A method and system for information push based on cloud computing and big data

Abstract

The present invention provides an information push method and system based on cloud computing and big data, and relates to the technical field of big data analysis. The information push method obtains user data and determines information to be pushed based on the user data, thereby realizing personalized push for each user, improving the relevance of the pushed content and user experience; by determining the time interval to be pushed based on a time curve and a time threshold, the push time can be dynamically adjusted, so that the push information can be sent to the user within the most appropriate time period, thereby increasing the validity of the information and the satisfaction of the publisher; at the same time, before pushing, by obtaining environmental data at the target time and determining a privacy tolerance parameter based on the environmental data, it is possible to avoid pushing at a time and place that is not suitable for the user to receive information, thereby protecting the user's privacy, making the pushed information more in line with the user's current status and needs, and improving the user's satisfaction.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to an information push method and system based on cloud computing and big data. Background Art

[0002] The development of information technology and the popularization of the Internet have led to the widespread application of information push technology in various fields, such as e-commerce, news media, and social networks. Information push technology actively pushes information of interest to the user terminal to improve the efficiency and convenience of users in obtaining information. However, traditional information push methods usually push information based on simple user attributes (such as age, gender, geographic location, etc.). Although this method can meet the needs of users to a certain extent, it often lacks in-depth user behavior analysis and cannot accurately capture the privacy needs of users. It is easy to cause user dissatisfaction and may even lead to a decrease in user trust in the platform. At the same time, traditional information push methods cannot take into account the cost-effectiveness requirements of information pushers, which can easily cause waste of information push resources and reduce the trust of information pushers in the platform.

[0003] In summary, the information push methods in related technologies have many limitations, and it is difficult to balance the cost-effectiveness requirements of information senders for information push and the privacy requirements of users for information reception. Summary of the invention

[0004] The present invention provides an information push method based on cloud computing and big data, comprising:

[0005] Get user data;

[0006] Determine the information to be pushed based on user data;

[0007] Determine a time-limit curve based on the information to be pushed and the historical data in the cloud platform; wherein the time-limit curve includes multiple time nodes and time-limit parameters corresponding to the time nodes;

[0008] Based on the timeliness curve and timeliness threshold, determine the time interval to be pushed;

[0009] Determine whether the target time is within the push time interval;

[0010] If yes, then obtain the environmental data of the target time and the timeliness parameter of the target time in the timeliness curve, and determine the privacy tolerance parameter based on the environmental data; if no, determine whether the next target time is within the push time interval;

[0011] Determine the push strategy based on privacy tolerance parameters and timeliness parameters.

[0012] Further, the environmental data includes network type data, location data and timestamp data;

[0013] Determine privacy tolerance parameters based on environmental data, including:

[0014] Based on the network type data, determining a network type level to which the network type data belongs;

[0015] Based on the location data, determining a location class to which the location data belongs;

[0016] Determine scenario parameters based on network type level and location level;

[0017] Determine whether the timestamp data is within a preset time interval, and determine a weight parameter based on the determination result; wherein the preset time interval does not completely overlap with the push time interval;

[0018] Based on the scenario parameters and weight parameters, the privacy tolerance parameters are determined.

[0019] Further, the network type level includes a high protection network level and a low protection network level, and the location level includes a high privacy location level, a medium privacy location level, and a low privacy location level;

[0020] Based on the network type level and location level, the scenario parameters are determined, including:

[0021] Determining scenario parameters based on a combination of the network type level and the location level;

[0022] Among them, the scene parameters include at least a first scene parameter, a second scene parameter, a third scene parameter and a fourth scene parameter. The first scene parameter is used to characterize a scene with high privacy, the second scene parameter is used to characterize a scene with lower privacy than the first scene parameter, the third scene parameter is used to characterize a scene with lower privacy than the second scene parameter, and the fourth scene parameter is used to characterize a scene with lower privacy than the third scene parameter.

[0023] Furthermore, based on the scenario parameters and the weight parameters, the privacy tolerance parameters are determined, including:

[0024]

[0025] Among them, x is the privacy tolerance parameter, k is the weight parameter, and n is the scenario parameter.

[0026] Furthermore, the timeliness parameters corresponding to the multiple time nodes within the time interval to be pushed are all greater than or equal to the timeliness threshold.

