗水设备实现洗剂的自动定量投放，降低人工成本，提高洗水效率，智能化程度高。
图6为本申请示例性实施例提供的一种订单处理方法的流程示意图。如图6所示，该订单处理方法包括：
S601：获取待洗水订单的面料总量；
S602：从能够为面料总量提供洗水操作的至少一台洗水设备中确定对待洗水订单进行洗水的目标洗水设备；
S603：将与该待洗水订单相应的洗水参数发送至目标洗水设备，以供目标洗水设备根据洗水参数对待洗水面料进行洗水操作。
在本实施例中，智能化工厂中的洗水设备可以按照洗水容量的不同分为不同类型的设备。在获取待洗水订单的面料总量后，从能够为面料总量提供洗水操作的至少一台洗水设备中确定对待洗水订单进行洗水的目标洗水设备；并将该待洗水订单相应的洗水参数发送至目标洗水设备，以供目标洗水设备根据洗水参数对待洗水面料进行洗水操作。
除上述提供的洗水系统和洗水设备之外，本申请一些实施例还提供一种洗水参数获取方法，本申请所提供的洗水参数获取方法可应用于上述洗水系统和洗水设备中，但不限于上述实施例提供的洗水系统及洗水设备。同理，上述洗水设备可以根据本申请实施例提供的洗水参数自动进行洗水操作，也可以按照其他方式提供的洗水参数自动进行洗水操作。例如，可以采用人工参与的方式设定洗水参数，将洗水参数下发给本申请实施例提供的洗水设备，使得洗水设备按照洗水参数自动进行洗水操作。
图7为本申请示例性实施例提供的一种洗水参数获取方法的流程示意图。如图7所示，该方法包括：
S701：确定待洗水面料的洗水效果；
S702：获取待洗水面料的属性信息；
S703：基于预先确定的洗水参数预测模型，对待洗水面料的属性信息和洗水效果对应的目标属性信息进行处理，得到对待洗水面料进行洗水所需的洗水参数。
在本申请实施例中，本申请的执行主体可以为多个具有数据支持、计算服务以及一 些管理服务的服务器，在本实施例中，并不限定服务器的实现形态，例如，服务器可以是常规服务器、云服务器、云主机、虚拟中心等服务器设备。其中，服务器设备的构成主要包括处理器、硬盘、内存、系统总线等，和通用的计算机架构类型。服务器包含一台网站服务器，也可以包含多台网站服务器。
在本实施例中，在本实施例中，基于预先确定的洗水参数预测模型，对待洗水面料的属性信息和洗水效果对应的目标属性信息进行处理，得到对待洗水面料进行洗水所需的参数。可选地，需要预先训练得到参数预测模型，基于训练得到的参数预测模型获取当前洗水轮次的洗水参数。在模型训练前，采集样本面料洗水前的属性信息和样本面料洗水后的属性信息以及样本面料进行洗水的洗水参数作为样本数据集，并对样本数据集进行标注形成标注数据集进行模型训练。
其中，关于参数预测模型的训练过程以及训练得到的参数预测模型的一种实现结构可参见图2所示。如图2所示，本申请实施例参数预测模型的网络结构由第一深度神经网络、第二深度神经网络和卷积神经网络搭建而成；其中，样本面料洗水前非颜色属性和样本面料洗水后非颜色属性作为第一深度神经网络的输入数据，样本面料洗水前颜色属性和样本面料洗水后颜色属性作为卷积神经网络的输入数据，将第一深度神经网络和卷积神经网络的输出数据合并后作为第二深度神经网络的输入数据，样本面料洗水参数作为第二深度神经网络的输出数据。一种可实现的方式为，利用样本面料洗水前的属性信息和样本面料洗水后的属性信息以及对样本面料进行洗水的洗水参数进行模型训练，得到参数预测模型。
在上述实施例中，样本面料洗水前的属性信息包括以下至少一种：经纬撕裂强度、撕破性、耐磨性能、透气性、K/S值、抗压刚性、折皱回复角、面料成份占比、经纬纱规格、经纬密度、颜色值和面料重量；样本面料洗水后的属性信息包括以下至少一种：经纬撕裂强度、撕破性、耐磨性能、透气性、K/S值、抗压刚性、折皱回复角、面料成份占比、经纬纱规格、经纬密度和颜色值。
