Scenario generation method, device, and equipment, and storage medium

Abstract

The application provides a scheme generation method and device, equipment and a storage medium. First, original data is acquired, wherein the original data includes user data of at least two different target objects. Then, target intersection data is determined according to a preset privacy set intersection algorithm and the original data, the determined target intersection data including user data of each target user, and the target user being a common user of the at least two different target objects. Then, a target scheme is generated according to the target intersection data and a preset strategy, so as to formulate a precise push mode for pushing respective to-be-pushed objects for the at least two different target objects. Moreover, the privacy of the user data of the at least two different target objects is ensured, and the security of the user data is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a scheme generation method and device, equipment and a storage medium. BACKGROUND

[0002] With the continuous development of big data technology, how to formulate a suitable and optimal push scheme for a to-be-pushed object based on user data has become the focus of attention of each pusher. The existing push scheme is usually based on the user data owned by the pusher itself, which makes the formulated push scheme not optimal and insufficient to improve the push rate of the to-be-pushed object. Therefore, there is an urgent need for a scheme formulation method to overcome the problems existing in the prior art. SUMMARY

[0003] The present application provides a scheme generation method, device, equipment and storage medium for providing a precise and user data non-leakage push scheme for a to-be-pushed object to overcome the problems existing in the prior art.

[0004] In a first aspect, the present application provides a scheme generation method, comprising:

[0005] obtaining original data, wherein the original data comprises user data of at least two different target objects;

[0006] determining target intersection data according to a preset privacy set intersection algorithm and the original data, wherein the target intersection data comprises the user data of each target user, and the target user refers to a common user of the at least two different target objects;

[0007] generating a target scheme according to the target intersection data and a preset formulation strategy.

[0008] In a possible design, the determining of the target intersection data according to the preset privacy set intersection algorithm and the original data comprises:

[0009] determining each target user according to the original data and the preset privacy set intersection algorithm, wherein each target user carries a user identifier of a preset attribute;

[0010] determining the user data of each target user as the target intersection data according to the user identifier.

[0011] In a possible design, the generating of the target scheme according to the target intersection data and the preset formulation strategy comprises:

[0012] determining a target feature value of each target user according to the target intersection data, a preset evaluation algorithm and a preset weight value;

[0013] generate the target scheme according to the preset judgment strategy and the target characteristic value of each target user;

[0014] The preset judgment strategy includes the preset evaluation algorithm, the preset weight value, and the preset judgment strategy.

[0015] In a possible design, the generating the target scheme according to the preset judgment strategy and the target characteristic value of each target user includes:

[0016] determining whether the target characteristic value of each target user is unique;

[0017] If yes, a first target scheme is generated, the first target scheme refers to that the at least two different target objects push the respective to-be-pushed objects according to a preset bundling mode;

[0018] If no, a second target scheme is generated, the second target scheme refers to that the at least two different target objects push the respective to-be-pushed objects according to a respective preset independent mode;

[0019] The target scheme includes the first target scheme and the second target scheme.

[0020] In a possible design, the to-be-pushed objects of the at least two different target objects have functional correlation.

[0021] In a possible design, the user data of the first target object at least includes: single laundry weight, washing frequency in a preset period, single rinsing times, total use time, and preset washing program proportion; and / or

[0022] The user data of the second target object at least includes: push proportion of different material clothes, total amount of clothes push, and preset characteristic amount of clothes push; and / or

[0023] The user data of the third target object at least includes: push proportion of different types of detergents, total amount of detergent push, and preset characteristic amount of detergent push.

[0024] The at least two different target objects include at least two of the first target object, the second target object, and the third target object.

[0025] In a second aspect, the present application provides a scheme generation device, including:

[0026] An acquisition module is configured to acquire original data, the original data including user data of at least two different target objects;

[0027] The processing module is configured to determine target intersection data according to the preset intersection algorithm and the original data, the target intersection data comprising user data of each target user, the target user being a common user of the at least two different target objects;

[0028] The generating module is configured to generate a target scheme according to the target intersection data and a preset formulation strategy, the target scheme comprising a pushing mode of each of the at least two different target objects pushing a respective to-be-pushed object.