[0027] Furthermore, based on the privacy tolerance parameter and the timeliness parameter, a push strategy is determined, including:

[0028] Determine a tolerance score based on the ratio of the privacy tolerance parameter to the tolerance threshold;

[0029] Determine an aging score based on the ratio of the aging parameter to the aging threshold;

[0030] Determine the push score based on the product of the tolerance score and the timeliness score;

[0031] In response to the push score being greater than or equal to the first score, determining the push strategy to be high push priority and multi-platform push, wherein the multi-platform push includes a combination of SMS push, phone push, and application push;

[0032] In response to the push score being greater than or equal to the second score, determining the push strategy as medium push priority and single platform push, wherein the single platform push includes SMS push, phone push, or application push;

[0033] In response to the push score being greater than or equal to the third score, the push policy is determined to be a non-push policy.

[0034] Furthermore, user data includes behavioral data, social data, and shopping data;

[0035] Based on user data, determine the information to be pushed, including:

[0036] Determine the basic user profile based on social data;

[0037] Determine user preference profiles based on behavioral data;

[0038] Determine user habit profiles based on shopping data;

[0039] Determine category labels based on user basic profiles and user preference profiles;

[0040] Determine brand labels based on user habit portraits and category labels;

[0041] Determine the information to be pushed based on category tags and brand tags.

[0042] Furthermore, the class label is associated with a tolerance threshold.

[0043] Furthermore, social data includes at least personal information, friend relationship data, dynamic data and interactive data; behavioral data includes at least browsing data, click data and search data; shopping data includes at least shopping cart data, browsing data and evaluation data.

[0044] The present invention also provides an information push system based on cloud computing and big data, comprising:

[0045] Acquisition module, used to obtain user data;

[0046] An information screening module is used to determine the information to be pushed based on user data;

[0047] The analysis module is used to determine a time curve based on the information to be pushed and the historical data in the cloud platform; wherein the time curve includes multiple time nodes and the time parameters corresponding to the time nodes; it is also used to determine the time interval to be pushed based on the time curve and the time threshold; it is also used to determine whether the target moment is in the push time interval; if so, obtain the environmental data of the target moment and the time parameters of the target moment in the time curve, and determine the privacy tolerance parameter based on the environmental data; if not, determine whether the next target moment is in the push time interval.

[0048] The policy module is used to determine the push policy based on the privacy tolerance parameter and the timeliness parameter.

[0049] Compared with the prior art, the information push method and system based on cloud computing and big data provided by the present invention have at least the following beneficial effects:

[0050] The information push method based on cloud computing and big data obtains user data, determines the information to be pushed based on the user data, realizes personalized push for each user, and improves the relevance of the pushed content and user experience; by determining the time interval to be pushed based on the time curve and the time threshold, the push time can be dynamically adjusted so that the push information can be sent to the user in the most appropriate time period, increasing the effectiveness of the information and the satisfaction of the publisher; at the same time, before pushing, by obtaining the environmental data at the target time and determining the privacy tolerance parameter based on the environmental data, it can avoid pushing at a time and place that is not suitable for the user to receive information, protect the user's privacy, make the push information more in line with the user's current status and needs, and improve user satisfaction. This method can simultaneously guarantee the information publisher's delivery needs and the user's receiving needs, thereby improving user satisfaction while enabling the information delivered by the publisher to be effectively utilized, reducing the delivery of unnecessary information, and increasing the accuracy of information delivery. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0052] Figure 1 It is a flow chart of an information push system based on cloud computing and big data according to some embodiments of this specification;

[0053] Figure 2 It is a structural block diagram of an information push system based on cloud computing and big data according to some embodiments of this specification;

[0054] Figure 3A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0055] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0056] Figure 1 is a flow chart of an information push system based on cloud computing and big data according to some embodiments of this specification, such as Figure 1 As shown, a method for pushing information based on cloud computing and big data may include the following steps. Each step is described in detail below.

[0057] Step S100, obtaining user data;

[0058] In this embodiment, user data includes behavior data, social data and shopping data. Social data includes at least personal information, friend relationship data, dynamic data and interactive data; behavior data includes at least browsing data, click data and search data; shopping data includes at least shopping cart data, browsing data and evaluation data.

[0059] Step S200, determining information to be pushed based on user data;

[0060] In this embodiment, the step may include:

[0061] Determine the basic user profile based on social data. It is understandable that the basic user profile can indicate the user's possible product target needs in the short term in the future. For example, through social data analysis, the basic user profile is obtained as (gender: male; age: 16 years old; occupation: student), then it can be inferred that the user's possible product target needs at this stage are books.