可选地，参数预测模型的一种训练方式为，将样本面料洗水前的属性信息中的颜色属性和样本面料洗水后的属性信息中的颜色属性输入卷积神经网络算法，得到样本面料洗水前后颜色属性的特征值；将样本面料洗水前的属性信息中的非颜色属性和样本面料洗水后的属性信息中的非颜色属性输入第一深度神经网络算法，得到样本面料洗水前后非颜色属性的特征值；将样本面料洗水前后颜色属性的特征值和样本面料洗水前后非颜 色属性的特征值进行合并，得到样本面料洗水前后属性信息的特征值；将样本面料洗水前后属性信息的特征值输入第二深度神经网络算法，建立样本面料洗水前后属性信息的特征值与洗水参数的映射关系，得到参数预测模型。
在本实施例中，参数预测模型的训练设备可以为获取洗水参数的服务器，也可以为另一台服务器。当参数预测模型的训练设备为另一台服务器时，参数预测模型的训练设备需要将训练好的参数预测模型下发至获取洗水参数的服务器中，以供获取洗水参数的服务器利用该参数预测模型获取对洗水面料进行洗水操作的洗水参数。
在获取到训练好的参数预测模型后，获取待洗水面料的属性信息和期望洗水后达到的目标属性信息，将待洗水面料的属性信息和期望洗水后达到的目标属性信息输入至已有的参数预测模型中，得到对待洗水面料进行洗水所需的洗水参数，之后将洗水参数洗水参数下发至控制设备，控制设备根据洗水参数控制洗水设备对待洗水面料进行洗水操作。
在上述实施例中，属性信息包括以下至少一种：经纬撕裂强度、撕破性、耐磨性能、透气性、K/S值、抗压刚性、折皱回复角、面料成份占比、经纬纱规格、经纬密度、颜色值和面料重量；目标属性信息包括以下至少一种：经纬撕裂强度、撕破性、耐磨性能、透气性、K/S值、抗压刚性、折皱回复角、面料成份占比、经纬纱规格、经纬密度和颜色值。
在本实施例中，确定待洗水面料的洗水效果包括但不限于以下几种方式：
方式一，响应于拍照操作，获取目标面料的图片，作为待洗水面料的洗水效果。
方式二，响应于图片导入操作，从图片库中获取目标面料的图片，作为待洗水面料的洗水效果。
在本实施例中，在对待洗水面料的属性信息和洗水效果对应的目标属性信息进行处理，得到对待洗水面料进行洗水所需的洗水参数之前，从目标面料的图片中，提取洗水效果对应的目标属性信息。
在上述实施例中，获取待洗水面料的属性信息包括但不限于以下几种方式：
获取方式一，接收第二面料选择请求，第二面料选择请求包括待洗水面料的标识；根据待洗水面料的标识，从数据库中获取待洗水面料的属性信息。
获取方式二，接收检测设备在对待洗水面料进行洗水前检测并上报的待洗水面料的属性信息。
获取方式三，接收第二面料选择请求，第二面料选择请求包括待洗水面料的标识；根据待洗水面料的标识，从数据库中获取待洗水面料的部分属性信息；接收检测设备在对待洗水面料进行洗水前检测并上报的待洗水面料的剩余部分属性信息。
在上述获取方式一中，服务器预先存储多种面料的属性信息，还包括与服务器建立通信连接的显示设备，用户通过显示设备进行待洗水面料的选择操作，其中，显示设备具有一电子显示屏，用户通过电子显示屏与显示设备交互进行待洗水面料的选择操作，以供获取待洗水面料的属性信息。一种可实现的方式为，响应于界面选择操作，展示面料选择界面，面料选择界面上展示多种面料的图像；响应于对被选择的待洗水面料的图像的触发操作，向服务器发送第二面料选择请求，第二面料选择请求中包括待洗水面料的标识；服务器根据待洗水面料的标识，从数据库中获取待洗水面料的属性信息。
在上述获取方式二中，待洗水面料的属性信息还可以通过各种检测设备通过对待洗水面料实物进行检测获得，例如，通过测色仪检测待洗水面料的颜色值，通过重量传感器获取待洗水面料的面料重量等；其中，也可以在洗水设备上设置重量传感器，在将待洗水面料放入洗水设备后，洗水设备获取到面料重量，上传至服务器。
在上述获取方式三中，通过上述方式一获取待洗水面料的部分属性信息，服务器通过上述方式二获取剩余部分属性信息。
同理，获取期望洗水后达到的目标属性信息，包括但不限于以下几种方式：
方式一，接收第一面料选择请求，第一面料选择请求包括参考面料的标识；根据参考面料的标识，从数据库中获取参考面料的属性信息，作为目标属性信息。
方式二，接收检测设备对参考面料进行检测并上报的属性信息，作为目标属性信息。