[0029] In a possible design, the processing module is specifically configured to:

[0030] determine the each target user according to the original data and the preset intersection algorithm, each target user carrying a user identifier of a preset attribute;

[0031] determine the user data of the each target user as the target intersection data according to the user identifier.

[0032] In a possible design, the generating module is specifically configured to:

[0033] determine a target feature value of the each target user according to the target intersection data, the preset evaluation algorithm and a preset weight value;

[0034] generate the target scheme according to a preset judgment strategy and the target feature value of the each target user;

[0035] The preset formulation strategy comprises the preset evaluation algorithm, the preset weight value and the preset judgment strategy.

[0036] In a possible design, the generating module is further specifically configured to:

[0037] determine whether the target feature value of the each target user is unique;

[0038] if yes, generate a first target scheme, the first target scheme being that the at least two different target objects push the respective to-be-pushed objects according to a preset bundling mode;

[0039] if no, generate a second target scheme, the second target scheme being that the at least two different target objects push the respective to-be-pushed objects according to a respective preset independent mode;

[0040] The target scheme comprises the first target scheme and the second target scheme.

[0041] In a possible design, the to-be-pushed objects of the at least two different target objects have functional correlation.

[0042] In a possible design, the user data of the first target object at least includes: single laundry weight, washing frequency in a preset period, single rinsing times, total usage time, and preset washing program proportion; and / or

[0043] The user data of the second target object at least includes: push proportion of different material clothes, total amount of clothes push, and preset characteristic amount of clothes push; and / or

[0044] The user data of the third target object at least includes: push proportion of different types of detergents, total amount of detergent push, and preset characteristic amount of detergent push.

[0045] The at least two different target objects include at least two of the first target object, the second target object, and the third target object.

[0046] In a third aspect, the present application provides an electronic device, comprising:

[0047] a processor; and

[0048] a memory configured to store a computer program of the processor;

[0049] The processor is configured to execute the computer program to implement the scheme generation method provided in the first aspect.

[0050] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the scheme generation method provided in the first aspect.

[0051] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and the computer program is executed by a processor to implement the scheme generation method provided in the first aspect.

[0052] The present application provides a scheme generation method, device, equipment and storage medium. First, the original data is acquired, wherein the original data includes user data of at least two different target objects. Then, the target intersection data is determined according to the preset privacy set intersection algorithm and the original data, and the determined target intersection data includes user data of each target user, and the target user refers to a common user of the at least two different target objects. Then, the target scheme is generated according to the target intersection data and the preset formulation strategy, so as to formulate the precise push mode of pushing the respective to-be-pushed object for the at least two different target objects. Moreover, the privacy of the user data of the at least two different target objects is ensured, and the security of the user data is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 An application scenario schematic diagram provided for an embodiment of the present application;

[0054] Figure 2 A flowchart of a scheme generation method provided for an embodiment of the present application;

[0055] Figure 3 A flowchart of another scheme generation method provided for an embodiment of the present application;

[0056] Figure 4 A flowchart of still another scheme generation method provided for an embodiment of the present application;

[0057] Figure 5 A flowchart of yet another scheme generation method provided for an embodiment of the present application;

[0058] Figure 6 A structural schematic diagram of a scheme generation apparatus provided for an embodiment of the present application;

[0059] Figure 7 A structural schematic diagram of an electronic device provided for the present application. DETAILED DESCRIPTION

[0060] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The following description is made with reference to the accompanying drawings in which like reference numerals refer to like elements, and the term "exemplary" is used herein to mean "serving as an example, instance, or illustration." Thus, the exemplary embodiments are not intended to be limited to the disclosed embodiments, but are intended to be as broad as possible under the aches of the prior art. Unless otherwise specified, the description of an embodiment herein is meant to be applicable to any like embodiment of the present application.

[0061] The terms "first", "second", "third", "fourth" and the like, if any, in the description and in the claims of the present application and in the above description of the drawings merely denote different categories and do not necessarily indicate a sequence or order unless otherwise specified. It is understood that the order or sequence of process steps, acts, or elements of the embodiments herein can be modified, as appropriate, so that certain steps, acts, or elements can be performed either before, after, or in between other steps, acts, or elements. Further, it will be understood that the terms "comprises", "comprising", "includes", "including", "has", "having", "contains", "containing", or any other similar term are intended to be inclusive (i.e., to mean either "including" or "consisting of") and are accordingly to be construed as not excluding additional steps, acts, or elements.

