An intelligent control method and system for baby carriage production based on data feedback

Abstract

The present invention relates to the technical field of baby carriage production, and discloses a method and system for intelligent control of baby carriage production and preparation based on data feedback, the method comprising: constructing a baby carriage preparation index based on interest point distribution, content preference and scene preference, collecting feedback data of the control target under the preparation index, performing reliability test on the feedback data, and obtaining a verification result; using the feedback data to determine the index value of the preparation index under the verification result, calculating the index weight of the index value, calculating the demand coefficient of the preparation index, and determining the baby carriage demand of the baby carriage user; collecting the preparation parameters of the baby carriage, discretizing the preparation parameters and the baby carriage demand, obtaining the discretized parameters and the discretized demand, analyzing the association rules between the discretized parameters and the discretized demand, and using the association rules to perform intelligent control on the baby carriage in production and preparation, and obtaining the production control result of the baby carriage. The present invention can improve the control efficiency of the quality influencing factors in the production and preparation process of the baby carriage.

Description

Technical Field

[0001] The present invention relates to the technical field of baby carriage production, and in particular to an intelligent control method and system for baby carriage production based on data feedback. Background Art

[0002] The production and preparation control of baby strollers mainly involves controlling the quality of baby strollers, determining the production process and parameter factors that affect the quality of the production process, and controlling these factors to improve the quality of baby strollers.

[0003] At present, when controlling the quality of baby strollers, it is necessary to consider the parameter factors and environmental factors during the production and manufacturing of baby strollers. According to the patents retrieved by the keywords "(production) AND (quality) AND (control)", the quality control method of scheme one is to match the produced products with the template products to detect unqualified products. Since the repair of unqualified products consumes a lot of energy, this method is obviously inefficient. Secondly, the environmental factors that affect the quality of baby strollers in the production process are collected, and the neural network model is used to predict whether the products produced under this environmental factor are qualified. When unqualified products are predicted, the environmental factors are adjusted. However, the numerical adjustment of environmental factors is vague and requires manual experts to judge, which is inefficient. Therefore, the control efficiency of quality influencing factors in the production and preparation process of baby strollers is low. Summary of the invention

[0004] In order to solve the above problems, the present invention provides an intelligent control method and system for the production and preparation of baby carriages based on data feedback, which can improve the control efficiency of quality influencing factors in the production and preparation process of baby carriages.

[0005] In a first aspect, the present invention provides an intelligent control method for producing and preparing a baby carriage based on data feedback, comprising:

[0006] Determine the control target of the baby stroller in production and preparation, and collect relevant data of the baby stroller users on the control target, wherein the relevant data includes usage records, evaluation content and usage scenarios;

[0007] Based on the usage records, analyzing the distribution of the baby stroller user's interest points in the baby stroller, based on the evaluation content, analyzing the baby stroller user's content preference for the baby stroller, and based on the usage scenarios, analyzing the baby stroller user's scenario preference for the baby stroller;

[0008] Based on the distribution of interest points, the content preference and the scene preference, construct the preparation index of the baby carriage, collect feedback data of the control target under the preparation index, perform reliability test on the feedback data, and obtain a verification result;

[0009] Determine the index value of the preparation index under the verification result by using the feedback data, calculate the index weight of the index value, calculate the demand coefficient of the preparation index based on the index weight, and determine the baby stroller demand of the baby stroller user according to the demand coefficient;

[0010] The preparation parameters of the baby carriage are collected, the preparation parameters and the baby carriage requirements are discretized to obtain discretized parameters and discretized requirements, the association rules between the discretized parameters and the discretized requirements are analyzed, and the association rules are used to perform intelligent control on the baby carriage during production and preparation to obtain a production control result of the baby carriage.

[0011] In a possible implementation manner of the first aspect, constructing the preparation index of the baby carriage based on the distribution of interest points, the content preference, and the scene preference includes:

[0012] Obtaining the distribution of points of interest, the content preference, and preferred keywords under the scene preference;

[0013] Based on the association between the preference keywords and the baby carriage, the preference keywords are decomposed to obtain the preparation index.

[0014] In a possible implementation manner of the first aspect, performing a reliability check on the feedback data to obtain a verification result includes:

[0015] Calculating a first correlation coefficient between every two pieces of feedback data in the feedback data;

[0016] Querying the feedback source of the feedback data, and querying the target feedback data of the feedback source at different times;

[0017] Calculating a second correlation coefficient between the feedback data and the target feedback data;

[0018] Analyzing the internal structure validity of the feedback data by using a preset confirmatory factor analysis method;

[0019] The verification result of the feedback data is determined using the first correlation coefficient, the second correlation coefficient and the internal structure validity.

[0020] In a possible implementation manner of the first aspect, calculating the indicator weight of the indicator value includes:

[0021] The numerical weight of the indicator value is calculated using the following formula:

[0022]

[0023] in, Indicates the numerical weight of the i-th indicator value of the j-th preparation indicator, represents the value of the i-th index of the j-th preparation index, represents the number of indicator values ​​of the j-th preparation indicator;

[0024] Based on the numerical weight, the indicator information entropy of the indicator value is calculated using the following formula:

[0025]

[0026] in, represents the index information entropy of the j-th preparation index, Indicates the numerical weight of the i-th indicator value of the j-th preparation indicator, represents the number of indicator values ​​of the j-th preparation indicator;

[0027] According to the indicator information entropy, the indicator weight of the indicator value is calculated using the following formula:

[0028]

[0029] in, represents the index weight of the j-th preparation index, represents the index information entropy of the j-th preparation index, Indicates the number of preparation indicators.

