State analysis method and device, computer device and storage medium

Abstract

Embodiments of the present application disclose a state analysis method and device, computer equipment and a storage medium. The method comprises: obtaining attribute information of M social accounts used by a target object in social activities, the attribute information of any social account comprising: target attribute states of any social account in each attribute dimension of N attribute dimensions; detecting the qualification of each social account in each attribute dimension based on the target attribute states of each social account in each attribute dimension, to obtain the qualification detection result of each social account; obtaining state score values of each attribute dimension, and calculating a state measurement value of the target object according to the qualification detection result of each social account and the state score values of each attribute dimension; and analyzing a target activity state of the target object in the social activities based on the state measurement value of the target object. The embodiments of the present application can improve the state analysis efficiency, effectively save time and reduce memory occupation.

Description

Technical Field

[0001] This application relates to the field of Internet technology, specifically to the field of state analysis technology, and in particular to a state analysis method, a state analysis device, a computer device, and a computer storage medium. Background Technology

[0002] With the development of internet technology, more and more entities (such as users) are participating in online social activities. These online social activities refer to social events held online, such as electronic resource package giveaways and online raffles. During these activities, it's typically necessary to analyze and determine the entity's activity status to determine subsequent business processes. Currently, the common approach is to first enumerate (or exhaustively search) all possible combinations of social accounts and attributes across various dimensions, then match the entity's actual activity information with these combinations to determine its activity status. This method not only consumes a significant amount of time, resulting in low efficiency, but also consumes a large amount of memory due to the need to exhaustively list all possible combinations. Summary of the Invention

[0003] This application provides a state analysis method, apparatus, computer device, and storage medium, which can improve state analysis efficiency, effectively save time, and reduce storage usage.

[0004] On one hand, embodiments of this application provide a state analysis method, the method comprising:

[0005] Obtain the attribute information of M social accounts used by the target object in social activities. The attribute information of any social account among the M social accounts includes: the target attribute state of any social account under each of the N attribute dimensions; where M and N are both positive integers.

[0006] Based on the target attribute status of each social account under each attribute dimension, the eligibility of each social account for bonus points under each attribute dimension is detected, and the eligibility detection result of each social account is obtained.

[0007] Obtain the status score for each attribute dimension, and calculate the status measurement value of the target object based on the qualification detection results of each social account and the status score for each attribute dimension;

[0008] Based on the state measurement value of the target object, the target activity state of the target object in the social activity is analyzed.

[0009] On the other hand, embodiments of this application provide a state analysis apparatus, the apparatus comprising:

[0010] The acquisition unit is used to acquire attribute information of M social accounts used by the target object in social activities. The attribute information of any social account among the M social accounts includes: the target attribute state of any social account under each of the N attribute dimensions; where M and N are both positive integers.

[0011] The analysis unit is used to detect the bonus eligibility of each social account under each attribute dimension based on the target attribute status of each social account under each attribute dimension, and obtain the eligibility detection result of each social account.

[0012] The acquisition unit is also used to acquire the state score value of each attribute dimension;

[0013] The analysis unit is also used to calculate the state measurement value of the target object based on the qualification detection results of each social account and the state score values ​​of each attribute dimension;

[0014] The analysis unit is also used to analyze the target activity state of the target object in the social activity based on the state measurement value of the target object.

[0015] On the other hand, embodiments of this application provide a computer device, the computer device including an input interface and an output interface, and the computer device further includes:

[0016] A processor, adapted to implement one or more instructions; and,

[0017] A computer storage medium storing one or more instructions adapted for loading by the processor and executing the following steps:

[0018] Obtain the attribute information of M social accounts used by the target object in social activities. The attribute information of any social account among the M social accounts includes: the target attribute state of any social account under each of the N attribute dimensions; where M and N are both positive integers.

[0019] Based on the target attribute status of each social account under each attribute dimension, the eligibility of each social account for bonus points under each attribute dimension is detected, and the eligibility detection result of each social account is obtained.

[0020] Obtain the status score for each attribute dimension, and calculate the status measurement value of the target object based on the qualification detection results of each social account and the status score for each attribute dimension;

[0021] Based on the state measurement value of the target object, the target activity state of the target object in the social activity is analyzed.

[0022] On the other hand, embodiments of this application provide a computer storage medium storing one or more instructions, which are adapted to be loaded by a processor and executed as follows:

[0023] Obtain the attribute information of M social accounts used by the target object in social activities. The attribute information of any social account among the M social accounts includes: the target attribute state of any social account under each of the N attribute dimensions; where M and N are both positive integers.

[0024] Based on the target attribute status of each social account under each attribute dimension, the eligibility of each social account for bonus points under each attribute dimension is detected, and the eligibility detection result of each social account is obtained.

[0025] Obtain the status score for each attribute dimension, and calculate the status measurement value of the target object based on the qualification detection results of each social account and the status score for each attribute dimension;

[0026] Based on the state measurement value of the target object, the target activity state of the target object in the social activity is analyzed.

[0027] On the other hand, embodiments of this application also provide a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in various alternative embodiments of the above-described state analysis method.

[0028] This application embodiment first obtains the target attribute status of each social account used by the target object in social activities, under each of the N attribute dimensions; and based on the target attribute status of each social account under each attribute dimension, detects the bonus qualification of each social account under each attribute dimension. Then, based on the qualification detection results of each social account and the status bonus value of each attribute dimension, the state measurement value of the target object can be calculated, thereby analyzing the target activity state of the target object in social activities based on the state measurement value of the target object. Using this state analysis method can effectively reduce the complexity of state analysis, thereby improving the efficiency of state analysis and saving time; moreover, since it is not necessary to exhaustively enumerate all state combinations, it can not only further save time, but also reduce memory usage. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1a This is a schematic diagram illustrating the interaction between a terminal and a server provided in an embodiment of this application;

[0031] Figure 1b This is a schematic diagram of a blockchain structure provided in an embodiment of this application;

[0032] Figure 2 This is a flowchart illustrating a state analysis method provided in an embodiment of this application;

[0033] Figure 3 This is a flowchart illustrating a state analysis method provided in another embodiment of this application;

[0034] Figure 4a This is a schematic diagram illustrating the analysis of target activity status based on state measurement values, provided in an embodiment of this application.

[0035] Figure 4b This is a schematic diagram illustrating another method for analyzing the target activity state based on state measurement values, provided in an embodiment of this application.