[0062] With the continuous development of big data technology, how to formulate appropriate and optimal push notification plans based on user data has become a key focus for push notification providers. Existing push notification plans are typically based on the user data owned by the push notification provider itself, with each party formulating its plan solely based on its own data. This results in personalized push notification plans for each individual, which are not necessarily optimal and insufficient to improve the delivery rate to the target audience. However, sharing user data to optimize push notification plans can compromise user privacy and potentially threaten data security. Therefore, how to formulate accurate push notification plans based on user data without compromising user privacy has become a pressing issue in the field of push notification plan development.

[0063] To address the aforementioned problems in the prior art, this application provides a scheme generation method, apparatus, device, and storage medium. The inventive concept of the scheme generation method provided in this application lies in: applying a preset privacy set intersection algorithm to the user data of at least two target objects to determine the user data of users shared by the at least two different target objects; then, generating a target scheme based on the user data of the shared users and a preset strategy, thereby providing at least two different target objects with accurate push schemes for pushing their respective target objects, while ensuring the privacy of the user data of the at least two different target objects and protecting user data security.

[0064] The following describes exemplary application scenarios of the embodiments of this application.

[0065] Figure 1 This is a schematic diagram of an application scenario provided in an embodiment of this application, such as... Figure 1 As shown, the processor in electronic device 11 is configured to execute the scheme generation method provided in the embodiments of this application, which, based on user data of at least two different target objects, formulates a push method for pushing their respective target objects to at least two different target objects. The at least two different target objects may, for example... Figure 1 The washing machine manufacturer 21, clothing manufacturer 22, and detergent manufacturer 23 shown in the diagram have the following characteristics: the target audience for the washing machine manufacturer 21 can be, for example, washing machines and dryers awaiting sale; the target audience for the clothing manufacturer 22 can be various garments it produces awaiting sale; and the target audience for the detergent manufacturer 23 can be, for example, various laundry products it produces awaiting sale. Pushing the target audience to each manufacturer can be understood as selling the products awaiting sale. By executing the solution generation method provided in this application embodiment, the electronic device 11 can formulate a precise push method for the washing machine manufacturer 21, clothing manufacturer 22, and detergent manufacturer 23 to push their respective target audiences, while also ensuring the privacy and security of the user data of each manufacturer.

[0066] It should be noted that the electronic device 11 in the above description can be a computer, a server, a smart phone, or the like terminal device, and the specific type of the electronic device 11 is not limited in the embodiments of the present application. Figure 1 The electronic device 11 in the above description is exemplified by a computer. At least two different target objects are exemplified, and it is not limited to this.

[0067] The above application scenarios are only illustrative, and the scheme generation method, device, equipment and storage medium provided by the embodiments of the present application include but are not limited to the above application scenarios.

[0068] Figure 2 A flowchart of a scheme generation method provided by the embodiments of the present application is shown. As shown in Figure 2 The scheme generation method provided by the embodiments of the present application comprises:

[0069] S101: Obtain original data.

[0070] The original data includes user data of at least two different target objects.

[0071] The user data of each party needs to formulate a target scheme, and each party is at least two different target objects.

[0072] Optionally, the at least two different target objects can include at least two of a first target object, a second target object and a third target object. The objects specifically referred to by the first target object, the second target object and the third target object are determined by the actual working condition. For example, the first target object can be a washing machine manufacturer, the second target object can be a clothing manufacturer, and the third target object can be a detergent manufacturer, etc. The specific type of the at least two different target objects is not limited in the embodiments of the present application.

[0073] The original data includes user data of at least two different target objects, and the user data of the first target object in the at least two different target objects can at least include: single washing weight, washing frequency in a preset period, single rinsing times, total use time and preset washing program proportion. And / or, the user data of the second target object in the at least two different target objects can at least include: push proportion of different material clothes, total amount of clothes push and preset characteristic amount of clothes push. And / or, the user data of the third target object in the at least two different target objects includes at least: push proportion of different types of detergents, total amount of detergent push and preset characteristic amount of detergent push.