方式三，服务器接收第二面料选择请求，第二面料选择请求包括参考面料的标识；根据参考面料的标识，从数据库中获取参考面料的部分属性信息；服务器接收检测设备在对参考面料进行洗水前检测并上报的参考面料的剩余部分属性信息。
在上述方式一中，服务器预先存储多种参考面料的属性信息，还包括与服务器建立通信连接的显示设备，用户通过显示设备进行参考面料的选择操作，其中，显示设备具有一电子显示屏，用户通过电子显示屏与显示设备交互进行参考面料的选择操作，以供服务器获取参考面料的属性信息。一种可实现的方式为，响应于界面选择操作，展示面料选择界面，面料选择界面上展示多种面料的图像；响应于对被选择的参考面料的图像 的触发操作，向服务器发送第一面料选择请求，第一面料选择请求中包括参考面料的标识；服务器根据参考面料的标识，从数据库中获取参考面料的属性信息。
在上述方式二中，参考面料的属性信息还可以通过各种检测设备通过对参考面料实物进行检测获得，例如，通过测色仪检测参考面料的颜色值，通过重量传感器获取参考面料的面料重量等；其中，也可以在洗水设备上设置重量传感器，在将参考面料放入洗水设备后，洗水设备获取到面料重量，上传至服务器。
在上述方式三中，服务器通过上述方式一获取参考面料的部分属性信息，服务器通过上述方式二获取剩余部分属性信息。
结合图2所示的参数预测模型的网络结构，获取到参数预测模型所需的输入数据后，将待洗水面料的属性信息和目标属性信息输入参数预测模型，得到对待洗水面料进行洗水所需的洗水参数。一种可选的实施例为，将待洗水面料的属性信息和目标属性信息输入参数预测模型；在参数预测模型内部，利用神经网络算法分别提取属性信息的特征值和目标属性信息的特征值；将属性信息的特征值和目标属性信息的特征值，在原始属性特征和目标属性信息特征与洗水参数映射关系中进行匹配，得到待洗水面料的洗水参数。
可选地，属性信息和目标属性信息分别包括：颜色属性和非颜色属性，利用神经网络算法分别提取属性信息的特征值和目标属性信息的特征值，一种可实现的方式为，利用卷积神经网络算法分别提取属性信息和目标属性信息中的颜色属性的特征值；利用第一深度神经网络算法分别提取属性信息和目标属性信息中的非颜色属性的特征值。
可选地，将属性信息和目标属性信息的特征值，在属性信息和目标属性信息的特征值与洗水参数映射关系中进行匹配，得到待洗水面料的洗水参数。一种可实现的方式为，将属性信息和目标属性信息的特征值，输入第二深度神经网络算法，得到待洗水面料的洗水参数；第二深度神经网络算法反映原始属性特征、目标属性特征与洗水参数映射关系。原始属性特征包括属性信息中的非颜色属性的特征值和属性信息中的颜色属性的特征值，目标属性特征包括目标属性信息中的非颜色属性的特征值和目标属性信息中的颜色属性的特征值，第二深度神经网络建立属性信息中的非颜色属性的特征值、属性信息中的颜色属性的特征值、目标属性信息中的非颜色属性的特征值和目标属性信息中的颜色属性的特征值与洗水参数之间的映射关系。
在上述实施例中，仅针对当前轮次的洗水操作推荐出相应的对待洗水面料进行洗水 的洗水参数，之后将该洗水参数下发至洗水设备。本申请对洗水设备如何利用该洗水参数对待洗水面料进行洗水操作不作限定，洗水设备根据该洗水参数对待洗水面料进行洗水操作包括但不限于下列实施方式：
结合图1a的系统架构，服务器10a在获取到对待洗水面料进行洗水所需的洗水参数后，将该洗水参数发送至控制设备10c，控制设备10c根据洗水参数生成控制指令控制洗水设备10b自动对待洗水面料进行洗水操作。
在另一种系统架构中，可以不包括控制设备，服务器在获取到对待洗水面料进行洗水所需的洗水参数后，将洗水参数发送至洗水设备，用户可以根据该洗水参数人工执行洗水操作，也可以由洗水设备的控制器根据该洗水参数生成控制指令控制洗水设备自动对待洗水面料进行洗水操作。