[0030] In a possible implementation manner of the first aspect, calculating the requirement coefficient of the preparation indicator based on the indicator weight includes:

[0031] constructing the positive and negative problems of the preparation index;

[0032] Collecting positive and negative answers corresponding to the positive and negative questions;

[0033] Query the demand frequency of the preparation index from the positive and negative answers;

[0034] Calculating the weight-frequency product between the indicator weight and the demand frequency;

[0035] Based on the weight-frequency product, the satisfaction coefficient of the preparation index is calculated using the following formula:

[0036]

[0037] in, The satisfaction coefficient, represents the weight-frequency product of attractive demand, represents the weight-frequency product of expected demand, represents the weight-frequency product of the essential demand, The weight-frequency product representing the indifferent attribute;

[0038] Based on the weight-frequency product, the dissatisfaction coefficient of the preparation index is calculated using the following formula:

[0039]

[0040] in, The dissatisfaction coefficient, represents the weight-frequency product of attractive demand, represents the weight-frequency product of expected demand, represents the weight-frequency product of the essential demand, The weight-frequency product representing the indifferent attribute;

[0041] The satisfaction coefficient and the dissatisfaction coefficient are used as the requirement coefficients of the preparation index.

[0042] In a possible implementation manner of the first aspect, determining the baby stroller demand of the baby stroller user according to the demand coefficient includes:

[0043] Obtaining a satisfaction coefficient and a dissatisfaction coefficient in the demand coefficient;

[0044] The satisfaction coefficient is used as the horizontal axis, the dissatisfaction coefficient is used as the vertical axis, and the coefficient mean of the satisfaction coefficient and the coefficient mean of the dissatisfaction coefficient are used as boundaries to divide the demand coefficient into four quadrants;

[0045] Determine the rectangular coordinate system of the demand coefficient according to the horizontal axis system, the vertical axis system and the four quadrants;

[0046] The preparation index that does not belong to the indifference attribute in the rectangular coordinate system is used as the baby stroller demand of the baby stroller user.

[0047] In a possible implementation manner of the first aspect, the discretizing the preparation parameters and the baby carriage requirements to obtain discretized parameters and discretized requirements includes:

[0048] Obtaining historical parameters of the preparation parameters and historical demands of the stroller;

[0049] When the preparation parameter is higher than the historical parameter, setting a first increased value of the preparation parameter;

[0050] When the preparation parameter is lower than the historical parameter, setting a first reduced value of the preparation parameter;

[0051] When the preparation parameter is equal to the historical parameter, setting a first constant value of the preparation parameter;

[0052] The discretization processing of the preparation parameter is completed through the first increased value, the first decreased value and the first unchanged value to obtain a discretized parameter;

[0053] When the baby stroller demand is higher than the historical demand, setting a second increased value of the baby stroller demand;

[0054] When the demand for baby carriages is lower than the historical demand, a second reduced value of the demand for baby carriages is set; when the demand for baby carriages is equal to the historical demand, a second unchanged value of the demand for baby carriages is set;

[0055] The discretization processing of the baby carriage demand is completed through the second increased value, the second decreased value and the second unchanged value to obtain a discretized demand.

[0056] In a possible implementation manner of the first aspect, the analyzing an association rule between the discretization parameter and the discretization requirement includes:

[0057] Taking all the discretization parameters in the discretization parameters and the target discretization requirements in the discretization requirements as an option set;

[0058] Calculate the single option support of each option in the option set;

[0059] When the support of the single option is not less than a set minimum support threshold, retain the options in the option set to obtain a single option;

[0060] Calculate the two-option support between each two retained options;

[0061] Identify the infrequent support less than the set minimum support threshold and the frequent support not less than the set minimum support threshold in the dual-option support;

[0062] Calculate the three-option support between the double option corresponding to the double option support and the single option;

[0063] Remove the target three options including the infrequent options corresponding to the infrequent support from the three options corresponding to the three options support, to obtain the remaining three options;

[0064] When the three-option support corresponding to the remaining three options is not less than the set minimum support threshold, the remaining three options are retained to obtain the retained three options;

[0065] Calculate the Bayesian probability between the discretization parameter and the discretization requirement using the single option, the frequent options corresponding to the frequent support, and the retained three options;

[0066] The Bayesian probability is used as an association rule between the discretization parameter and the discretization requirement.

[0067] In a possible implementation manner of the first aspect, the using the association rule to intelligently control the baby carriage during production and preparation to obtain a production control result of the baby carriage includes:

[0068] Determining the demand trend of the baby stroller demand;

[0069] Searching the association rules for a target association rule that the discretized demand meets the demand tendency;

[0070] Identifying parameter tendencies of discretized parameters in the target association rule;

[0071] Based on the parameter tendency, the baby carriage is intelligently controlled during production and preparation to obtain a production control result of the baby carriage.