[0074] The user data of the first target object, the user data of the second target object, and the user data of the third target object are respectively listed in Table 1.

[0075] Table 1

[0076]

[0077] In Table 1, the preset washing program in the user data of the laundry machine manufacturer can be a related program preset in the washing machine, such as a quick laundry program. The graphene material sales proportion, the bamboo fiber material sales proportion, and the silver ion antibacterial material sales proportion in the user data of the clothing manufacturer can be understood as the push proportions of different material clothes in the user data of the second target object. The total number of clothing sales can be understood as the total amount of clothing push, and the total clothing consumption can be understood as the preset characteristic quantity of clothing push. The sterilization type detergent purchase proportion and the bacteriostatic type detergent purchase proportion in the user data of the detergent manufacturer can be understood as the push proportions of different types of detergents in the user data of the third target object. The total number of sold detergents can be understood as the total amount of detergent push, and the total consumption of sold detergents can be understood as the preset characteristic quantity of detergent push. In addition, it should be noted that, in the user data shown in Table 1, the units of the data ranges corresponding to the user data in each field note are set according to the situation, and the embodiments of the present application are not limited.

[0078] It can be understood that the user data of the first target object, the second target object, and the third target object in the above Table 1 is only illustrative, and does not mean a limitation on the specific content included in the user data of at least two different target objects.

[0079] Optionally, in the embodiments of the present application, the acquisition method of the original data, that is, the acquisition method of the user data of at least two different target objects, can be the reporting method of each different target object. For example, the laundry machine manufacturer reports the user data of user A as listed in the above Table 1, the clothing manufacturer reports the user data of user B as listed in the above Table 1, and the detergent manufacturer reports the user data of user C as listed in the above Table 1. Each target object only has the user data of its own user. The user data of each user can be distinguished by a user identifier, such as a user name, a user's real-name mobile phone number, a user address, and the like.

[0080] S102: determining target intersection data according to a preset privacy set intersection algorithm and the original data.

[0081] The target intersection data includes the user data of each target user, and the target user refers to a common user of at least two different target objects.

[0082] After obtaining the user data of at least two different target objects, the target intersection data is determined based on the user data by using a preset private set intersection algorithm. The target intersection data refers to the user data of common users of the at least two different target objects, and the common user can be defined as a target user.

[0083] The preset private set intersection algorithm can be, for example, a private set intersection (PSI) algorithm, which refers to that participants obtain the intersection of data held by both parties without revealing any additional information. The additional information refers to any information other than the intersection of data of the participants. The target intersection data is determined according to the preset private set intersection algorithm and the original data, which actually obtains the user data of common users of the at least two different target objects, thereby ensuring the privacy of the user data of each target object.

[0084] In a possible design, the step S102 can be implemented in the manner as shown in Figure 3 . Figure 3 Another scheme generation method provided by the embodiment of the present application is shown in the flowchart of Figure 3 . The embodiment of the present application includes the following steps:

[0085] S1021: Determine each target user according to the original data and the preset private set intersection algorithm.

[0086] Each target user carries a user identifier with a preset attribute.

[0087] S1022: Determine the user data of each target user as the target intersection data according to the user identifier.

[0088] For example, the preset private set intersection algorithm is the PSI algorithm, the original data is determined as the input data of the algorithm, the output data of the algorithm is obtained, and the obtained output data is each target user. Each target user carries a user identifier with a preset attribute to distinguish, such as a mobile phone number, and the preset attribute is the attribute of the mobile phone number. Then, the user data of each target user is determined as the target intersection data according to the user identifier.

[0089] According to the foregoing embodiment description, the target user is a common user of at least two different target objects, and thus after obtaining each target object, the user data of each target user can be determined as the target intersection data according to the user identifier of each target user. For example, the target user is user D, user E and user F, each of which carries a user identifier, and then the user data corresponding to user D in each target object is obtained according to the user identifier to obtain the target intersection data corresponding to user D.

[0090] It should be noted that the number of target users is determined by the user data of each target object, and is determined by the actual output data. The specific scheme involved in the use of the preset privacy set intersection algorithm is not limited, for example, it can involve Oblivious Transfer (OT) and the like, which is not limited by the embodiments of the present application.