[0036] Figure 5a This is a schematic diagram of an interface content provided in an embodiment of this application;

[0037] Figure 5b This is a schematic diagram of another interface content provided in an embodiment of this application;

[0038] Figure 5c This is a schematic diagram of another interface content provided in an embodiment of this application;

[0039] Figure 5d This is a schematic diagram of another interface content provided in an embodiment of this application;

[0040] Figure 5e This is a schematic diagram of a target activity interface provided in an embodiment of this application;

[0041] Figure 6 This is a schematic diagram of the structure of a state analysis device provided in an embodiment of this application;

[0042] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0043] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0044] In this embodiment, the social activities mentioned below refer to online social activities, such as electronic resource package distribution activities, online lottery activities, knowledge quiz activities, etc. In practical applications, N attribute dimensions can be configured for social activities according to the actual business needs of the social activities, so that the activity status of each object participating in the social activities can be analyzed from N attribute dimensions, where N is a positive integer. For example, when the social activity is an electronic resource package distribution activity, the N attribute dimensions configured for the social activity may include, but are not limited to: whether the participant is eligible, whether they possess an electronic resource package, whether they have received an electronic resource package, etc. As another example, when the social activity is an online lottery activity, the N attribute dimensions configured for the social activity may include, but are not limited to: whether the participant is eligible, whether they have participated in the lottery, whether they have received the prize, etc. As yet another example, when the social activity is a knowledge quiz activity, the N attribute dimensions configured for the social activity may include, but are not limited to: whether the participant has participated in the activity, whether they have achieved the target quiz score, etc. It should be noted that the objects mentioned later in the embodiments of this application may be, for example, a single user participating in a social activity, or a group of users participating in a social activity, and there is no limitation thereto. Here, a user group refers to a group of users consisting of at least two users who participate in social activities collaboratively.

[0045] For any attribute dimension involved in a social activity, two attribute states can be configured for that attribute dimension based on actual business needs. For example, for the attribute dimension of eligibility, "eligible" and "uneligible" can be configured; for the attribute dimension of whether or not one possesses an electronic resource package, "possesses an electronic resource package" and "does not possess an electronic resource package" can be configured; for the attribute dimension of whether or not one has received an electronic resource package, "has not received an electronic resource package" and "has received an electronic resource package" can be configured; for the attribute dimension of whether or not one has achieved the target quiz score, "achieved the target quiz score" and "did not achieve the target quiz score" can be configured, and so on.

[0046] Research indicates that attribute states across different attribute dimensions often exhibit dependency relationships. This dependency means that the specific value of an attribute state in one attribute dimension can influence the specific value of the attribute state in another attribute dimension. For example, considering the attribute dimensions of eligibility and possession of an electronic resource package: when the specific value of the eligibility attribute state is "uneligible," the specific value of the electronic resource package possession attribute state will definitely be "not possessed." Conversely, when the specific value of the eligibility attribute state is "eligible," the specific value of the electronic resource package possession attribute state may be either "not possessed" or "possessed." Therefore, the specific value of the eligibility attribute state can influence the specific value of the possession of an electronic resource package attribute state; thus, it can be stated that there is a dependency relationship between the eligibility attribute state and the possession of an electronic resource package attribute state.

[0047] Based on these research findings, this application takes social activities as the activity for claiming electronic resource packs, and further studies the case where social activities are configured with two attribute dimensions (attribute status under the attribute dimension of eligibility, and attribute dimension of whether one possesses an electronic resource pack):

[0048] First, assume that social activities have at least the four activity states shown in Table 1:

[0049] Table 1

[0050]

[0051] Secondly, based on the dependency relationships between the attribute states under the two attribute dimensions configured for social activities, assuming "Eligible Status" adds 10 points, "Eligible Status" adds no points, "Opened E-Resource Package Status" adds 1 point, and "Unopened E-Resource Package Status" adds no points, then, based on the different numbers of social accounts used by the target object in the social activities, the total score involved in each of the four activity states is calculated, resulting in the calculation results shown in Table 2:

[0052] Table 2

[0053]

[0054] As can be seen from Tables 1 and 2 above, when a social activity is configured with multiple attribute dimensions, different counting units can be used to assign status bonuses to each attribute dimension based on the dependencies between attribute states under different attribute dimensions. This results in different counting units for the status bonuses of each attribute dimension. The so-called counting unit refers to a numerical unit of measurement, such as ones, tens, hundreds, thousands, etc. Then, based on the status bonuses of each attribute dimension and whether each social account used by the target object in the social activity is eligible for bonuses under each attribute dimension, the total score of the target object in the social activity is calculated. Since the counting unit for the status bonuses of each attribute dimension is different, different digits of the total score obtained using this calculation method can correspond to different attribute dimensions. This allows for the analysis and processing of the total score digit by digit, from left to right (or from high to low), thereby quickly analyzing the target object's target activity state in the social activity. Here, digit refers to the position of each number in a number, such as ones, tens, hundreds, thousands, etc.

[0055] Similarly, when a social activity is configured with an attribute dimension, a status bonus value can be assigned to this attribute dimension based on business needs or experience. The target object's total score can be calculated based on whether each social account used by the target object in the social activity is eligible for bonus points under this attribute dimension, as well as the status bonus value of this attribute dimension. Then, the target object's target activity status in the social activity can be analyzed based on the total score.

[0056] Based on the above description, this application proposes a state analysis method to quickly analyze the target activity state of a target object in a social activity. Specifically, the general principle of this state analysis method is as follows: First, the attribute information of M social accounts used by the target object in the social activity can be obtained, where M is a positive integer; the attribute information of any social account may include: the target attribute state of any social account under each of the N attribute dimensions. Second, based on the target attribute state of each social account used by the target object under each attribute dimension, the bonus qualification of each social account under each attribute dimension can be detected. Then, based on the qualification detection result of each social account and the state bonus value of each attribute dimension, the state measurement value (i.e., the total score) of the target object can be calculated. Finally, based on the state measurement value of the target object, the target activity state of the target object in the social activity can be analyzed.

[0057] In its implementation, this state analysis method can be executed by a computer device, which can be a terminal or a server. The terminal can include, but is not limited to, smartphones, tablets, laptops, desktop computers, smart TVs, smartwatches, etc.; various clients (or applications, APPs) can be installed and run on the terminal, such as game clients, social clients, video clients, music clients, etc. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms, etc.

[0058] It should be understood that, ① if the computer device is the terminal, then when it is necessary to analyze the target activity state of a target object in a social activity, the server can send the attribute information of each social account used by the target object in the social activity to the computer device (i.e., the terminal), so that the computer device can use this state analysis method and perform state analysis on the target object based on the received attribute information of each social account, thereby determining the target activity state of the target object in the social activity, such as... Figure 1a As shown. ② In other embodiments, the state analysis method can also be executed by any client running within the terminal (such as a social client or a video client); or, the state analysis method can also be executed jointly by the terminal and the server, and this application embodiment does not limit this. ③ The computer device mentioned in the embodiments of this application can be located outside the blockchain network or inside the blockchain network, and this is not limited.