[0072] In a second aspect, the present invention provides an intelligent control system for the production and preparation of baby carriages based on data feedback, the system comprising:

[0073] A data collection module, used to determine the control target of the baby stroller during production and preparation, and collect relevant data of the baby stroller users on the control target, wherein the relevant data includes usage records, evaluation content and usage scenarios;

[0074] a preference analysis module, configured to analyze the distribution of the baby stroller user's interest points for the baby stroller based on the usage records, analyze the baby stroller user's content preference for the baby stroller based on the evaluation content, and analyze the baby stroller user's scene preference for the baby stroller based on the usage scene;

[0075] a reliability test module, configured to construct a preparation index of the baby carriage based on the distribution of interest points, the content preference and the scene preference, collect feedback data of the control target under the preparation index, perform reliability test on the feedback data, and obtain a verification result;

[0076] a demand determination module, configured to determine the index value of the preparation index under the verification result by using the feedback data, calculate the index weight of the index value, calculate the demand coefficient of the preparation index based on the index weight, and determine the baby stroller demand of the baby stroller user according to the demand coefficient;

[0077] The production control module is used to collect the preparation parameters of the baby carriage, discretize the preparation parameters and the baby carriage requirements to obtain discretized parameters and discretized requirements, analyze the association rules between the discretized parameters and the discretized requirements, and use the association rules to intelligently control the baby carriage during production and preparation to obtain the production control result of the baby carriage.

[0078] Compared with the prior art, the technical principle and beneficial effects of this solution are:

[0079] The embodiment of the present invention discretizes the preparation parameters and the baby stroller demand to characterize the preparation parameters and the baby stroller demand by increasing, decreasing or keeping unchanged, so as to find out the influence rules between the preparation parameters and the baby stroller demand under these numerical changes. Furthermore, the embodiment of the present invention uses the association rules to intelligently control the baby stroller during production and preparation, so as to use formula calculation to determine how the preparation parameters should be adjusted, improve the clarity of the numerical adjustment of baby stroller parameters, machine parameters and environmental factors, reduce the participation of manual experts, and thus improve the efficiency of numerical adjustment. Therefore, the intelligent control method and system for baby stroller production and preparation based on data feedback proposed in the embodiment of the present invention can improve the control efficiency of quality influencing factors in the production and preparation process of baby strollers. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0081] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0082] Figure 1 A schematic diagram of a flow chart of an intelligent control method for producing and preparing a baby carriage based on data feedback provided by an embodiment of the present invention;

[0083] Figure 2 A schematic diagram of a module of an intelligent control system for producing and preparing a baby carriage based on data feedback provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0084] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0085] The embodiment of the present invention provides a method for intelligent control of the production and preparation of baby carriages based on data feedback, and the execution subject of the method for intelligent control of the production and preparation of baby carriages based on data feedback includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present invention. In other words, the method for intelligent control of the production and preparation of baby carriages based on data feedback can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0086] See also Figure 1 FIG. 1 is a flow chart of an intelligent control method for producing and preparing baby carriages based on data feedback provided by an embodiment of the present invention. Figure 1 The intelligent control method for the production and preparation of baby carriages based on data feedback described in the specification includes:

[0087] S1. Determine control targets of the baby stroller during production and preparation, and collect relevant data of baby stroller users on the control targets, wherein the relevant data includes usage records, evaluation contents, and usage scenarios.

[0088] In an embodiment of the present invention, the control target refers to the production quality of the baby stroller. Furthermore, the relevant data refers to the data of the baby stroller being used by the baby stroller user. The usage record refers to the usage of the baby stroller by the baby stroller user recorded by the baby stroller or a related recording instrument. The evaluation content refers to the evaluation of the baby stroller by the baby stroller user. The usage scenario refers to the surrounding environment when the baby stroller user uses the baby stroller.

[0089] S2. Analyzing the distribution of the baby stroller user's interests in the baby stroller based on the usage record, analyzing the baby stroller user's content preferences for the baby stroller based on the evaluation content, and analyzing the baby stroller user's scene preferences for the baby stroller based on the usage scenario.

[0090] In the embodiment of the present invention, the distribution of points of interest refers to the distribution of functions on the baby stroller that are frequently used by the baby stroller user in the usage record.

[0091] In one embodiment of the present invention, analyzing the distribution of interest points of the stroller user in the stroller based on the usage record includes: collecting keyword data in the usage record; and using the keyword data in the usage record as the distribution of interest points.

[0092] Furthermore, in an embodiment of the present invention, the content preference refers to the keywords on the baby stroller mentioned by the baby stroller user when giving a positive evaluation of the baby stroller, and the scene preference refers to the distribution of scenes in which the baby stroller user frequently uses the baby stroller in the usage scenarios, such as parks, indoors, and playgrounds.

[0093] It should be noted that the principle of analyzing the content preference of the stroller user for the stroller based on the evaluation content and the principle of analyzing the scene preference of the stroller user for the stroller based on the usage scenario are similar to the principle of analyzing the distribution of interest points of the stroller user for the stroller based on the usage record, and will not be further elaborated here.