[0091] The scheme generation method provided by the embodiments of the present application first determines each target user by using the preset privacy set intersection algorithm on the original data, and then obtains the user data of each target user according to the user identifier carried by each target user to obtain the target intersection data. The use of the preset privacy set intersection algorithm ensures the privacy of the user data possessed by each target object.

[0092] S103: generating a target scheme according to the target intersection data and a preset formulation strategy.

[0093] After obtaining the target intersection data, a target scheme is generated according to the target intersection data and a preset formulation strategy.

[0094] The target scheme can include at least two different target objects pushing their respective to-be-pushed objects in a pushing manner, for example, it can include a first target scheme of pushing the to-be-pushed objects in a preset bundling mode and a second target scheme of pushing each to-be-pushed object in a preset independent mode. The preset bundling mode can be understood as that each target object uses a bundling pushing manner for its respective to-be-pushed object, and the specific content of the bundling pushing is the specific content of the preset bundling mode agreed by each target object. The specific content is set by the actual working condition, and the embodiments of the present application are not limited.

[0095] The preset formulation strategy can be understood as an implementation strategy of generating a target scheme based on the target intersection data, which is used to distinguish the first target scheme and the second target scheme, so as to formulate a precise pushing manner for each target object. For example, if it is judged through the preset formulation strategy that each target object has the intention of pushing the to-be-pushed object in the preset bundled mode, the generated target scheme is the first target scheme. On the contrary, if it is judged through the preset formulation strategy that each target object does not have the intention of pushing the to-be-pushed object in the preset bundled mode, and may have the intention of pushing the respective to-be-pushed object in the preset independent mode, the generated target scheme is the second target scheme. It should be noted that the preset bundled mode pushing the to-be-pushed object is the preset bundled mode selling the to-be-sold product, and the preset independent mode pushing the to-be-pushed object is the preset independent mode selling the to-be-sold product.

[0096] The scheme generation method provided by the embodiment of the present application first acquires original data, wherein the original data includes user data of at least two different target objects. Then, the target intersection data is determined according to the preset privacy set intersection algorithm and the original data, the determined target intersection data includes user data of each target user, and the target user refers to a common user of the at least two different target objects. Then, the target scheme is generated according to the target intersection data and the preset formulation strategy, so as to formulate a precise pushing manner of pushing respective to-be-pushed objects for the at least two different target objects. Moreover, the privacy of the user data of each target object of the at least two different target objects is guaranteed, and the security of the user data is guaranteed.

[0097] Figure 4 The flowchart of another scheme generation method provided by the embodiment of the present application is shown in FIG. 6. As shown in FIG. 6, the embodiment of the present application includes: Figure 4

[0098] S201: acquiring original data.

[0099] The original data includes user data of at least two different target objects.

[0100] S202: determining target intersection data according to a preset privacy set intersection algorithm and the original data.

[0101] The target intersection data includes user data of each target user, and the target user refers to a common user of the at least two different target objects.

[0102] The implementation manner, principle and corresponding technical effects of step S201 and step S202 are similar to those of step S101 and step S102 in the foregoing embodiment, and the detailed description can be referred to the foregoing embodiment, which will not be repeated here.

[0103] ​S203: determining a target feature value according to the target intersection data, a preset evaluation algorithm, and a preset weight value.

[0104] S204: generating a target scheme according to a preset judgment strategy and the target feature value.

[0105] The preset judgment strategy includes the preset evaluation algorithm, the preset weight value, and the preset judgment strategy.

[0106] After obtaining the target intersection data, the target intersection data and the preset weight value are applied to the preset evaluation algorithm to determine the target feature value.

[0107] For example, for a target user, the target feature value of the target user is determined based on the target intersection data of the target user and the preset weight value by using Formula (1) as shown below.

[0108] F = W1*Q1 + W2*Q2 + W3*Q3 (1)

[0109] Wherein, F represents the target feature value of any target user, W1, W2, and W3 are preset weight values corresponding to each target object, Q1, Q2, and Q3 are user data corresponding to the target user on each target object, for example, Q1 represents the user data of the target object on the first target object, Q2 represents the user data of the target object on the second target object, and Q3 represents the user data of the target object on the third target object, which are collectively referred to as target intersection data.