[0062] Determine the user preference profile based on behavioral data. The user preference profile can indicate the user's current preferred product needs. For example, through behavioral data analysis, the user preference profile is (browsing keyword: mobile phone; number of clicks: 10 times; search time: 30 minutes), then it can be inferred that the user's current preferred product needs are mobile phones.

[0063] Determine the user habit profile based on shopping data. For example, if the user habit profile is (high cost performance) through shopping data, then we can infer some suitable brands in the product category that meet the user's needs.

[0064] Determine category labels (for example, mobile phones and books mentioned above) based on the basic user profile and user preference profile.

[0065] Determine brand labels based on user habit profiles and category labels (for example, some cost-effective brands of mobile phones and books);

[0066] Determine the information to be pushed based on category tags and brand tags.

[0067] Step S300, determining a time-efficiency curve based on the information to be pushed and the historical data in the cloud platform; wherein the time-efficiency curve includes a plurality of time nodes and time-efficiency parameters corresponding to the time nodes;

[0068] It is worth noting that the timeliness curve is derived from the historical data of the message to be pushed, which represents the probability of users clicking on the push information in the subsequent time period in the historical data. For example, if the probability of users clicking on a certain push information is 60% between 11:00 and 12:00 noon, and 30% between 1:00 and 2:00 p.m., it means that the timeliness parameter corresponding to 11:00 and 12:00 is 60%, and the timeliness parameter corresponding to 1:00 and 2:00 p.m. is 30%.

[0069] Step S400: Determine the time interval to be pushed based on the timeliness curve and the timeliness threshold.

[0070] Specifically, the timeliness parameters corresponding to multiple time nodes within the time interval to be pushed are all greater than or equal to the timeliness threshold (for example, 50%). It is understandable that, for example, for a takeaway push message, if the user searches for keyword information such as "lunch" at 9 a.m., the time period with a timeliness parameter greater than the timeliness threshold can be determined as the time interval to be pushed (for example, the timeliness parameter from 11 a.m. to 12 p.m. is 60%, and 60% is greater than 50%, so at this time, the user's willingness to click after receiving the push message is stronger, and the timeliness parameter corresponding to the click probability is higher, so the time interval to be pushed can be determined).

[0071] Step S500, determining whether the target time is within the push time interval;

[0072] Step S610: If yes, then obtain the environmental data at the target time and the aging parameter at the target time in the aging curve, and determine the privacy tolerance parameter based on the environmental data;

[0073] In this embodiment, the environmental data includes network type data, location data and timestamp data. It is understandable that the network type data may include WIFI, 4G, 5G, etc. The location data may include the geographical location of the terminal device (e.g., home, office, public place, etc.). The timestamp data refers to the timestamp of the current moment. It is also understandable that since WIFI is usually used in home or office environments, the privacy tolerance in the WIFI environment will be higher than that in the 4G or 5G environment. It is also understandable that through the acquisition of location data, the privacy tolerance is higher in the environment of home or private space; while in the environment of public places and office locations, its privacy tolerance will be lower than the privacy tolerance in the home environment. It is also understandable that, such as during non-working hours (evenings or weekends), its privacy tolerance is higher than the privacy tolerance in the working time environment.

[0074] In step S610, steps S611 to S615 may also be included.

[0075] Step S611, based on the network type data, determining the network type level to which the network type data belongs;

[0076] Step S612, based on the location data, determining the location level to which the location data belongs;

[0077] In this embodiment, the network type level includes a high protection network level and a low protection network level, and the location level includes a high privacy location level, a medium privacy location level, and a low privacy location level. It can be understood that the WIFI scenario is usually used in a home or office environment, which corresponds to a high protection network level, while the 4G or 5G scenario corresponds to a low protection network level. In the home or private space environment, the corresponding location level may be a high privacy location level, which has a higher degree of privacy due to its more private location; in the office environment, the corresponding location level may be a medium privacy location level, which has a slightly lower degree of privacy than the home or private space scenario; and in the public place environment, the corresponding location level may be a low privacy location level, which generally has a lower degree of privacy.