[0094] S3. Based on the distribution of interest points, the content preference and the scene preference, construct the preparation index of the baby carriage, collect feedback data of the control target under the preparation index, perform reliability test on the feedback data, and obtain verification results.

[0095] In one embodiment of the present invention, constructing the preparation index of the baby carriage based on the interest point distribution, the content preference and the scene preference includes: obtaining preference keywords under the interest point distribution, the content preference and the scene preference; and performing keyword decomposition on the preference keywords based on the association between the preference keywords and the baby carriage to obtain the preparation index.

[0096] Among them, the preference keywords refer to the keyword data in the interest point distribution, the content preference and the scene preference.

[0097] It should be noted that, the process of decomposing the preference keywords based on the association between the preference keywords and the baby stroller to obtain the preparation index means that when the preference keywords have multiple correlations with the function, area, structure, color, etc. of the baby stroller, the preference keywords are represented by multiple related baby stroller data. For example, the "cost" keyword of a baby stroller is related to multiple parts of the baby stroller, such as the cost of the tires, the cost of the turntable, and the cost of the seat.

[0098] Furthermore, in the embodiment of the present invention, the feedback data refers to feedback made by the baby stroller user on the use of the baby stroller.

[0099] In one embodiment of the present invention, the reliability test of the feedback data to obtain the verification result includes: calculating a first correlation coefficient between every two feedback data in the feedback data; querying the feedback source of the feedback data, and querying the target feedback data of the feedback source at different times; calculating a second correlation coefficient between the feedback data and the target feedback data; analyzing the internal structural validity of the feedback data by a preset confirmatory factor analysis method; and determining the verification result of the feedback data by using the first correlation coefficient, the second correlation coefficient and the internal structural validity.

[0100] Among them, the first correlation coefficient and the second correlation coefficient can be Pearson correlation coefficients, etc., the feedback source refers to the stroller user, the target feedback data refers to another feedback data made by the same stroller user on the premise of making the feedback data, and the confirmatory factor analysis method refers to a research method used to measure whether the correspondence between factors and measurement items (scale items) is consistent with the researcher's prediction.

[0101] Optionally, the process of determining the verification result of the feedback data using the first correlation coefficient, the second correlation coefficient and the internal structure validity means that when the first correlation coefficient and the second correlation coefficient are both greater than a preset correlation coefficient threshold and a preset validity threshold of the internal structure validity, it indicates that the reliability test is successful, otherwise the reliability test fails. It should be noted that the verification result includes a successful reliability test and a failed reliability test.

[0102] S4. Determine the index value of the preparation index under the verification result by using the feedback data, calculate the index weight of the index value, calculate the demand coefficient of the preparation index based on the index weight, and determine the baby stroller demand of the baby stroller user according to the demand coefficient.

[0103] It should be noted that, in the process of constructing the preparation index of the baby carriage based on the distribution of interest points, the content preference and the scene preference before using the feedback data to determine the index value of the preparation index under the verification result, the obtained preparation index is an indicator without a specific value. The value of each preparation indicator can be determined from the baby carriage user through the feedback data, and the credibility of the value of each preparation indicator can be determined through the verification result, and the value of the preparation indicator with a high credibility is used as the indicator value.

[0104] In one embodiment of the present invention, the calculating the indicator weight of the indicator value includes: calculating the numerical weight of the indicator value using the following formula:

[0105]

[0106] in, Indicates the numerical weight of the i-th indicator value of the j-th preparation indicator, represents the value of the i-th index of the j-th preparation index, represents the number of indicator values ​​of the j-th preparation indicator;

[0107] Based on the numerical weight, the indicator information entropy of the indicator value is calculated using the following formula:

[0108]

[0109] in, represents the index information entropy of the j-th preparation index, Indicates the numerical weight of the i-th indicator value of the j-th preparation indicator, represents the number of indicator values ​​of the j-th preparation indicator;

[0110] According to the indicator information entropy, the indicator weight of the indicator value is calculated using the following formula:

[0111]

[0112] in, represents the index weight of the j-th preparation index, represents the index information entropy of the j-th preparation index, Indicates the number of preparation indicators.

[0113] In one embodiment of the present invention, the calculation of the demand coefficient of the preparation indicator based on the indicator weight includes: constructing positive and negative questions of the preparation indicator; collecting positive and negative answers corresponding to the positive and negative questions; querying the demand frequency of the preparation indicator from the positive and negative answers; calculating the weight-frequency product between the indicator weight and the demand frequency; and calculating the satisfaction coefficient of the preparation indicator based on the weight-frequency product using the following formula:

[0114]

[0115] in, The satisfaction coefficient is represents the weight-frequency product of attractive demand, represents the weight-frequency product of expected demand, represents the weight-frequency product of the essential demand, The weight-frequency product representing the indifferent attribute;

[0116] Based on the weight-frequency product, the dissatisfaction coefficient of the preparation index is calculated using the following formula:

[0117]

[0118] in, The dissatisfaction coefficient, represents the weight-frequency product of attractive demand, represents the weight-frequency product of expected demand, represents the weight-frequency product of the essential demand, The weight-frequency product representing the indifferent attribute;

[0119] The satisfaction coefficient and the dissatisfaction coefficient are used as the requirement coefficients of the preparation index.