[0059] A blockchain network is a network composed of a peer-to-peer (P2P) network and a blockchain. Blockchain refers to a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, it is a decentralized database, a chain of data blocks (or blocks) linked together using cryptographic methods. See also... Figure 1bAs shown, a blockchain can consist of multiple blocks. The genesis block includes a block header and a block body. The block header stores input information feature values, version number, timestamp, and difficulty value, while the block body stores the input information. The next block after the genesis block takes the genesis block as its parent block. The next block also includes a block header and a block body. The block header stores the input information feature values ​​of the current block, the block header feature values ​​of the parent block, version number, timestamp, and difficulty value, and so on. This ensures that the block data stored in each block of the blockchain is related to the block data stored in the parent block, guaranteeing the security of the input information in the blocks.

[0060] Based on the above description, the following will combine Figure 2 The flowchart shown further illustrates the specific implementation of the state analysis method proposed in the embodiments of this application. Figure 2 The state analysis method shown can be executed by a computer device (such as a terminal or server), or by both a terminal and a server; for ease of explanation, the following description will use the execution of this state analysis method by a computer device as an example. Please refer to [link to relevant documentation]. Figure 2 The state analysis method may include the following steps S201-S205:

[0061] S201, Obtain the attribute information of the M social accounts used by the target object in social activities.

[0062] In this embodiment, the target object can be any user among all users participating in the social activity, or any user group among all user groups participating in the social activity; there is no limitation in this regard. The target object can use M social accounts to participate in the social activity, where M is a positive integer. During the process of the target object participating in the social activity using any social account, attribute information of that social account can be generated. This attribute information may include the target attribute status of the social account under each of the N attribute dimensions. It should be understood that the target attribute status of any social account under different attribute dimensions is different; for example, the target attribute status of any social account under the attribute dimension of eligibility can be "eligible status," and the target attribute status under the attribute dimension of whether or not an electronic resource package has been received can be "electronic resource package received status."

[0063] Specifically, the target attribute status of any social media account under each attribute dimension is determined based on the activities performed by that account during the target user's participation in the social activity. If the target user's activities through that social media account meet the eligibility requirements, then that social media account is determined to be in an "eligible state" under the eligibility attribute dimension; conversely, if the target user's activities through that social media account do not meet the eligibility requirements, then that social media account is determined to be in an "uneligible state" under the eligibility attribute dimension.

[0064] S202, based on the target attribute status of each social account under each attribute dimension, detect the bonus qualification of each social account under each attribute dimension, and obtain the qualification detection result of each social account.

[0065] As mentioned above, any attribute dimension can have two attribute states. Therefore, the two attribute states under any attribute dimension can be pre-divided into valid and invalid states according to business needs, so that the attribute states of any attribute dimension include valid and invalid states. Specifically, a valid state of any attribute dimension refers to an attribute state that allows adding points to a social media account under that attribute dimension; correspondingly, an invalid state of any attribute dimension refers to an attribute state that prohibits adding points to a social media account under that attribute dimension.

[0066] For example, regarding the qualification attribute dimension, "qualified" can be classified as a valid state, and "unqualified" as an invalid state. Therefore, when any social media account is in the "qualified" state under the qualification attribute dimension, it is permissible to apply bonus points to that account under the qualification attribute dimension; conversely, when any social media account is in the "unqualified" state, it is prohibited from applying bonus points to that account under the qualification attribute dimension. Similarly, regarding the possession of an e-resource package attribute dimension, "possessing an e-resource package" can be classified as a valid state, and "not possessing an e-resource package" as an invalid state. Furthermore, regarding the receipt of an e-resource package attribute dimension, "not receiving an e-resource package" can be classified as a valid state, and "not receiving an e-resource package" as an invalid state, and so on.

[0067] Based on this, in the specific implementation of step S202, the computer device can traverse the M social accounts used by the target object in social activities. For the currently traversed social account, it can determine whether the target attribute state of the current social account under any attribute dimension is a valid state under that attribute dimension to detect the current social account's eligibility for bonus points under any attribute dimension, thereby obtaining the eligibility detection result of the current social account. The eligibility detection result of the current social account can be used to indicate whether the current social account has eligibility for bonus points under each attribute dimension. Bonus point eligibility refers to the qualification to possess a status bonus value under the corresponding attribute dimension. Specifically, if the target attribute state of the current social account under any attribute dimension is a valid state under that attribute dimension, then the current social account is determined to have eligibility for bonus points under that attribute dimension; otherwise, the current social account is determined not to have eligibility for bonus points under that attribute dimension. In other words, when the target attribute state of any social account under any attribute dimension is a valid state under that attribute dimension, that social account has eligibility for bonus points under that attribute dimension.

[0068] Taking eligibility as an example, if the target attribute status of the current social media account under the eligibility attribute dimension is "eligible," then it can be determined that the computer device allows the current social media account to receive bonus points under the eligibility attribute dimension, thus confirming that the current social media account is eligible for bonus points under the eligibility attribute dimension. Conversely, if the target attribute status of the current social media account under the eligibility attribute dimension is "uneligible," then it can be determined that the computer device prohibits the current social media account from receiving bonus points under the eligibility attribute dimension, thus confirming that the current social media account is not eligible for bonus points under the eligibility attribute dimension.

[0069] S203, obtain the status score value of each attribute dimension.

[0070] In one specific implementation, if N equals 1, meaning the social activity is configured with an attribute dimension, then step S203 can be implemented by assigning a status score to the attribute dimension based on business needs or experience. For example, a unit value can be used as the status score for the attribute dimension; the unit value refers to the basic unit in the numerical space, and the unit value is the value "1". Alternatively, a preset value other than the unit value can be used as the status score for the attribute dimension, such as setting the status score for the attribute dimension to the value "2" or the value "0.5", etc.

[0071] In another specific implementation, if the value of N is greater than 1, meaning the social activity is configured with multiple attribute dimensions, then the attribute state under any of the N attribute dimensions has a dependency relationship with the attribute states under one or more other attribute dimensions; where other attribute dimensions refer to attribute dimensions other than any of the N attribute dimensions. Therefore, the specific implementation of step S203 can be:

[0072] First, the weight priority of each attribute dimension can be determined based on the dependencies between attribute states under different attribute dimensions. Specifically, in any dependency relationship between two attribute dimensions, the weight priority of the attribute dimension corresponding to the dependent attribute state is higher than the weight priority of the other attribute dimension. For example, regarding the attribute dimensions of eligibility and possession of an electronic resource package, the specific value of the attribute state under the eligibility attribute dimension can affect the specific value of the attribute state under the possession of an electronic resource package attribute dimension. Therefore, it can be determined that the attribute state under the possession of an electronic resource package attribute dimension depends on the attribute state under the eligibility attribute dimension. Thus, in both the eligibility and possession of an electronic resource package attribute dimensions, the dependent attribute state is the attribute state under the eligibility attribute dimension; therefore, the weight priority of the eligibility attribute dimension is higher than the weight priority of the possession of an electronic resource package attribute dimension.