需要补充说明的是，洗水设备利用服务器下发的洗水参数对待洗水面料进行洗水操作，可能并不会达到预期的洗水效果，可能在洗水过程中需要对该洗水参数作出调整，因此待洗水面料洗水至达标时所使用的使用的实际洗水参数可能与服务器下发的洗水参数有所不同。因此，在每次洗水操作完成后，洗水设备将实际洗水参数发送至服务器以供服务器根据实际洗水参数对原有的参数预测模型进行更新，以获得更新后的参数预测模型。本申请实施例将洗水操作所产生的实际洗水参数反馈至服务器度对原有的参数预测模型进行更新，提高参数预测的准确性。
在本申请上述洗水参数获取方法实施例中，基于机器学习，预先学习样本面料在洗水前后的属性信息与实际洗水参数的关系；根据待洗水面料的属性信息和目标属性信息，得到对待洗水面料进行洗水所需的洗水参数；并将该洗水参数下发至洗水设备，以供洗水设备根据洗水参数对待洗水面料进行洗水操作，通过参数预测模型自动获得需要对待洗水面料的洗水参数，降低人工经验对洗水的影响，降低人工成本，提高洗水效率以及洗水的成功率。
基于以上各实施例部分的描述，图8为本申请实施例提供的一种面料的洗水参数预测模型的训练方法，该方法包括：
S801：获取样本面料洗水前的属性信息和样本面料洗水后的属性信息以及对样本面料进行洗水的洗水参数；
S802：利用样本面料洗水前的属性信息和样本面料洗水后的属性信息以及对样本面料进行洗水的洗水参数进行洗水参数预测训练，得到参数预测模型。
结合以上各实施例部分的描述，从人机交互的角度描述，图9为本申请实施例提供的一种洗水参数获取方法的方法流程图，如图9所示，该方法包括：
S901：响应于界面展示操作，展示一人机交互界面；
S902：响应于在人机交互界面上的待洗水面料的洗水效果获取操作，获取目标面料的图片，作为待洗水面料的洗水效果；
S903：响应于在人机交互界面上的属性信息输入操作，获取待洗水面料的属性信息；
S904：响应于对界面上的洗水参数预测操作，得到对待洗水面料进行洗水所需的洗水参数。
在本实施例中，响应于在人机交互界面上的待洗水面料的洗水效果获取操作，获取目标面料的图片。包括但不限于以下两种方式：
方式一，响应于拍照操作，获取目标面料的图片；
方式二，响应于图片导入操作，从图片库中获取目标面料的图片。
在本实施例中，响应于在人机交互界面上的属性信息输入操作，获取待洗水面料的属性信息，包括：
方式一，响应于在人机交互界面上的属性信息输入项中输入待洗水面料的属性信息的操作，获取待洗水面料的属性信息；
方式二，响应于在人机交互界面上的待洗水面料的选择操作，从面料数据库中获取被选中的洗水面料的属性信息。
本实施例中，在获取到对待洗水面料进行洗水所需的洗水参数后，展示对待洗水面料进行洗水所需的洗水参数。
图10为本申请一示例性实施例提供的一种服务器的结构示意图。如图10所示，该数据处理设备包括：存储器1001和处理器1002。另外，该数据处理设备还包括通信组件1003和电源组件1004等必须组件。
存储器1001，用于存储计算机程序，并可被配置为存储其它各种数据以支持在数据处理设备上的操作。这些数据的示例包括用于在数据处理设备上操作的任何应用程序或方法的指令。
存储器1001，可以由任何类型的易失性或非易失性存储设备或者它们的组合实现， 如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
通信组件1003，用于将洗水参数下发至洗水设备。
处理器1002，可执行存储器1001中存储的计算机指令，以用于：确定待洗水面料的洗水效果；获取待洗水面料的属性信息；基于预先确定的洗水参数预测模型，对待洗水面料的属性信息和洗水效果对应的目标属性信息进行处理，得到对待洗水面料进行洗水所需的洗水参数。
可选地，处理器1002确定待洗水面料的洗水效果，具体用于：响应于拍照操作，获取目标面料的图片，作为待洗水面料的洗水效果；或者，响应于图片导入操作，从图片库中获取目标面料的图片，作为待洗水面料的洗水效果。
可选地，处理器1002在对待洗水面料的属性信息和洗水效果对应的目标属性信息进行处理，得到对待洗水面料进行洗水所需的洗水参数之前，还可用于：从目标面料的图片中，提取洗水效果对应的目标属性信息。