[0120] Among them, the positive and negative questions refer to the questionnaire survey of the stroller users set in advance before building the KANO model, and the demand frequency refers to the frequency of occurrence of each demand answered by the stroller users. It should be noted that these demands are attractive demands, expected demands, essential demands, and indifferent attributes.

[0121] In one embodiment of the present invention, determining the stroller demand of the stroller user based on the demand coefficient includes: obtaining a satisfaction coefficient and a dissatisfaction coefficient in the demand coefficient; taking the satisfaction coefficient as the horizontal axis, taking the dissatisfaction coefficient as the vertical axis, and dividing the demand coefficient into four quadrants using the coefficient mean of the satisfaction coefficient and the coefficient mean of the dissatisfaction coefficient as boundaries; determining the rectangular coordinate system of the demand coefficient based on the horizontal axis, the vertical axis and the four quadrants; and taking the preparation indicators in the rectangular coordinate system that do not belong to the indifference attribute as the stroller demand of the stroller user.

[0122] It should be noted that the preparation indicators falling in the first quadrant belong to attractive needs, the preparation indicators falling in the second quadrant belong to indifferent attributes, the preparation indicators falling in the third quadrant belong to essential needs, and the preparation indicators falling in the fourth quadrant belong to expected needs.

[0123] S5. Collect preparation parameters of the baby carriage, discretize the preparation parameters and the baby carriage requirements to obtain discretized parameters and discretized requirements, analyze association rules between the discretized parameters and the discretized requirements, and use the association rules to intelligently control the baby carriage during production and preparation to obtain a production control result of the baby carriage.

[0124] In the embodiment of the present invention, the preparation parameters refer to various parameters of the baby stroller, parameters of the production machine, and environmental parameters that are adjusted when the baby stroller is produced.

[0125] Furthermore, the embodiment of the present invention discretizes the preparation parameters and the baby stroller demand to characterize the preparation parameters and the baby stroller demand by increasing, decreasing or keeping unchanged, so as to find out the influence rules between the preparation parameters and the baby stroller demand under these numerical changes.

[0126] In one embodiment of the present invention, the preparation parameters and the stroller demand are discretized to obtain the discretized parameters and the discretized demand, including: obtaining historical parameters of the preparation parameters and historical demands of the stroller demand; when the preparation parameters are higher than the historical parameters, setting a first increased value of the preparation parameters; when the preparation parameters are lower than the historical parameters, setting a first decreased value of the preparation parameters; when the preparation parameters are equal to the historical parameters, setting a first constant value of the preparation parameters; completing the discretization of the preparation parameters through the first increased value, the first decreased value and the first constant value to obtain the discretized parameters; when the stroller demand is higher than the historical demand, setting a second increased value of the stroller demand; when the stroller demand is lower than the historical demand, setting a second decreased value of the stroller demand; when the stroller demand is equal to the historical demand, setting a second constant value of the stroller demand; completing the discretization of the stroller demand through the second increased value, the second decreased value and the second constant value to obtain the discretized demand.

[0127] The first increasing value and the second increasing value are both represented by increasing symbols, such as H, the first decreasing value and the second decreasing value are both represented by decreasing symbols, and the first unchanged value and the second unchanged value are both represented by unchanged symbols.

[0128] Optionally, the discretization processing of the preparation parameters is completed through the first increased value, the first decreased value and the first constant value, and the process of obtaining the discretized parameters refers to characterizing the preparation parameters through the first increased value, the first decreased value and the first constant value. Further, the discretization processing of the baby carriage demand is completed through the second increased value, the second decreased value and the second constant value, and the process of obtaining the discretized demand refers to characterizing the baby carriage demand through the second increased value, the second decreased value and the second constant value.

[0129] In one embodiment of the present invention, the analysis of the association rules between the discretization parameters and the discretization requirements includes: taking all the discretization parameters in the discretization parameters and the target discretization requirements in the discretization requirements as an option set; calculating the single option support of each option in the option set; when the single option support is not less than a set minimum support threshold, retaining the options in the option set to obtain single options; calculating the dual option support between every two retained options; identifying the infrequent support less than the set minimum support threshold and the frequent support not less than the set minimum support threshold in the dual option support; calculating the the three-option support between the double option corresponding to the double-option support and the single option; removing the target three options including the non-frequent options corresponding to the non-frequent support from the three options corresponding to the three-option support to obtain the remaining three options; retaining the remaining three options to obtain the retained three options when the three-option support corresponding to the remaining three options is not less than the set minimum support threshold; calculating the Bayesian probability between the discretization parameter and the discretization requirement using the single option, the frequent options corresponding to the frequent support and the retained three options; and using the Bayesian probability as the association rule between the discretization parameter and the discretization requirement.

[0130] Optionally, the process of taking all the discretization parameters in the discretization parameters and the target discretization requirements in the discretization requirements as an option set refers to merging all the discretization parameters and a randomly selected discretization requirement into a set, and each discretization parameter or the selected discretization requirement is an option in this set, aiming to find the correlation between other discretization parameters in this set and this discretization requirement.