[0073] Secondly, following the principle that the weight priority and the unit level of the counting unit are positively correlated, the corresponding counting unit can be determined for each attribute dimension according to its weight priority, so that the counting units for different attribute dimensions are different. For example, if a social activity is configured with two attribute dimensions: eligibility and eligibility for receiving an electronic resource package, then since the weight priority of the eligibility attribute dimension is higher than that of the eligibility attribute dimension, the counting unit for the eligibility attribute dimension can be determined as ten, and the counting unit for the eligibility attribute dimension can be determined as one (one). For example, if a social activity is configured with three attribute dimensions: eligibility, possession of an electronic resource package, and receipt of an electronic resource package, then since the weight priority of the eligibility attribute dimension is higher than that of possession of an electronic resource package, and the weight priority of possession of an electronic resource package is higher than that of receipt of an electronic resource package, the counting unit for the eligibility attribute dimension can be determined as 100, the counting unit for possession of an electronic resource package can be determined as 10, and the counting unit for receipt of an electronic resource package can be determined as 1 (one).

[0074] The status score for each attribute dimension is generated using a unit value and a counting unit for each attribute dimension. For example, continuing the previous example, suppose a social activity is configured with three attribute dimensions: eligibility, possession of an electronic resource package, and receipt of an electronic resource package. Since the counting unit for the eligibility attribute dimension is 100 and the unit value is 1, the status score for the eligibility attribute dimension can be 100; since the counting unit for the possession of an electronic resource package is 10, the status score for the possession of an electronic resource package can be 10; and since the counting unit for the receipt of an electronic resource package is one, the status score for the receipt of an electronic resource package can be 1. It should be understood that this embodiment is only an example of generating status scores using a unit value. In other embodiments, other values ​​besides the unit value can also be used to generate the status scores for each attribute dimension, and this is not limited.

[0075] It should be noted that the execution order of steps S202 and S203 is not limited in the embodiments of this application. That is, in specific implementation, the computer device may execute step S202 first and then step S203; or it may execute step S203 first and then step S202; or it may execute steps S202 and S203 simultaneously, and so on.

[0076] S204. Calculate the state measurement value of the target object based on the eligibility test results of each social account and the state score of each attribute dimension.

[0077] In the specific implementation process, a reference measurement value for each social media account can be calculated first based on the eligibility verification results and status scores of each attribute dimension. Then, the reference measurement values ​​of each social media account are summed to obtain the status measurement value of the target object. The eligibility verification results of each social media account can be used to indicate whether each account is eligible for bonus points in each attribute dimension. Therefore, the specific implementation method for calculating the reference measurement value of each social media account based on its eligibility verification results and status scores of each attribute dimension can be as follows:

[0078] Iterate through M social media accounts and determine the current social media account being iterated over. Based on the eligibility check results of the current social media account, select one or more valid attribute dimensions from N attribute dimensions. Valid attribute dimensions refer to the attribute dimensions corresponding to which the current social media account is eligible for bonus points. Then, sum the state scores of each selected valid attribute dimension to obtain the reference evaluation value of the current social media account. For example, continuing with the previous example, suppose a social activity is configured with three attribute dimensions: eligibility, possession of an e-resource package, and receipt of an e-resource package. If the current social account is eligible for bonus points under both the eligibility and possession of an e-resource package attributes, but not under the receipt of an e-resource package attribute, then the valid attribute dimensions include eligibility and possession of an e-resource package. Then, the status bonus value (100) for eligibility and the status bonus value (10) for possession of an e-resource package can be summed to obtain a reference measurement value of 110 for the current social account.

[0079] S205, Based on the state measurement value of the target object, analyze the target activity state of the target object in social activities.

[0080] In practical implementation, if the number of attribute dimensions configured for a social activity is one, i.e., N equals 1, the main principle of state analysis based on state measurement values ​​is as follows: based on the state measurement value and the state score value of that attribute dimension, the number of social accounts eligible for a score under that attribute dimension can be determined; then, based on the relationship between the determined number of accounts and the total number of social accounts used by the target object (i.e., M), the target activity state of the target object in the social activity can be analyzed. If the number of attribute dimensions configured for a social activity is multiple, i.e., N is greater than 1, the main principle of state analysis based on state measurement values ​​is as follows: the state measurement value can be analyzed digit by digit in order from left to right (or from high to low), thereby quickly analyzing and obtaining the target activity state of the target object in the social activity.

[0081] This application embodiment first obtains the target attribute status of each social account used by the target object in social activities, under each of the N attribute dimensions; and based on the target attribute status of each social account under each attribute dimension, detects the bonus qualification of each social account under each attribute dimension. Then, based on the qualification detection results of each social account and the status bonus value of each attribute dimension, the state measurement value of the target object can be calculated, thereby analyzing the target activity state of the target object in social activities based on the state measurement value of the target object. Using this state analysis method can effectively reduce the complexity of state analysis, thereby improving the efficiency of state analysis and saving time; moreover, since it is not necessary to exhaustively enumerate all state combinations, it can not only further save time, but also reduce memory usage.

[0082] Please see Figure 3 This is a flowchart illustrating another state analysis method provided in an embodiment of this application. This state analysis method can be executed by a computer device (such as a terminal or server), or by both a terminal and a server; for ease of explanation, the following description will use the execution of this state analysis method by a computer device as an example. Please refer to... Figure 3 The state analysis method may include the following steps S301-S308:

[0083] S301, Obtain the attribute information of M social accounts used by the target object in social activities. The attribute information of any social account includes: the target attribute status of any social account under each of the N attribute dimensions.

[0084] S302, based on the target attribute status of each social account under each attribute dimension, detect the bonus qualification of each social account under each attribute dimension, and obtain the qualification detection result of each social account.

[0085] S303. Determine the weight priorities of each attribute dimension according to the dependency relationships between the attribute states under different attribute dimensions. Among them, in the two attribute dimensions involved in any dependency relationship, the weight priority of the attribute dimension corresponding to the dependent attribute state is higher than that of the other attribute dimension.

[0086] S304. According to the principle that the weight priority is positively correlated with the unit level of the counting unit, determine the corresponding counting units for each attribute dimension according to the weight priorities of each attribute dimension.

[0087] Specifically, first arrange the N attribute dimensions in order according to the weight priorities of each attribute dimension from low to high. Then, determine the counting unit of the attribute dimension in the first place as the reference counting unit. The reference counting unit can be set according to business requirements. For example, the reference counting unit can be set to one (1) or ten, etc. Then, for the attribute dimension in the nth position, calculate the counting unit of the attribute dimension in the nth position according to the counting unit of the attribute dimension in the (n - 1)th position and the preset rate. Where n ∈ [2, N]; and the preset rate can be set according to business requirements. For example, the preset rate can be set to 10 or 100, etc. The so-called rate refers to the ratio between the value represented by the counting unit with a higher unit level and the value represented by the counting unit with a lower unit level.