可选地，处理器1002在获取待洗水面料的属性信息时，具体用于：接收第一面料选择请求，第一面料选择请求包括待洗水面料的标识；根据待洗水面料的标识，从数据库中获取待洗水面料的属性信息；或者，接收检测设备在对待洗水面料进行洗水前检测并上报的待洗水面料的属性信息。
可选地，处理器1002在获取待洗水面料的属性信息时，还可用于：将洗水参数发送至洗水设备，以供洗水设备根据洗水参数对待洗水面料进行洗水操作；或者，将洗水参数发送至控制设备，以供控制设备根据洗水参数控制洗水设备对待洗水面料进行洗水操作。
可选地，处理器1002在基于预先确定的洗水参数预测模型，对待洗水面料的属性信息和洗水效果对应的目标属性信息进行处理，得到对待洗水面料进行洗水所需的洗水参数时，具体用于：将待洗水面料的属性信息和目标属性信息输入参数预测模型；在参数预测模型内部，利用神经网络算法分别提取属性信息的特征值和目标属性信息的特征值；将属性信息的特征值和目标属性信息的特征值，在原始属性特征、目标属性特征与洗水参数映射关系中进行匹配，得到待洗水面料的洗水参数。
可选地，属性信息和目标属性信息分别包括：颜色属性和非颜色属性；处理器1002 在利用神经网络算法分别提取属性信息的特征值和目标属性信息的特征值时，具体用于：利用卷积神经网络算法分别提取属性信息和目标属性信息中的颜色属性的特征值；利用第一深度神经网络算法分别提取属性信息和目标属性信息中的非颜色属性的特征值。
可选地，处理器1002在将属性信息和目标属性信息的特征值，在属性信息和目标属性信息的特征值与洗水参数映射关系中进行匹配，得到待洗水面料的洗水参数时，具体用于：将属性信息和目标属性信息的特征值，输入第二深度神经网络算法，得到待洗水面料的洗水参数；第二深度神经网络算法反映原始属性特征、目标属性特征与洗水参数映射关系。
可选地，属性信息包括以下至少一种：经纬撕裂强度、撕破性、耐磨性能、透气性、K/S值、抗压刚性、折皱回复角、面料成份占比、经纬纱规格、经纬密度、颜色值和面料重量；目标属性信息包括以下至少一种：经纬撕裂强度、撕破性、耐磨性能、透气性、K/S值、抗压刚性、折皱回复角、面料成份占比、经纬纱规格、经纬密度和颜色值。
可选地，处理器1002在将洗水参数发送至洗水设备之后，还可用于：
接收洗水设备发送的实际洗水参数，对参数预测模型进行更新；实际洗水参数是洗水设备将待洗水面料洗水至达标时实际使用的洗水参数。
可选地，处理器1002在使用参数预测模型之前，还可用于：
利用样本面料洗水前的属性信息和样本面料洗水后的属性信息以及对样本面料进行洗水的洗水参数进行模型训练，得到参数预测模型。
可选地，处理器1002在利用样本面料洗水前的属性信息和样本面料洗水后的属性信息以及对样本面料进行洗水的洗水参数进行模型训练，得到参数预测模型，具体用于：将样本面料洗水前的属性信息中的颜色属性和样本面料洗水后的属性信息中的颜色属性输入卷积神经网络算法，得到样本面料洗水前后颜色属性的特征值；将样本面料洗水前的属性信息中的非颜色属性和样本面料洗水后的属性信息中的非颜色属性输入第一深度神经网络算法，得到样本面料洗水前后非颜色属性的特征值；将样本面料洗水前后颜色属性的特征值和样本面料洗水前后非颜色属性的特征值输入第二深度神经网络算法，建立原始属性特征、目标属性特征与洗水参数的映射关系，得到参数预测模型。
相应地，本申请实施例还提供一种存储有计算机程序的计算机可读存储介质。当计 算机可读存储介质存储计算机程序，且计算机程序被一个或多个处理器执行时，致使一个或多个处理器执行图7所示方法实施例中的各步骤。
图11为本申请一示例性实施例提供的一种服务器的结构示意图。如图11所示，该数据处理设备包括：存储器1101和处理器1102。另外，该数据处理设备还包括通信组件1103和电源组件1104等必须组件。
存储器1101，用于存储计算机程序，并可被配置为存储其它各种数据以支持在数据处理设备上的操作。这些数据的示例包括用于在数据处理设备上操作的任何应用程序或方法的指令。