[0131] Optionally, the process of calculating the Bayesian probability between the discretization parameter and the discretization requirement using the single option, the frequent options corresponding to the frequent support, and the three retained options refers to: the single option, the frequent options corresponding to the frequent support, and the three retained options finally screened are three sets, and the Bayesian probability in each set is calculated respectively, that is, the Bayesian probability between several options appearing in the same set, for example is the Bayesian probability that A and B appear at the same time.

[0132] Furthermore, the embodiment of the present invention uses the association rules to perform intelligent control on the baby stroller during production and preparation, so as to use formula calculation to determine how the preparation parameters should be adjusted, thereby improving the clarity of the numerical adjustment of the baby stroller parameters, machine parameters and environmental factors, reducing the participation of manual experts, and thus improving the efficiency of numerical adjustment.

[0133] In one embodiment of the present invention, the use of the association rules to intelligently control the baby stroller during production and preparation to obtain the production control result of the baby stroller includes: determining the demand trend of the baby stroller demand; querying the target association rule in the association rules for the discretized demand to meet the demand trend; identifying the parameter trend of the discretized parameter in the target association rule; based on the parameter trend, intelligently controlling the baby stroller during production and preparation to obtain the production control result of the baby stroller.

[0134] The demand tendency refers to an increasing tendency, a constant tendency, and a decreasing tendency of the baby stroller users' demand for the baby stroller, that is, a tendency of the baby stroller users' expectation of an increasing, constant, or decreasing demand for the baby stroller.

[0135] Optionally, the process of searching the association rules for the target association rules that the discretized demand meets the demand tendency means that, since the Bayesian probability is calculated using symbols such as increasing values, decreasing values ​​and unchanged values, it is only necessary to find out whether the symbols such as increasing values, decreasing values ​​and unchanged values ​​in the Bayesian probability meet the demand tendency, for example middle In order to meet the discretization requirements of the demand trend, The parameter tendency is a parameter tendency of a discretized parameter, and the parameter tendency includes an increasing tendency, a constant tendency and a decreasing tendency, that is, the preparation parameters of the baby carriage are increased, constant or decreased.

[0136] It can be seen that the embodiment of the present invention discretizes the preparation parameters and the baby stroller demand to characterize the preparation parameters and the baby stroller demand by increasing, decreasing or keeping unchanged, so as to find out the influence rules between the preparation parameters and the baby stroller demand under these numerical changes. Furthermore, the embodiment of the present invention uses the association rules to intelligently control the baby stroller during production and preparation, so as to use formula calculation to determine how the preparation parameters should be adjusted, improve the clarity of the numerical adjustment of baby stroller parameters, machine parameters and environmental factors, reduce the participation of manual experts, and thus improve the efficiency of numerical adjustment. Therefore, the intelligent control method and system for baby stroller production and preparation based on data feedback proposed in the embodiment of the present invention can improve the control efficiency of quality influencing factors in the production and preparation process of baby strollers.

[0137] like Figure 2 The figure shows a functional module diagram of the intelligent control system for producing and preparing baby carriages based on data feedback according to the present invention.

[0138] The baby carriage production and preparation intelligent control system 200 based on data feedback of the present invention can be installed in an electronic device. According to the functions implemented, the baby carriage production and preparation intelligent control system based on data feedback can include a data acquisition module 201, a preference analysis module 202, a reliability test module 203, a demand determination module 204 and a production control module 205. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0139] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0140] The data collection module 201 is used to determine the control target of the baby stroller during production and preparation, and collect relevant data of the baby stroller user on the control target, wherein the relevant data includes usage records, evaluation content and usage scenarios;

[0141] The preference analysis module 202 is used to analyze the distribution of the baby stroller user's interest points for the baby stroller based on the usage records, analyze the baby stroller user's content preference for the baby stroller based on the evaluation content, and analyze the baby stroller user's scene preference for the baby stroller based on the usage scenario;

[0142] The reliability verification module 203 is used to construct the preparation index of the baby carriage based on the distribution of the points of interest, the content preference and the scene preference, collect the feedback data of the control target under the preparation index, and perform reliability verification on the feedback data to obtain a verification result;

[0143] The demand determination module 204 is used to determine the index value of the preparation index under the verification result by using the feedback data, calculate the index weight of the index value, calculate the demand coefficient of the preparation index based on the index weight, and determine the baby stroller demand of the baby stroller user according to the demand coefficient;

[0144] The production control module 205 is used to collect the preparation parameters of the baby carriage, discretize the preparation parameters and the baby carriage requirements to obtain discretized parameters and discretized requirements, analyze the association rules between the discretized parameters and the discretized requirements, and use the association rules to intelligently control the baby carriage during production and preparation to obtain the production control result of the baby carriage.

[0145] In detail, each module in the baby carriage production and preparation intelligent control system 200 based on data feedback in the embodiment of the present invention is used in the same manner as described above. Figure 1The technical means are the same as the intelligent control method for the production of baby carriages based on data feedback described in, and can produce the same technical effects, so they will not be repeated here.