[0088] Illustrate with an example: Let the reference counting unit be one (1) and the preset rate be 10. If the social activity is configured with 3 attribute dimensions: the attribute dimension of whether there is eligibility, the attribute dimension of whether there is an electronic resource package, and the attribute dimension of whether the electronic resource package is received; then after arranging these 3 attribute dimensions in order according to the weight priorities, the attribute dimension in the first place is the attribute dimension of whether the electronic resource package is received. Therefore, the counting unit of the attribute dimension of whether the electronic resource package is received can be determined as one. Since the attribute dimension in the 2nd position is the attribute dimension of whether there is an electronic resource package, the counting unit of the attribute dimension of whether there is an electronic resource package can be calculated as ten by using the preset rate (10) and the counting unit (one) of the attribute dimension in the first place (the attribute dimension of whether the electronic resource package is received). Since the attribute dimension in the 3rd position is the attribute dimension of whether there is eligibility, the counting unit of the attribute dimension of whether there is eligibility can be calculated as one hundred by using the preset rate (10) and the counting unit (ten) of the attribute dimension in the 2nd position (the attribute dimension of whether there is an electronic resource package).

[0089] S305. Generate the status score values of each attribute dimension by using the unit value and the counting units of each attribute dimension.

[0090] S306. Calculate the state measurement value of the target object based on the eligibility test results of each social account and the state score of each attribute dimension.

[0091] S307, Based on the state measurement value of the target object, analyze the target activity state of the target object in social activities.

[0092] As mentioned above, in the specific implementation process, the state analysis method adopted by the computer device varies depending on the number of attribute dimensions configured for the social activity. The specific implementation method of step S307 will be described below for two cases: when N is greater than 1 and when N is equal to 1.

[0093] In the first case, N is greater than 1: As mentioned above, different attribute dimensions have different counting units; and the state measurement value calculated through steps S301-S306 can be represented by P digits, with each digit corresponding to an attribute dimension through a corresponding counting unit, and P being a positive integer. For any digit, if there is no social account among the M social accounts that is eligible for bonus points under the attribute dimension corresponding to that digit, the value at that digit is invalid. If there is a social account among the M social accounts that is eligible for bonus points under the attribute dimension corresponding to that digit, the value at that digit is valid, and the value at that digit is equal to the product of the number of social accounts eligible for bonus points under the attribute dimension corresponding to that digit and the state bonus value of the attribute dimension corresponding to that digit. In this case, the specific implementation of step S307 can include the following steps s11-s13:

[0094] s11, select the reference digit from the unselected digits among the P digits in the order of selection from left to right.

[0095] In one specific implementation, the computer device can directly execute step s11 after calculating the state measurement value. In another specific implementation, considering that in practical applications, the attribute information of the M social media accounts used by the target object may contain dirty data; dirty data here refers to attribute information where the target attribute state of the indicated social media account under each attribute dimension has logical conflicts. For example, suppose there are two attribute dimensions: eligibility and eligibility for receiving an e-resource package. If the attribute information of social media account A indicates that the target attribute state of social media account A under the eligibility dimension is "uneligible" and the target attribute state under the e-resource package receipt dimension is "received e-resource package"; then since social media account A is uneligible, there cannot be a "received e-resource package" situation. Therefore, the target attribute state of social media account A indicated by the attribute information of social media account A under each attribute dimension has logical conflicts, and thus the attribute information of social media account A is dirty data.

[0096] When the attribute information of the M social media accounts contains dirty data, the state measurement values ​​calculated through steps S301-S306 will be incorrect, potentially affecting the accuracy of the target activity state analyzed based on the state measurement values ​​through steps s11-s13. Therefore, to improve the accuracy of the target activity state obtained in subsequent analyses, the computer device can first perform a validity check on the state measurement values ​​of the target object after calculating them. If the state measurement value passes the validity check, step s11 is executed; if the state measurement value fails the validity check, the target activity state of the target object in the social activity is determined to be the first activity state. The first activity state indicates that each of the M social media accounts is in an invalid state under each attribute dimension. For example, suppose there are two attribute dimensions: eligibility and eligibility for receiving the electronic resource package; then the first activity state indicates that each of the M social media accounts is in an "uneligible state" under the eligibility attribute dimension and in an "unreceived electronic resource package" state under the eligibility attribute dimension.

[0097] One specific implementation method for validating the status measurement value of a target object is as follows: From the counting units of N attribute dimensions, determine the highest-level counting unit; and using the determined counting unit and a preset value, calculate the valid measurement value. Then, compare whether the status measurement value of the target object is less than the valid measurement value; if the status measurement value is less than the valid measurement value, the status measurement value fails the validity check; if the status measurement value is greater than or equal to the valid measurement value, the status measurement value passes the validity check. For example, suppose the preset value is 1, there are a total of 2 attribute dimensions, and the counting units of each attribute dimension are as follows: the counting unit for the "eligibility" attribute dimension is 10, and the counting unit for the "receiving electronic resource package" attribute dimension is 1 (one); then the highest-level counting unit is 10, and the calculated valid measurement value is 10.

[0098] This verification method allows for the determination of the target activity state as the first activity state when the calculated state measurement value (the value of P) is less than N due to dirty data. Instead of analyzing the target activity state through steps s11-s13, the method directly determines the target activity state as the first activity state, effectively improving the accuracy of the target activity state. For example, if the target activity only uses social account A in a social activity, and since the attribute information of social account A is dirty data, it indicates that the target attribute state of social account A under the "eligibility" attribute dimension is "uneligible" and under the "received electronic resource package" attribute dimension is "received electronic resource package." Therefore, the state measurement value calculated through steps S301-S306 is 1. This verification method allows for the direct determination of the target activity state as the first activity state because the state measurement value is less than the valid measurement value (10), thus avoiding the misjudgment of the target activity state as the fourth activity state mentioned later due to state analysis performed in steps s11-s13.

[0099] Optionally, another specific implementation for validating the state measurement value of the target object can be: checking whether the number of digits (P values) of the state measurement value is equal to N. If not, the state measurement value fails the validity check. If equal, further checking whether there are invalid digits among the remaining digits of the P digits of the state measurement value (excluding the leftmost digit); wherein, an invalid digit satisfies the following condition: the value of the digit to the left of and adjacent to the invalid digit is less than the value of the invalid digit. If such a digit exists, the state measurement value fails the validity check; if not, the state measurement value passes the validity check. For example, suppose N = 2, and the state measurement value is 12, that is, the number of digits of the state measurement value is 2, which is equal to N; however, since the value of the units digit of the state measurement value is greater than the value of the tens digit, the units digit of the state measurement value is an invalid digit, and in this case, the state measurement value fails the validity check.

[0100] This verification method avoids the possibility of incorrect target activity states being analyzed in steps s11-s13 when the calculated state measurement value is erroneous due to dirty data, thus effectively ensuring the accuracy of subsequent steps s11-s13. It should be noted that when using this verification method to validate the state measurement value, if the state measurement value fails the validity check, the step of determining the target activity state as the first activity state can be skipped. Instead, dirty data in the attribute information of the M social activities is calibrated to ensure that the attribute information of each social account is correct after calibration. Then, based on the calibrated attribute information of the social accounts, steps S302-S307 are re-executed to determine the target activity state of the target object in the social activities.