[0078] Step S613, determining scene parameters based on the network type level and the location level;

[0079] Optionally, step S611 may include: determining scene parameters based on a combination set of network type level and location level; wherein the scene parameters include at least a first scene parameter, a second scene parameter, a third scene parameter and a fourth scene parameter, the first scene parameter is used to characterize a scene with high privacy, the second scene parameter is used to characterize a scene with lower privacy than the first scene parameter, the third scene parameter is used to characterize a scene with lower privacy than the second scene parameter, and the fourth scene parameter is used to characterize a scene with lower privacy than the third scene parameter.

[0080] Specifically, the first scene parameter, the second scene parameter, the third scene parameter and the fourth scene parameter can all be in numerical form, for example, the first scene parameter is 10 points, the second scene parameter is 8 points, the third scene parameter is 5 points, and the fourth scene parameter is 3 points.

[0081] Optionally, based on a combination of the network type level and the location level, the step of determining the scene parameter may include:

[0082] When the network type level is a high protection network level and the location level is a high privacy location level, the scene parameter is a first scene parameter;

[0083] When the network type level is a high protection network level and the location level is a medium privacy location level; and when the network type level is a low protection network level and the location level is a high privacy location level, the scene parameter is a second scene parameter;

[0084] When the network type level is a high protection network level and the location level is a low privacy location level; and when the network type level is a low protection network level and the location level is a medium privacy location level, the scene parameter is a third scene parameter;

[0085] When the network type level is a low protection network level and the location level is a low privacy location level, the scene parameter is the fourth scene parameter.

[0086] It is worth noting that by classifying the combination of network type and location level, scenarios of different privacy properties can be accurately identified. The scenario of a combination of a high-protection network and a high-privacy location (for example, a user browsing data through WIFI at home) can be set as the first scenario parameter, indicating that the user is in a high-privacy scenario at this time, which can be conducive to pushing some highly sensitive information (for example, medical information, etc.); and the scenario of a combination of a low-protection network and a low-privacy location (for example, a user browsing data through a 5G network on the subway) can be set as the fourth scenario parameter, indicating that the user is in a low-privacy scenario at this time, which is not conducive to pushing highly sensitive information to avoid users receiving inappropriate information. This method can make the information received by users more in line with their needs and expectations, thereby enhancing users' trust in and desire to use information push services.

[0087] Step S614, determining whether the timestamp data is within a preset time interval, and determining a weight parameter based on the determination result; wherein the preset time interval does not completely overlap with the push time interval;

[0088] The steps of determining whether the timestamp data is in a preset time interval and determining a weight parameter based on the determination result may include: setting the preset time to 8 pm to 12 midnight, when the user is more likely to be in a high privacy scenario; if the timestamp data is in the preset time interval, determining that the weight parameter may be a first weight parameter (for example, 1); and when the timestamp data is not in the preset time interval, determining that the weight parameter may be a second weight parameter (for example, 0.5).

[0089] Accordingly, if the preset time interval does not completely overlap with the push time interval, the push effect and user experience can be effectively balanced, and the ability of the push method in terms of privacy protection and personalized services can also be improved.

[0090] Step S615: determining a privacy tolerance parameter based on the scenario parameter and the weight parameter.

[0091] Specifically, we can use

[0092]

[0093] Among them, x is the privacy tolerance parameter, k is the weight parameter, and n is the scenario parameter.

[0094] Step S620, if not, determine whether the next target time is within the push time interval;

[0095] It is worth noting that if the target time is not within the push time interval, the information to be pushed can be retained and the next target time can be determined. This step can avoid missing the message to be pushed. Specifically, after multiple target times (for example, 3) are not within the push time interval, the message to be pushed can be cancelled. The time period between two adjacent target times can be fixed, for example, 6 hours.

[0096] Step S700: Determine a push strategy based on a privacy tolerance parameter and a timeliness parameter.

[0097] Specifically, step S700 may include the following steps:

[0098] Determine a tolerance score based on the ratio of the privacy tolerance parameter to the tolerance threshold;

[0099] Determine an aging score based on the ratio of the aging parameter to the aging threshold;

[0100] Determine the push score based on the product of the tolerance score and the timeliness score;

[0101] In response to the push score being greater than or equal to the first score, determining the push strategy to be high push priority and multi-platform push, wherein the multi-platform push includes a combination of SMS push, phone push, and application push;

[0102] In response to the push score being greater than or equal to the second score, determining the push strategy as medium push priority and single platform push, wherein the single platform push includes SMS push, phone push, or application push;

[0103] In response to the push score being greater than or equal to the third score, the push policy is determined to be a non-push policy.