[0146] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

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

[0148] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0149] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0150] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent control method for the production of baby carriages based on data feedback, characterized in that: The method comprises: Determine the control target of the baby stroller in production and preparation, and collect relevant data of the baby stroller users on the control target, wherein the relevant data includes usage records, evaluation content and usage scenarios; Based on the usage records, analyzing the distribution of the baby stroller user's interest points in the baby stroller, based on the evaluation content, analyzing the baby stroller user's content preference for the baby stroller, and based on the usage scenarios, analyzing the baby stroller user's scenario preference for the baby stroller; Based on the distribution of interest points, the content preference and the scene preference, construct the preparation index of the baby carriage, collect feedback data of the control target under the preparation index, perform reliability test on the feedback data, and obtain a verification result; Determine the index value of the preparation index under the verification result by using the feedback data, calculate the index weight of the index value, calculate the demand coefficient of the preparation index based on the index weight, and determine the baby stroller demand of the baby stroller user according to the demand coefficient; The preparation parameters of the baby carriage are collected, the preparation parameters and the baby carriage requirements are discretized to obtain discretized parameters and discretized requirements, the association rules between the discretized parameters and the discretized requirements are analyzed, and the association rules are used to intelligently control the baby carriage during production and preparation to obtain the production control result of the baby carriage, wherein the discretization of the preparation parameters and the baby carriage requirements to obtain discretized parameters and discretized requirements includes: Obtaining historical parameters of the preparation parameters and historical demands of the stroller; When the preparation parameter is higher than the historical parameter, setting a first increased value of the preparation parameter; When the preparation parameter is lower than the historical parameter, setting a first reduced value of the preparation parameter; When the preparation parameter is equal to the historical parameter, setting a first constant value of the preparation parameter; The discretization processing of the preparation parameter is completed through the first increased value, the first decreased value and the first unchanged value to obtain a discretized parameter; When the baby stroller demand is higher than the historical demand, setting a second increased value of the baby stroller demand; When the demand for baby carriages is lower than the historical demand, a second reduced value of the demand for baby carriages is set; when the demand for baby carriages is equal to the historical demand, a second unchanged value of the demand for baby carriages is set; The discretization process of the baby carriage demand is completed through the second increased value, the second decreased value and the second unchanged value to obtain a discretized demand; The analyzing the association rule between the discretization parameter and the discretization requirement includes: Taking all the discretization parameters in the discretization parameters and the target discretization requirements in the discretization requirements as an option set; Calculate the single option support of each option in the option set; When the support of the single option is not less than a set minimum support threshold, retain the options in the option set to obtain a single option; Calculate the two-option support between each two retained options; Identify the infrequent support less than the set minimum support threshold and the frequent support not less than the set minimum support threshold in the dual-option support; Calculate the three-option support between the double option corresponding to the double option support and the single option; Remove the target three options including the infrequent options corresponding to the infrequent support from the three options corresponding to the three options support, to obtain the remaining three options; When the three-option support corresponding to the remaining three options is not less than the set minimum support threshold, the remaining three options are retained to obtain the retained three options; Calculate the Bayesian probability between the discretization parameter and the discretization requirement using the single option, the frequent options corresponding to the frequent support, and the retained three options; The Bayesian probability is used as an association rule between the discretization parameter and the discretization requirement.

2. The method according to claim 1, characterized in that The constructing the preparation index of the baby carriage based on the distribution of interest points, the content preference and the scene preference includes: Obtaining the distribution of points of interest, the content preference, and preferred keywords under the scene preference; Based on the association between the preference keywords and the baby carriage, the preference keywords are decomposed to obtain the preparation index.

3. The method according to claim 1, characterized in that The performing of reliability check on the feedback data to obtain a check result includes: Calculating a first correlation coefficient between every two pieces of feedback data in the feedback data; Querying the feedback source of the feedback data, and querying the target feedback data of the feedback source at different times; Calculating a second correlation coefficient between the feedback data and the target feedback data; Analyzing the internal structure validity of the feedback data by using a preset confirmatory factor analysis method; The verification result of the feedback data is determined using the first correlation coefficient, the second correlation coefficient and the internal structure validity.

4. The method according to claim 1, characterized in that The calculation of the indicator weight of the indicator value includes: The numerical weight of the indicator value is calculated using the following formula: in, Indicates the numerical weight of the i-th indicator value of the j-th preparation indicator, represents the value of the i-th index of the j-th preparation index, represents the number of indicator values ​​of the j-th preparation indicator; Based on the numerical weight, the indicator information entropy of the indicator value is calculated using the following formula: in, represents the index information entropy of the j-th preparation index, Indicates the numerical weight of the i-th indicator value of the j-th preparation indicator, represents the number of indicator values ​​of the j-th preparation indicator; According to the indicator information entropy, the indicator weight of the indicator value is calculated using the following formula: in, represents the index weight of the j-th preparation index, represents the index information entropy of the j-th preparation index, Indicates the number of preparation indicators.