[0110] It can be understood that the number of preset weight values is the same as the number of target objects, and each target object corresponds to a preset weight value. The specific value of the preset weight value is determined by negotiation of each target object.

[0111] The values of Q1, Q2, and Q3 can be the sum of each part included in the user data. Referring to Table 1, it is assumed that the user data corresponding to the target user on the washing machine manufacturer is 8, 5, 3, 60, and 0.3, respectively, in order. The corresponding Q1 is the sum of 8, 5, 3, 60, and 0.3, and Q2 and Q3 are determined in a similar manner to Q1, and the unit of measurement can be ignored.

[0112] From the above description, it can be seen that for each target user, Q1, Q2 and Q3 are uniquely determined values regardless of which of the target objects, and for each target object, the number of target feature values F obtained by formula (1) is not unique if the values of W1, W2 and W3 are set by each target object respectively. Conversely, if a set of values of W1, W2 and W3 recognized by each target object is set, the target feature value F obtained by formula (1) is unique.

[0113] Therefore, in a possible design, a possible implementation manner of step S204 can be as shown in Figure 5 . Figure 5 Another scheme development method provided by the embodiment of the present application is provided. As shown in Figure 5 , the embodiment of the present application includes:

[0114] S2041: determining whether the target feature value of each target user is unique.

[0115] S2042: if yes, generating a first target scheme.

[0116] The first target scheme refers to that at least two different target objects push their respective to-be-pushed objects according to a preset bundling mode.

[0117] S2043: if no, generating a second target scheme.

[0118] The second target scheme refers to that at least two different target objects push their respective to-be-pushed objects according to their respective preset independent mode.

[0119] The target scheme includes the first target scheme and the second target scheme.

[0120] Determining whether the target feature value of each target user is unique, if yes, it indicates that each target object recognizes a set of preset weight values, and each target object has a bundling pushing intention for the to-be-pushed objects, and the first target scheme in the target scheme is generated, which refers to that at least two different target objects push their respective to-be-pushed objects according to a preset bundling mode. For example, when a washing machine manufacturer, a clothing manufacturer and a detergent manufacturer recognize a set of preset weight values, the target feature value of each target user obtained by formula (1) is unique, that is, it indicates that the washing machine manufacturer, the clothing manufacturer and the detergent manufacturer can have a bundling sales intention for their respective products, and each manufacturer can sell their respective products according to a preset bundling mode. Specifically, the specific content of the preset bundling mode can be set by each target object according to actual conditions, and the embodiment of the present application is not limited.

[0121] Conversely, if it is judged that the target characteristic value of each target user is not unique, it indicates that each target object sets the preset weight value, and each target object does not have the push intention of bundled push for the to-be-pushed object, and can have the push intention of individual push, and thus a second target scheme in the target schemes is generated, the second target scheme refers to that at least two different target objects push respective to-be-pushed objects according to respective preset independent modes. For example, when a washing machine manufacturer, a clothing manufacturer, and a detergent manufacturer each set a preset weight value, the target characteristic value of each target user obtained through formula (1) is not unique, and the washing machine manufacturer, the clothing manufacturer, and the detergent manufacturer can not have the sales intention of bundled sales for respective products, and can have the sales intention of individual sales, and each manufacturer can sell respective products according to a preset independent mode. Specifically, the specific content of the preset independent mode can be set by each target object according to actual conditions, and the embodiments of the present application are not limited.

[0122] The scheme generation method provided in the embodiments of the present application first acquires original data, wherein the original data includes user data of at least two different target objects. Then, target intersection data is determined according to a preset privacy set intersection algorithm and the original data, the determined target intersection data includes user data of each target user, and the target user refers to a common user of the at least two different target objects. Then, a target characteristic value of each target user is determined according to the target intersection data, a preset evaluation algorithm, and a preset weight value, and then a first target scheme and a second target scheme in the target schemes are respectively generated by judging whether the target characteristic value of each target user is unique. For each different target object, a precise push mode is formulated to push respective to-be-pushed objects. Moreover, the privacy of user data of each target object of the at least two different target objects is ensured, and the security of the user data is ensured.