[0101] s12, when there is an associated reference digit for the reference digit, determine the value on the reference digit as the reference value, and determine the value on the reference digit as the reference value.

[0102] The reference digit refers to the digit that is to the right of the reference digit and adjacent to it. For example, if the state measure value is a three-digit number, meaning the state measure value includes three digits: the ones place, the tens place, and the hundreds place; if the reference digit is the hundreds place, then the reference digit is the tens place, which is to the right of the hundreds place and adjacent to it; if the reference digit is the tens place, then the reference digit is the ones place, which is to the right of the tens place and adjacent to it, and so on.

[0103] s13 can analyze the target activity status of the target object in social activities based on at least one of the baseline value and the reference value.

[0104] In the specific implementation process, it can be first determined whether the baseline value is invalid (i.e., the value 0). If the reference value is invalid, the target activity state of the target object in the social activity is determined to be the second activity state; where the second activity state indicates that: m out of the M social accounts are all in a valid state under the first attribute dimension, and m social accounts are all in an invalid state under the second attribute dimension. If the reference value is valid, the baseline value and the reference value are compared; if the baseline value is greater than the reference value, the target activity state is determined to be the third activity state, where the third activity state indicates that: m social accounts are all in a valid state under the first attribute dimension, and some of the m social accounts are in an invalid state under the second attribute dimension.

[0105] If the baseline value is not greater than the reference value, then the reference digit and the rightmost digit among the P digits are matched to check if the reference digit and the rightmost digit are the same, that is, to check if the reference digit is located at the rightmost of the P digits. If the match is successful, it indicates that the reference digit is located at the rightmost of the P digits, and the target activity state can be determined as the fourth activity state. This fourth activity state indicates that m social accounts out of M social accounts are in a valid state under each attribute dimension. If the match fails, it indicates that the reference digit is not located at the rightmost of the P digits, and a new baseline digit can be selected from the P digits, that is, jump back to step s11 to iteratively execute steps s11-s13.

[0106] In the above description, the value of m is less than or equal to M; the first attribute dimension includes the attribute dimension corresponding to the baseline digit and the attribute dimensions corresponding to each digit to the left of the baseline digit; the second attribute dimension refers to the attribute dimension corresponding to the reference digit. For example, let P = 3, and the P digits include: hundreds, tens, and units; where the hundreds digit corresponds to the qualification attribute dimension, the tens digit corresponds to the possession of the electronic resource package attribute dimension, and the units digit corresponds to the receipt of the electronic resource package attribute dimension. Then, if the baseline digit is the hundreds digit, the reference digit is the tens digit; the first attribute dimension includes the qualification attribute dimension; the second attribute dimension includes the possession of the electronic resource package attribute dimension. If the baseline digit is the tens digit, the reference digit is the units digit; the first attribute dimension includes the qualification attribute dimension and the possession of the electronic resource package attribute dimension; the second attribute dimension includes the receipt of the electronic resource package attribute dimension.

[0107] Taking the first attribute dimension as including: eligibility and possession of the electronic resource package; and the second attribute dimension as including: whether the electronic resource package has been received, the specific content indicated by the second, third, and fourth activity states is as follows:

[0108] The second activity status indicator is as follows: m out of the M social media accounts are all in the "eligible" status in the "eligible" attribute dimension, m social media accounts are all in the "possessing electronic resource pack" status in the "possessing electronic resource pack" attribute dimension, and m social media accounts are in the "not claiming electronic resource pack" status in the "not claiming electronic resource pack" attribute dimension.

[0109] The third activity status indicator is as follows: all m social media accounts are in the "eligible" status in the "eligible" attribute dimension; all m social media accounts are in the "possessing electronic resource pack" status in the "possessing electronic resource pack" attribute dimension; and some of the m social media accounts are in the "not claimed electronic resource pack" status in the "claimed electronic resource pack" attribute dimension.

[0110] The fourth activity status indicator is as follows: m social media accounts are all in the "eligible" status in the "eligible" attribute dimension; m social media accounts are all in the "possessing electronic resource pack" status in the "possessing electronic resource pack" attribute dimension; and m social media accounts are all in the "received electronic resource pack" status in the "received electronic resource pack" attribute dimension.

[0111] It should be noted that, in the specific implementation of steps s11-s13, the computer device can also use a dual-pointer method, with pointer A pointing to the reference digit and pointer A+1 pointing to the reference digit, thereby improving the reading efficiency of the reference and reference values, and thus improving the processing efficiency of the entire state analysis process. The following will combine... Figure 4a The flowchart shown illustrates one implementation method for how a computer device uses a two-pointer approach to analyze the target activity state:

[0112] First, the state measurement value can be obtained, and the valid measurement value can be calculated. Second, it can be determined whether the state measurement value is less than the valid measurement value. If it is less, the target activity state is determined to be the first activity state. If it is not less, following the digit order from left to right, assuming pointer A is at the leftmost digit (i.e., the reference digit is the leftmost digit), it can be determined whether the value of pointer A+1 (i.e., the reference value at the reference digit) is equal to 0 (i.e., an invalid value). If it is equal to 0, the target activity state is determined to be the second activity state. If it is not equal to 0, it can be determined whether the value of pointer A (i.e., the reference value at the reference digit) is greater than the value of pointer A+1. If it is greater, the target activity state is determined to be the third activity state. If it is not greater, it can be determined whether pointer A+1 points to the rightmost digit. If it is, the target activity state is determined to be the fourth activity state. If it is not, pointer A is moved to the right, and the process jumps to the step of determining whether the value of pointer A+1 is equal to 0.

[0113] For example, suppose the target uses 2 social media accounts, and their social activities are configured with two attribute dimensions: eligibility and eligibility to receive electronic resource packages. Additionally, let's assume the effective measure value is 10. If the state measure value is 22, since 22 is greater than 10, pointer A can be further set to the tens digit, meaning pointer A's value is the tens digit 2. Since pointer A+1's value is the units digit 2, it can be determined that pointer A+1's value is not equal to 0. At this point, we can further determine if pointer A's value is greater than pointer A+1's value. Since pointer A's value is the tens digit 2, and pointer A+1's value is the units digit 2, it can be determined that pointer A's value is equal to pointer A+1's value, meaning pointer A's value is not greater than pointer A+1's value. At this point, it can be further determined whether pointer A+1 points to the rightmost digit. Since the reference digit pointed to by pointer A+1 at this time is the units digit, which is the rightmost digit, the target activity state can be determined to be the fourth activity state. This fourth activity state is used to indicate that: the two social media accounts used by the target object are both in the "eligible" state under the eligibility attribute dimension; and both are in the "received electronic resource package" state under the eligibility attribute dimension.