[0104] Among them, the first score is greater than the second score, and the second score is greater than the third score.

[0105] It is worth noting that the setting of push scores divides push strategies into three levels: high priority, medium priority, and no push. According to the different thresholds of push scores, the push strategy can be accurately adjusted to improve the effectiveness of information push and user response rate. For example, for high push priority and multi-platform push strategies, information can be conveyed more effectively at important moments or in emergencies. In addition, by setting different push strategies, it is possible to avoid pushing too much information at inappropriate times, thereby improving the user experience and satisfaction. For example, when the user is in a time period that is not suitable for receiving information, the system chooses not to push or to push on a single platform to avoid disturbing the user.

[0106] In addition, in this embodiment, the category label and the tolerance threshold are related to each other. For example, the tolerance threshold of mobile phones and books is relatively low, which may be 3; while the tolerance threshold of medical products and drugs is relatively high, which may be 5.

[0107] Figure 2 is a structural block diagram of an information push system based on cloud computing and big data according to some embodiments of this specification, such as Figure 2 As shown, an information push system based on cloud computing and big data may include: an acquisition module for acquiring user data; an information screening module for determining information to be pushed based on user data; an analysis module for determining a time-limit curve based on the information to be pushed and historical data in the cloud platform; wherein the time-limit curve includes multiple time nodes and time-limit parameters corresponding to the time nodes; and further for determining a time interval to be pushed based on the time-limit curve and the time-limit threshold; and further for determining whether a target moment is within the push time interval; if so, obtaining environmental data of the target moment and the time-limit parameters of the target moment in the time-limit curve, and determining a privacy tolerance parameter based on the environmental data; if not, determining whether the next target moment is within the push time interval. A policy module for determining a push policy based on the privacy tolerance parameter and the time-limit parameter.

[0108] An information push system based on cloud computing and big data can apply an information push method based on cloud computing and big data. For more descriptions of an information push system based on cloud computing and big data, please refer to the relevant descriptions of an information push method based on cloud computing and big data, which will not be repeated here.

[0109] Figure 3 The structure diagram of the computer system suitable for implementing the electronic device of the embodiment of the present application is shown. It should be noted that: Figure 3 The computer system 300 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0110] like Figure 3 As shown, the computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0111] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0112] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part 309, and / or installed from a removable medium 311. When the computer program is executed by a central processing unit (CPU) 301, various functions defined in the system of the present application are executed.

[0113] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier, which carries a computer-readable computer program. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0114] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0115] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0116] Another aspect of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor of a computer, the computer executes the above method. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0117] Another aspect of the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the information push method based on cloud computing and big data provided in each of the above embodiments.

[0118] The above embodiments are only preferred embodiments for fully illustrating the present application, and the protection scope of the present application is not limited thereto. Any equivalent substitution or change made by a person skilled in the art based on the present application is within the protection scope of the present application.

Claims

1. A method for information push based on cloud computing and big data, characterized in that: include: Get user data; Determining information to be pushed based on the user data; Determine a time-efficiency curve based on the information to be pushed and the historical data in the cloud platform; wherein the time-efficiency curve includes a plurality of time nodes and time-efficiency parameters corresponding to the time nodes; Based on the timeliness curve and the timeliness threshold, determine the time interval to be pushed; Determine whether the target time is within the push time interval; If yes, then obtain the environmental data of the target time and the timeliness parameter of the target time in the timeliness curve, and determine the privacy tolerance parameter based on the environmental data; if no, determine whether the next target time is within the push time interval; Determine a push strategy based on the privacy tolerance parameter and the timeliness parameter; The determining of the push strategy based on the privacy tolerance parameter and the timeliness parameter includes: Determining a tolerance score based on a ratio of the privacy tolerance parameter to a tolerance threshold; Determining an aging score based on a ratio of the aging parameter to the aging threshold; Determine a push score based on the product of the tolerance score and the timeliness score; In response to the push score being greater than or equal to the first score, determining the push strategy to be high push priority and multi-platform push, wherein the multi-platform push includes a combination of SMS push, phone push, and application push; In response to the push score being greater than or equal to the second score, determining the push strategy as medium push priority and single platform push, wherein the single platform push includes SMS push, phone push or application push; In response to the push score being greater than or equal to a third score, determining the push strategy to be a non-push strategy; The environmental data includes network type data, location data and timestamp data, and determining the privacy tolerance parameter based on the environmental data includes: Based on the network type data, determining a network type level to which the network type data belongs; Based on the location data, determining a location level to which the location data belongs; Determining a scenario parameter based on the network type level and the location level; Determine whether the timestamp data is within a preset time interval, and determine a weight parameter based on the determination result; wherein the preset time interval does not completely overlap with the push time interval; The privacy tolerance parameter is determined based on the scenario parameter and the weight parameter.