5. The method according to claim 1, characterized in that The step of calculating the demand coefficient of the preparation index based on the index weight includes: constructing the positive and negative problems of the preparation index; Collecting positive and negative answers corresponding to the positive and negative questions; Query the demand frequency of the preparation index from the positive and negative answers; Calculating the weight-frequency product between the indicator weight and the demand frequency; Based on the weight-frequency product, the satisfaction coefficient of the preparation index is calculated using the following formula: in, The satisfaction coefficient, represents the weight-frequency product of attractive demand, represents the weight-frequency product of expected demand, represents the weight-frequency product of the essential demand, The weight-frequency product representing the indifferent attribute; Based on the weight-frequency product, the dissatisfaction coefficient of the preparation index is calculated using the following formula: in, The dissatisfaction coefficient, represents the weight-frequency product of attractive demand, represents the weight-frequency product of expected demand, represents the weight-frequency product of the essential demand, The weight-frequency product representing the indifferent attribute; The satisfaction coefficient and the dissatisfaction coefficient are used as the requirement coefficients of the preparation index.

6. The method according to claim 1, characterized in that The determining the baby stroller demand of the baby stroller user according to the demand coefficient includes: Obtaining a satisfaction coefficient and a dissatisfaction coefficient in the demand coefficient; The satisfaction coefficient is used as the horizontal axis, the dissatisfaction coefficient is used as the vertical axis, and the coefficient mean of the satisfaction coefficient and the coefficient mean of the dissatisfaction coefficient are used as boundaries to divide the demand coefficient into four quadrants; Determine the rectangular coordinate system of the demand coefficient according to the horizontal axis system, the vertical axis system and the four quadrants; The preparation index that does not belong to the indifference attribute in the rectangular coordinate system is used as the baby stroller demand of the baby stroller user.

7. The method according to claim 1, characterized in that The intelligently controlling the baby carriage during production and preparation by using the association rules to obtain the production control result of the baby carriage includes: Determining the demand trend of the baby stroller demand; Searching the association rules for a target association rule that the discretized demand meets the demand tendency; Identifying parameter tendencies of discretized parameters in the target association rule; Based on the parameter tendency, the baby carriage is intelligently controlled during production and preparation to obtain a production control result of the baby carriage.

8. An intelligent control system for the production of baby carriages based on data feedback, characterized in that: The system comprises: A data collection module, used to determine the control target of the baby stroller during production and preparation, and collect relevant data of the baby stroller users on the control target, wherein the relevant data includes usage records, evaluation content and usage scenarios; a preference analysis module, configured to analyze the distribution of the baby stroller user's interest points for the baby stroller based on the usage records, analyze the baby stroller user's content preference for the baby stroller based on the evaluation content, and analyze the baby stroller user's scene preference for the baby stroller based on the usage scene; a reliability test module, configured to construct a preparation index of the baby carriage based on the distribution of interest points, the content preference and the scene preference, collect feedback data of the control target under the preparation index, perform reliability test on the feedback data, and obtain a verification result; a demand determination module, configured to determine the index value of the preparation index under the verification result by using the feedback data, calculate the index weight of the index value, calculate the demand coefficient of the preparation index based on the index weight, and determine the baby stroller demand of the baby stroller user according to the demand coefficient; The production control module is used to collect the preparation parameters of the baby carriage, discretize the preparation parameters and the baby carriage requirements to obtain discretized parameters and discretized requirements, analyze the association rules between the discretized parameters and the discretized requirements, and use the association rules to intelligently control the baby carriage during production and preparation to obtain the production control result of the baby carriage, wherein the discretization of the preparation parameters and the baby carriage requirements to obtain the discretized parameters and the discretized requirements includes: Obtaining historical parameters of the preparation parameters and historical demands of the stroller; When the preparation parameter is higher than the historical parameter, setting a first increased value of the preparation parameter; When the preparation parameter is lower than the historical parameter, setting a first reduced value of the preparation parameter; When the preparation parameter is equal to the historical parameter, setting a first constant value of the preparation parameter; The discretization processing of the preparation parameter is completed through the first increased value, the first decreased value and the first unchanged value to obtain a discretized parameter; When the baby stroller demand is higher than the historical demand, setting a second increased value of the baby stroller demand; When the demand for baby carriages is lower than the historical demand, a second reduced value of the demand for baby carriages is set; when the demand for baby carriages is equal to the historical demand, a second unchanged value of the demand for baby carriages is set; The discretization process of the baby carriage demand is completed through the second increased value, the second decreased value and the second unchanged value to obtain a discretized demand; The analyzing the association rule between the discretization parameter and the discretization requirement includes: Taking all the discretization parameters in the discretization parameters and the target discretization requirements in the discretization requirements as an option set; Calculate the single option support of each option in the option set; When the support of the single option is not less than a set minimum support threshold, retain the options in the option set to obtain a single option; Calculate the two-option support between each two retained options; Identify the infrequent support less than the set minimum support threshold and the frequent support not less than the set minimum support threshold in the dual-option support; Calculate the three-option support between the double option corresponding to the double option support and the single option; Remove the target three options including the infrequent options corresponding to the infrequent support from the three options corresponding to the three options support, to obtain the remaining three options; When the three-option support corresponding to the remaining three options is not less than the set minimum support threshold, the remaining three options are retained to obtain the retained three options; Calculate the Bayesian probability between the discretization parameter and the discretization requirement using the single option, the frequent options corresponding to the frequent support, and the retained three options; The Bayesian probability is used as an association rule between the discretization parameter and the discretization requirement.

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