[0114] In the second case, N equals 1: Since the attribute state under the attribute dimension includes valid and invalid states; when any social account's target attribute state under the attribute dimension is valid, any social account is eligible for bonus points under the attribute dimension. Therefore, the state measurement value calculated through steps S301-S306 is as follows: When there are no social accounts among the M social accounts that are eligible for bonus points under the attribute dimension, the state measurement value is invalid; when there are social accounts among the M social accounts that are eligible for bonus points under the attribute dimension, the state measurement value is equal to the product of the number of social accounts eligible for bonus points under the attribute dimension and the state bonus value of the attribute dimension. Therefore, in this specific implementation, the specific implementation method of step S307 may include the following steps:

[0115] First, based on the status measurement value and the status bonus value of the attribute dimension, the number of social media accounts eligible for bonus points under the attribute dimension can be determined. Specifically, if the status bonus value of the attribute dimension is a unit value, the number of accounts is equal to the status measurement value; if the status bonus value of the attribute dimension is not a unit value, the number of accounts is equal to the ratio between the status measurement value and the status bonus value. After determining the number of accounts, it can be determined whether the number of accounts is less than or equal to M. If the number of accounts is less than M, the target activity state is determined to be the fifth activity state; the fifth activity state indicates that some of the M social media accounts are in an invalid state under the attribute dimension. If the number of accounts is equal to M, the target activity state is determined to be the sixth activity state; the sixth activity state indicates that all of the M social media accounts are in a valid state under the attribute dimension.

[0116] Optionally, to improve the accuracy of the target activity status, before determining the number of social accounts eligible for bonus points in the attribute dimension based on the status measurement value and the status bonus value of the attribute dimension, the computer device may first determine a measurement value threshold and check whether the status measurement value is less than or equal to the measurement value threshold. When the status measurement value is less than or equal to the measurement value threshold, the target activity status of the target object in the social activity is determined to be the seventh activity status; the seventh activity status indicates that each of the M social accounts is in an invalid state in the attribute dimension. When the status measurement value is greater than the measurement value threshold, the step of determining the number of social accounts eligible for bonus points in the attribute dimension based on the status measurement value and the status bonus value of the attribute dimension is then executed; in this case, the specific implementation process of step S307 can be found in [reference needed]. Figure 4b As shown:

[0117] For example, suppose the target audience uses 2 social media accounts, i.e., M = 2; and suppose social activities are configured with one attribute dimension: eligibility, with a status score of 1; additionally, the threshold value for the measurement value can be set to 0. See then... Figure 4b As shown: If the state measure value is 2, then since the state measure value (2) is greater than the measure value threshold (0), the number of social accounts that are eligible for bonus points under the attribute dimension can be further determined to be 2 based on the state bonus value and the state measure value. Since the number of accounts (2) is equal to M, the target activity state can be determined to be the sixth activity state, which indicates that each social account of the target object is in the "eligible state" under the attribute dimension of eligibility.

[0118] In this embodiment, when a social activity is configured with multiple attribute dimensions, a status score can be assigned to each attribute weight based on the dependency relationship between attribute states under different attribute dimensions. Furthermore, based on the target attribute state of each social account of the target object under each attribute dimension, the eligibility for scoring under each attribute dimension is detected. Then, using a scoring weighting method, the status measurement value of the target object is calculated based on the eligibility detection result of each social account and the status score value of each attribute dimension. This allows for rapid analysis of the target activity state of the target object in the social activity by performing digit-by-digit matching analysis from left to right on the status measurement value. This status analysis method effectively reduces the complexity of status analysis, thereby improving efficiency and saving time. Moreover, since it does not require exhaustively enumerating all state combinations, it not only saves time but also reduces memory usage. Additionally, this status analysis method ensures that the time complexity remains O(N) as the number of social accounts and attribute dimensions increases, resulting in high efficiency and optimized code structure.

[0119] It should be noted that, in practical applications, through the above... Figure 2 or Figure 3The method embodiment shown illustrates that after analyzing the target activity state of the target object in social activities, the computer device can further associate and store the target activity state with the target object's object identifier (such as user identifier, group identifier, etc.) in the blockchain to prevent the target activity state from being tampered with. Specifically, the computer device can add the target activity state and the target object's object identifier to the block body of the target block, and perform a hash operation on the data in the block body to obtain a Merkel hash value. Next, a random number can be generated using a random algorithm, and the calculated Merkel hash value, the random number, the version number, the previous block hash value, the current timestamp, and the current difficulty value are used to form the block header of the target block. Here, the version number refers to the version information of the relevant block protocol in the blockchain; the previous block hash value refers to the feature value of the previous block's header; the current timestamp refers to the system time when the block header is formed; and the current difficulty value refers to the calculated difficulty value, which is a fixed value within a fixed time period and is re-determined after the fixed time period expires. Then, a feature-value algorithm (such as SHA256) can be used to perform one or more hash operations on the content contained in the block header to obtain the feature value of the block header of the target block. The number of hash operations here can be determined according to the computational difficulty; the greater the computational difficulty, the more hash operations are performed. After obtaining the target block based on the above steps, the target block can be broadcast to various consensus nodes in the blockchain network for consensus processing. After passing the consensus processing, the target block is added to the blockchain.

[0120] Furthermore, after obtaining the target activity status, the computer device can also perform subsequent business processing based on the target activity status. For example, based on the target activity status, the corresponding activity interface can be output on the user terminal screen; specifically, the target interface content corresponding to the target activity status can be determined based on the correspondence between the activity status and the interface content, and then the target activity interface can be generated based on the target interface content, thereby outputting the target activity interface on the user terminal screen. For example, consider a target object using one or two social media accounts to participate in an electronic resource package redemption activity, where the redemption activity involves the four activity statuses shown in Table 3:

[0121] Table 3

[0122]

[0123] For state a, the corresponding interface content may include at least: a first notification message 51 indicating that the recipient is ineligible, and a second notification message 52 indicating that the recipient cannot receive the electronic resource package; for example, the first notification message 51 could be... Figure 5a The second prompt message 52 in any of the interface content shown can be "No eligibility for distribution". Figure 5aThe message "We regret that you were unable to participate in the sharing" appears in any of the interface content shown.

[0124] For state b, the corresponding interface content may include at least: a first resource claiming component 53 for claiming electronic resource packages, so that the object can trigger the first resource claiming component 53 to perform electronic resource package claiming operations for all social accounts. For example, the first resource claiming component 53 may be... Figure 5b The "Claim a Share of Red Envelopes" component in any of the interface contents shown.

[0125] For state c, the corresponding interface content may at least include: a resource withdrawal component 54 for withdrawing the electronic resource package, and resource quantity information 55 of the already claimed electronic resource package, so that the object can perform a resource withdrawal operation by triggering the resource withdrawal component 54. The withdrawal of the electronic resource package mentioned here refers to the operation of withdrawing the electronic resource package from the default account to another account; the default account refers to the account that is cached by default when claiming the electronic resource package. For example, the resource withdrawal component 54 could be... Figure 5c The "Withdraw" component in any of the interface content shown; resource quantity information 55 can be... Figure 5c The content of any of the interfaces shown reads "A total of 40.23 yuan was distributed".