2. The information push method based on cloud computing and big data according to claim 1, characterized in that: The network type level includes a high protection network level and a low protection network level, and the location level includes a high privacy location level, a medium privacy location level and a low privacy location level; The determining of scene parameters based on the network type level and the location level includes: Determining the scenario parameters based on a combination of the network type level and the location level; Among them, the scene parameters include at least a first scene parameter, a second scene parameter, a third scene parameter and a fourth scene parameter. The first scene parameter is used to characterize a scene with high privacy, the second scene parameter is used to characterize a scene with lower privacy than the first scene parameter, the third scene parameter is used to characterize a scene with lower privacy than the second scene parameter, and the four scene parameters are used to characterize a scene with lower privacy than the third scene parameter.

3. The information push method based on cloud computing and big data according to claim 2, characterized in that: The determining the privacy tolerance parameter based on the scenario parameter and the weight parameter includes: ;in, x is the privacy tolerance parameter, k is the weight parameter, n is the scene parameter.

4. The information push method based on cloud computing and big data according to claim 1, characterized in that: The timeliness parameters corresponding to the multiple time nodes within the time interval to be pushed are all greater than or equal to the timeliness threshold.

5. The information push method based on cloud computing and big data according to claim 1, characterized in that: The user data includes behavioral data, social data and shopping data; Based on the user data, determining information to be pushed includes: Determine a basic user profile based on the social data; Determine a user preference profile based on the behavioral data; Determine a user habit profile based on the shopping data; Determining a category label based on the basic user profile and the user preference profile; Determining a brand label based on the user habit profile and the category label; The information to be pushed is determined based on the category tag and the brand tag.

6. The information push method based on cloud computing and big data according to claim 5, characterized in that: The category label is associated with the tolerance threshold.

7. According to the information push method based on cloud computing and big data in claim 5, the social data at least includes personal information, friend relationship data, dynamic data and interactive data; the behavior data at least includes browsing data, click data and search data; the shopping data at least includes shopping cart data, browsing data and evaluation data.

8. An information push system based on cloud computing and big data, characterized in that: include Acquisition module, used to obtain user data; An information screening module, used to determine the information to be pushed based on the user data; An analysis module is used to determine a time-limit curve based on the information to be pushed and historical data in the cloud platform; wherein the time-limit curve includes multiple time nodes and time-limit parameters corresponding to the time nodes; it is also used to determine the time interval to be pushed based on the time-limit curve and the time-limit threshold; it is also used to determine whether the target moment is in the push time interval; if so, obtain the environmental data of the target moment and the time-limit parameters of the target moment in the time-limit curve, and determine the privacy tolerance parameter based on the environmental data; if not, determine whether the next target moment is in the push time interval; the environmental data includes network type data, location data and timestamp data, and the determination of the privacy tolerance parameter based on the environmental data includes: determining the network type level to which the network type data belongs based on the network type data; determining the location level to which the location data belongs based on the location data; determining the scene parameter based on the network type level and the location level; determining whether the timestamp data is in a preset time interval, and determining the weight parameter based on the judgment result; wherein the preset time interval does not completely overlap with the push time interval; determining the privacy tolerance parameter based on the scene parameter and the weight parameter; A policy module is used to determine a push strategy based on the privacy tolerance parameter and the timeliness parameter; the push strategy determined based on the privacy tolerance parameter and the timeliness parameter includes: determining a tolerance score based on the ratio of the privacy tolerance parameter to a tolerance threshold; determining a timeliness score based on the ratio of the timeliness parameter to the timeliness threshold; determining a push score based on the product of the tolerance score and the timeliness score; in response to the push score being greater than or equal to a first score, determining the push strategy to be a high push priority and multi-platform push, wherein the multi-platform push includes a combination of SMS push, phone push and application push; in response to the push score being greater than or equal to a second score, determining the push strategy to be a medium push priority and a single platform push, wherein the single platform push includes SMS push, phone push or application push; in response to the push score being greater than or equal to a third score, determining the push strategy to be a no push strategy.

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