存储器1101，可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
通信组件1103，用于与其他设备进行通信。
处理器1102，可执行存储器1101中存储的计算机指令，以用于：获取待洗水订单的面料总量；从能够为面料总量提供洗水操作的至少一台洗水设备中确定对待洗水订单进行洗水的目标洗水设备；将与该待洗水订单相应的洗水参数发送至目标洗水设备，以供目标洗水设备根据洗水参数对待洗水面料进行洗水操作。
相应地，本申请实施例还提供一种存储有计算机程序的计算机可读存储介质。当计算机可读存储介质存储计算机程序，且计算机程序被一个或多个处理器执行时，致使一个或多个处理器执行图6所示方法实施例中的各步骤。
图12为本申请一示例性实施例提供的一种模型训练设备的结构示意图。如图12所示，该数据处理设备包括：存储器1201和处理器1202。另外，该数据处理设备还包括通信组件1203和电源组件1204等必须组件。
存储器1201，用于存储计算机程序，并可被配置为存储其它各种数据以支持在数据处理设备上的操作。这些数据的示例包括用于在数据处理设备上操作的任何应用程序或方法的指令。
存储器1201，可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器， 快闪存储器，磁盘或光盘。
通信组件1203，用于与其他设备进行通信。
处理器1202，可执行存储器1201中存储的计算机指令，以用于：获取样本面料洗水前的属性信息和样本面料洗水后的属性信息以及对样本面料进行洗水的洗水参数；利用样本面料洗水前的属性信息和样本面料洗水后的属性信息以及对样本面料进行洗水的洗水参数进行洗水参数预测训练，得到参数预测模型。
相应地，本申请实施例还提供一种存储有计算机程序的计算机可读存储介质。当计算机可读存储介质存储计算机程序，且计算机程序被一个或多个处理器执行时，致使一个或多个处理器执行图8所示方法实施例中的各步骤。
图12为本申请一示例性实施例提供的一种模型训练设备的结构示意图。如图12所示，该数据处理设备包括：存储器1201和处理器1202。另外，该数据处理设备还包括通信组件1203和电源组件1204等必须组件。
存储器1201，用于存储计算机程序，并可被配置为存储其它各种数据以支持在数据处理设备上的操作。这些数据的示例包括用于在数据处理设备上操作的任何应用程序或方法的指令。
存储器1201，可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
通信组件1203，用于与其他设备进行通信。
处理器1202，可执行存储器1201中存储的计算机指令，以用于：响应于界面展示操作，展示一人机交互界面；响应于在人机交互界面上的待洗水面料的洗水效果获取操作，获取目标面料的图片，作为待洗水面料的洗水效果；响应于在人机交互界面上的属性信息输入操作，获取待洗水面料的属性信息；响应于对界面上的洗水参数预测操作，得到对待洗水面料进行洗水所需的洗水参数。
相应地，本申请实施例还提供一种存储有计算机程序的计算机可读存储介质。当计算机可读存储介质存储计算机程序，且计算机程序被一个或多个处理器执行时，致使一个或多个处理器执行图9所示方法实施例中的各步骤。
上述图10-图12中的通信组件被配置为便于通信组件所在设备和其他设备之间有线 或无线方式的通信。通信组件所在设备可以接入基于通信标准的无线网络，如WiFi，2G或3G，或它们的组合。在一个示例性实施例中，通信组件经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，通信组件还包括近场通信(NFC)技术、射频识别(RFID)技术、红外数据协会(IrDA)技术、超宽带(UWB)技术和蓝牙(BT)技术等，以促进短程通信。