[0123] Figure 6 A structural schematic diagram of a scheme generation device provided in the embodiments of the present application is shown in FIG. 3. As shown in FIG. 3, the scheme generation device 300 provided in the embodiments of the present application includes: Figure 6

[0124] The acquisition module 301 is configured to acquire original data.

[0125] The original data includes user data of at least two different target objects.

[0126] The processing module 302 is configured to determine target intersection data according to a preset privacy set intersection algorithm and the original data.

[0127] The target intersection data includes user data of each target user, and the target user refers to a common user of the at least two different target objects.

[0128] ​The generating module 303 is configured to generate a target scheme according to the target intersection data and a preset making strategy.

[0129] In a possible design, the processing module 302 is specifically configured to:

[0130] determine each target user according to the original data and a preset privacy set intersection algorithm, and each target user carries a user identifier of a preset attribute;

[0131] determine user data of each target user as the target intersection data according to the user identifier.

[0132] In a possible design, the generating module 303 is specifically configured to:

[0133] determine a target feature value of each target user according to the target intersection data, a preset evaluation algorithm and a preset weight value;

[0134] generate a target scheme according to a preset judgment strategy and the target feature value of each target user;

[0135] The preset making strategy includes the preset evaluation algorithm, the preset weight value and the preset judgment strategy.

[0136] In a possible design, the generating module 303 is further specifically configured to:

[0137] determine whether the target feature value of each target user is unique;

[0138] if yes, generate a first target scheme, and the first target scheme refers to that at least two different target objects push respective to-be-pushed objects according to a preset bundling mode;

[0139] if no, generate a second target scheme, and the second target scheme refers to that at least two different target objects push respective to-be-pushed objects according to respective preset independent modes;

[0140] The target scheme includes the first target scheme and the second target scheme.

[0141] In a possible design, the respective to-be-pushed objects of the at least two different target objects have functional correlation.

[0142] In a possible design, the user data of the first target object at least includes: single laundry weight, washing frequency in a preset period, single rinsing times, total use time and preset washing program proportion; and / or

[0143] The user data of the second target object at least includes: push proportion of different material clothes, total amount of clothes push and preset feature amount of clothes push; and / or

[0144] The user data of the third target object at least includes a push proportion of different types of detergents, a total amount of detergent push, and a preset characteristic amount of detergent push.

[0145] The at least two different target objects include at least two of the first target object, the second target object, and the third target object.

[0146] It is worth noting that the above Figure 6 The scheme generation apparatus provided in the optional embodiments can be used to execute each step of the scheme generation method provided in any of the above embodiments, and the specific implementation manners and technical effects are similar, and thus will not be described here.

[0147] The above device embodiments provided in the present application are merely illustrative, and the module division therein is merely a logical function division. In actual implementation, another division manner can be used. For example, multiple modules can be combined or integrated into another system. The coupling between the modules can be realized through some interfaces, which are usually electrical communication interfaces, but mechanical interfaces or other forms of interfaces are not excluded. Therefore, the modules described as separate components can be or can not be physically separated, and can be located in one place or distributed to different locations of the same or different devices.

[0148] Figure 7 A structural schematic diagram of an electronic device is provided in the present application. As shown in the figure, Figure 7 The electronic device 400 can include at least one processor 401 and a memory 402. Figure 7 An electronic device with one processor is shown.

[0149] The memory 402 is used to store the computer program of the processor 401. Specifically, the program can include program code including computer operation instructions.

[0150] The memory 402 can include a high-speed RAM memory, and can also include a non-volatile memory such as at least one disk memory.

[0151] The processor 401 is configured to execute the computer program stored in the memory 402 to implement each step of the scheme generation method in the above method embodiments.

[0152] The processor 401 can be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to perform the operations of the embodiments of the present application.

[0153] Optionally, the memory 402 can be independent or integrated with the processor 401. When the memory 402 is independent of the processor 401, the electronic device 400 can further include:

[0154] The bus 703 is used to connect the processor 401 and the memory 402. The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, an extended industry standard architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, and the like, but does not mean that there is only one bus or one type of bus.