上述图10-图12中的电源组件，为电源组件所在设备的各种组件提供电力。电源组件可以包括电源管理系统，一个或多个电源，及其他与为电源组件所在设备生成、管理和分配电力相关联的组件。
在本实施例中，基于机器学习，预先学习样本面料在洗水前后的属性信息与实际洗水参数的关系；根据待洗水面料的属性信息和目标属性信息，得到对待洗水面料进行洗水所需的洗水参数；并将该洗水参数下发至洗水设备，以供洗水设备根据洗水参数对待洗水面料进行洗水操作，通过参数预测模型自动获得需要对待洗水面料的洗水参数，降低人工经验对洗水的影响，降低人工成本，提高洗水效率以及洗水的成功率。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
在一个典型的配置中，计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
内存可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。内存是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
还需要说明的是，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、商品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、商品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、商品或者设备中还存在另外的相同要素。
以上所述仅为本申请的实施例而已，并不用于限制本申请。对于本领域技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原理之内所作的任何修改、等同替换、改进等，均应包含在本申请的权利要求范围之内。

Claims (17)

  1. 一种洗水设备，其特征在于，包括：机体，机体上设置有容纳待洗水面料的中空腔体以及控制洗水工序的控制器；
    所述机体上还设置有洗剂投放组件，用于在控制器控制下向所述中空腔体内投入洗剂；
    所述机体上还设置有洗水组件，用于在控制器控制下对待洗水面料进行洗水操作；
    所述机体上还设置有数据采集组件，用于采集工作过程中的数据并上传至服务器。
  2. 根据权利要求1所述的设备，其特征在于，所述洗剂投放组件包括机体上设置的洗剂投放口和洗剂投放口内设置的流量传感器；
    所述洗剂投放口与所述中空腔体贯通设置，在控制器控制下能够开启或闭合以向中空腔体内投入洗剂；
    所述流量传感器，用于检测中空腔体内的洗剂投入量，并将洗剂投入量上报至控制器。
  3. 根据权利要求1所述的设备，其特征在于，所述数据采集组件包括中空腔体底部的重量传感器；所述重量传感器检测放入所述中空腔体内的面料的重量，并上传至服务器，以供所述服务器根据所述重量确定并向所述控制器下发洗水参数。
  4. 根据权利要求2所述的设备，其特征在于，所述洗剂投放口包括以下至少一种投放口：酵素洗剂投放口、漂白洗剂投放口和水投放口；每种投放口内分别设有流量传感器，用于检测相应种类的洗剂投入量并上报所述控制器；所述控制器用于在各流量传感器上报的相应种类的洗剂投入量达到要求的投入量时控制相应种类的洗剂投放口关闭。
  5. 根据权利要求4所述的设备，其特征在于，所述洗剂投放口包括酵素洗剂投放口、漂白洗剂投放口和水投放口；所述酵素洗剂投放口、漂白洗剂投放口和水投放口并排设于机体一侧的顶部。
  6. 根据权利要求4所述的设备，其特征在于，所述酵素洗剂投放口、漂白洗剂投放口和水投放口处分别设有能够移动的挡板，挡板能够移动实现洗剂投放口的开启关闭。
  7. 根据权利要求1所述的设备，其特征在于，还包括：设置于机体顶部的水温设定组件；所述水温设定组件在控制器控制下调节中空腔体内洗剂的温度。
  8. 根据权利要求1所述的设备，其特征在于，还包括：设置于机体顶部的转速设 定组件，所述转速设定组件在控制器控制下调节洗水组件的洗水转速。
  9. 根据权利要求1所述的设备，其特征在于，还包括：设置于机体顶部的定时组件，所述定时组件在控制器控制下调节每种洗水工序的时长。
  10. 根据权利要求1或2所述的设备，其特征在于，所述洗水参数包括以下至少一种：工艺流程顺序，水温，转速，每种洗水工序的时长和洗剂投入量。
  11. 一种洗水方法，适用于洗水设备，其特征在于，所述方法包括：
    根据洗水参数，生成酵素洗指令和漂洗指令；
    根据酵素洗指令，控制所述洗水设备上的洗剂投放组件向容纳待洗水面料的中空腔体内投入酵素洗所需的洗剂，并控制所述洗水设备上的洗水组件对所述待洗水面料进行酵素洗操作；
    在酵素洗操作完成后，根据漂洗指令，控制所述洗剂投放组件向所述中空腔体内投入漂洗所需的洗剂，并控制所述洗水组件对所述待洗水面料进行漂洗操作。
  12. 根据权利要求11所述的方法，其特征在于，所述洗剂投放组件包括洗剂投放口和洗剂投放口内设置的流量传感器；
    根据酵素洗指令或漂洗指令，控制所述洗水设备上的洗剂投放组件向容纳待洗水面料的中空腔体内投入酵素洗或漂洗所需的洗剂，包括：
    根据酵素洗指令或漂洗指令，控制洗剂投放口开启以向中空腔体内投入酵素洗或漂洗所需的洗剂；
    获取所述流量传感器检测到的中空腔体内的洗剂投入量，并在所述洗剂投放量达到酵素洗或漂洗要求的投入量时控制洗剂投放口关闭。
  13. 根据权利要求11所述的方法，其特征在于，所述洗水设备还包括：水温设定组件、转速设定组件和定时组件中至少一个组件；
    在控制洗水组件对待洗水面料进行酵素洗或漂洗操作之前，所述方法还包括以下至少一种操作：
    通过水温设定组件设置中空腔体内的洗剂在酵素洗或漂洗操作中的温度；
    通过转速设定组件设置所述洗水组件的洗水转速；
    通过定时组件设置所述洗水组件在执行每种酵素洗或漂洗工序的时长。
  14. 根据权利要求11所述的方法，其特征在于，在根据洗水参数，生成酵素洗指令和漂洗指令之前，还包括：
    接收服务器下发的所述洗水参数，其中，所述洗水参数是根据待洗水面料的属性信 息和洗水效果对应的目标属性信息预测得到的，或者是根据衣物定制方要求的洗水效果计算获得的。
  15. 一种订单处理方法，其特征在于，包括：
    获取待洗水订单的面料总量；
    从能够为所述面料总量提供洗水操作的至少一台洗水设备中确定对所述待洗水订单进行洗水的目标洗水设备；
    将与该待洗水订单相应的洗水参数发送至目标洗水设备，以供目标洗水设备根据所述洗水参数对待洗水面料进行洗水操作。
  16. 一种服务器，其特征在于，包括：存储器和处理器；
    所述存储器，用于存储一条或多条计算机指令；
    所述处理器，用于执行所述一条或多条计算机指令以用于：
    获取待洗水订单的面料总量；
    从能够为所述面料总量提供洗水操作的至少一台洗水设备中确定对所述待洗水订单进行洗水的目标洗水设备；
    将与该待洗水订单相应的洗水参数发送至目标洗水设备，以供目标洗水设备根据所述洗水参数对待洗水面料进行洗水操作。
  17. 一种存储有计算机程序的计算机可读存储介质，其特征在于，当所述计算机程序被一个或多个处理器执行时，致使所述一个或多个处理器执行包括以下的动作：
    获取待洗水订单的面料总量；
    从能够为所述面料总量提供洗水操作的至少一台洗水设备中确定对所述待洗水订单进行洗水的目标洗水设备；
    将与该待洗水订单相应的洗水参数发送至目标洗水设备，以供目标洗水设备根据所述洗水参数对待洗水面料进行洗水操作。
PCT/CN2020/122065 2019-10-21 2020-10-20 洗水参数获取方法、设备、系统及存储介质 WO2021078108A1 (zh)

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