[0155] Optionally, in a specific implementation, if the memory 402 and the processor 401 are integrated on a chip, the memory 402 and the processor 401 can communicate through an internal interface.

[0156] The present application also provides a computer readable storage medium, which can include a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various storage program codes. Specifically, the computer readable storage medium stores a computer program, and when at least one processor of the electronic device executes the computer program, the electronic device executes the steps of the scheme generation method provided by the various embodiments.

[0157] The embodiments of the present application also provide a computer program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to make the electronic device implement the steps of the scheme generation method provided by the various embodiments.

[0158] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0159] It is to be understood that the application is not limited to the precise construction herein disclosed and shown in the drawings, and that various changes in shape, size and arrangements of parts can be made without departing from the scope of the application. The scope of the application is limited only by the claims that follow.

Claims

1. A scheme generation method, characterized in that, include: Obtain raw data, which includes user data from at least two different target objects; The at least two different target objects include washing machine manufacturers, clothing manufacturers and / or detergent manufacturers, and the objects to be pushed to each of the at least two different target objects are functionally related; The target intersection data is determined based on the preset privacy set intersection algorithm and the original data. The target intersection data includes the user data of each target user. The target user refers to the user shared by the at least two different target objects. The target feature value of each target user is determined based on the target intersection data, the preset evaluation algorithm, and the preset weight value. A target scheme is generated based on a preset judgment strategy and the target feature values ​​of each target user. The step of generating a target scheme based on a preset judgment strategy and the target feature values ​​of each target user includes: judging whether the target feature value of each target user is unique according to the preset judgment strategy; If so, generate a first target scheme, wherein the first target scheme refers to the push of the respective objects to be pushed by the at least two different target objects according to a preset bundling mode; If not, a second target scheme is generated, which refers to the at least two different target objects pushing their respective objects to be pushed according to their respective preset independent modes; wherein, the target scheme includes the first target scheme and the second target scheme.

2. The scheme generation method according to claim 1, characterized in that, The step of determining the target intersection data based on the preset privacy set intersection algorithm and the original data includes: The target users are determined based on the original data and the preset privacy set intersection algorithm, and each target user carries a user identifier with preset attributes. The user data of each target user is determined as the target intersection data according to the user identifier.

3. The scheme generation method according to claim 1, characterized in that, The user data of the first target object includes at least: weight of clothes washed in a single wash, washing frequency within a preset cycle, number of rinses per wash, total usage time, and percentage of preset washing programs; and / or The user data of the second target object includes at least: the proportion of clothing pushed by different materials, the total number of clothing pushed, and the preset feature quantities of clothing pushed; and / or The user data of the third target object includes at least: the proportion of different types of detergents pushed, the total amount of detergents pushed, and the preset feature quantity of detergent push; The at least two different target objects include at least two of the first target object, the second target object, and the third target object.

4. A scheme generation apparatus, characterized in that, include: The acquisition module is used to acquire raw data, which includes user data of at least two different target objects. The at least two different target objects include washing machine manufacturers, clothing manufacturers and / or detergent manufacturers, and the objects to be pushed to each of the at least two different target objects are functionally related; The processing module is used to determine the target intersection data according to the preset privacy set intersection algorithm and the original data. The target intersection data includes the user data of each target user, and the target user refers to the user shared by the at least two different target objects. The generation module is used to determine the target feature value of each target user based on the target intersection data, the preset evaluation algorithm, and the preset weight value; A target scheme is generated based on a preset judgment strategy and the target feature values ​​of each target user. The generation module is further specifically used to: determine whether the target feature value of each target user is unique according to a preset judgment strategy; If so, generate a first target scheme, wherein the first target scheme refers to the push of the respective objects to be pushed by the at least two different target objects according to a preset bundling mode; If not, a second target scheme is generated, which refers to the at least two different target objects pushing their respective objects to be pushed according to their respective preset independent modes; wherein, the target scheme includes the first target scheme and the second target scheme.

5. An electronic device, characterized in that, include: processor; as well as, Memory for storing the computer program of the processor; The processor is configured to execute the scheme generation method according to any one of claims 1 to 3 by executing the computer program.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the scheme generation method according to any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the scheme generation method according to any one of claims 1 to 3.

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