[0126] For state d, the corresponding interface content may include at least: a second resource claiming component 56 for continuing to claim the electronic resource package, so that the object can trigger the second resource claiming component 56 to perform the electronic resource package claiming operation for the social media account that has not yet claimed the electronic resource package. For example, the second resource claiming component 56 may be... Figure 5d The "Continue to claim" component in any of the interface contents shown.

[0127] Based on the descriptions of the various states and corresponding interface content above, taking the example of the target object using two social media accounts to participate in the electronic resource package redemption activity: if the target activity state is state a, then according to Figure 5a The interface content shown on the left is generated and output. Figure 5e The first image shows the target activity interface. If the target activity state is state b, then it can be determined according to... Figure 5b The interface content shown on the left is generated and output. Figure 5e The second image shows the target activity interface. If the target activity state is state c, then it can be determined according to... Figure 5c The interface content shown on the left is generated and output. Figure 5e The third image shows the target activity interface. If the target activity state is state d, then it can be determined according to... Figure 5d Generate and output the interface content shown. Figure 5eThe fourth image shows the target activity interface. It should be noted that if the target object is in state b, and is displayed on the user terminal screen... Figure 5e Following the second image, the target object triggers the first resource claiming component 53 to perform the electronic resource package claiming operation for all social accounts. If any social account fails to claim the electronic resource package, the target object's target activity status can be updated to status d; if all social accounts successfully claim the electronic resource package, the target object's target activity status can be updated to status c.

[0128] Based on the description of the above-described state analysis method embodiments, this application also discloses a state analysis apparatus, which may be a computer program (including program code) running on a computer device. This state analysis apparatus can execute... Figure 2 or Figure 3 The method shown. Please refer to [link / reference]. Figure 6 The state analysis device can operate the following units:

[0129] The acquisition unit 601 is used to acquire attribute information of M social accounts used by the target object in social activities. The attribute information of any social account among the M social accounts includes: the target attribute status of any social account under each of the N attribute dimensions; where M and N are both positive integers.

[0130] Analysis unit 602 is used to detect the bonus qualification of each social account under each attribute dimension based on the target attribute status of each social account under each attribute dimension, and obtain the qualification detection result of each social account;

[0131] The acquisition unit 601 is also used to acquire the state score value of each attribute dimension;

[0132] The analysis unit 602 is also used to calculate the state measurement value of the target object based on the qualification detection results of each social account and the state score values ​​of each attribute dimension;

[0133] The analysis unit 602 is also used to analyze the target activity state of the target object in the social activity based on the state measurement value of the target object.

[0134] In one implementation, N is greater than 1, and the attribute state under any of the N attribute dimensions has a dependency relationship with the attribute state under one or more other attribute dimensions; wherein, the other attribute dimensions refer to the attribute dimensions other than the N attribute dimensions.

[0135] Accordingly, when the acquisition unit 601 is used to acquire the state score value of each attribute dimension, it can be specifically used to: determine the weight priority of each attribute dimension according to the dependency relationship between attribute states under different attribute dimensions; wherein, in any two attribute dimensions involved in any dependency relationship, the weight priority of the attribute dimension corresponding to the dependent attribute state is higher than the weight priority of the other attribute dimension.

[0136] Based on the principle that the weight priority and the unit level of the counting unit are positively correlated, the corresponding counting unit is determined for each attribute dimension according to the weight priority of each attribute dimension.

[0137] Using unit numerical values ​​and counting units for each attribute dimension, state score values ​​for each attribute dimension are generated respectively.

[0138] In another implementation, when the acquisition unit 601 determines the corresponding counting unit for each attribute dimension according to the weight priority of each attribute dimension based on the principle that the weight priority and the counting unit level are positively correlated, it can be specifically used for:

[0139] The N attribute dimensions are arranged in order of weight priority from low to high.

[0140] The counting unit of the attribute dimension that is first is determined as the base counting unit;

[0141] For an attribute dimension whose position is n, the count unit of the attribute dimension whose position is (n-1) is calculated based on the count unit of the attribute dimension whose position is (n-1) and the preset conversion rate; where n∈[2,N].

[0142] In another implementation, when the analysis unit 602 calculates the state measurement value of the target object based on the qualification detection results of each social account and the state scores of each attribute dimension, it may specifically be used for:

[0143] Based on the eligibility test results of each social media account and the status scores of each attribute dimension, a reference measurement value is calculated for each social media account.

[0144] The reference values ​​of each social media account are summed to obtain the state value of the target object.

[0145] In another implementation, the eligibility test result of each social media account is used to indicate whether each social media account is eligible for bonus points under each attribute dimension; correspondingly, when the analysis unit 602 calculates the reference measurement value of each social media account based on the eligibility test result of each social media account and the status score of each attribute dimension, it can be specifically used for:

[0146] Iterate through the M social media accounts and determine the current social media account being iterated over;

[0147] Based on the qualification test results of the current social account, one or more valid attribute dimensions are selected from the N attribute dimensions. The valid attribute dimension refers to the attribute dimension corresponding to the current social account's qualification for bonus points.

[0148] The state scores of each selected valid attribute dimension are summed to obtain the reference measurement value of the current social account.

[0149] In another implementation, N is greater than 1, and different attribute dimensions have different counting units; the state measurement value is represented by P digits, with each digit corresponding to an attribute dimension through a corresponding counting unit, and P being a positive integer.

[0150] For any given number, if there is no social account among the M social accounts that is eligible for bonus points under the attribute dimension corresponding to that number, the value of that number is invalid.

[0151] When there is a social account among the M social accounts that is eligible for bonus points under the attribute dimension corresponding to any of the digits, the value of any of the digits is a valid value, and the value of any of the digits is equal to the product of the number of social accounts that are eligible for bonus points under the attribute dimension corresponding to any of the digits and the status bonus value of the attribute dimension corresponding to any of the digits.

[0152] Among them, the attribute status under any attribute dimension includes a valid status and an invalid status, and when the target attribute status of any social account under any attribute dimension is a valid status under any attribute dimension, the social account is eligible for bonus points under any attribute dimension.

[0153] In another implementation, when the analysis unit 602 analyzes the target activity state of the target object in the social activity based on the target object's state measurement value, it may specifically be used for:

[0154] According to the selection order of digits from left to right, a reference digit is selected from the unselected digits among the P digits;

[0155] When the reference digit has an associated reference digit, the value on the reference digit is determined to be a reference value, and the value on the reference digit is determined to be a reference value; the reference digit refers to the digit located to the right of the reference digit and adjacent to the reference digit.

[0156] Based on at least one of the baseline value and the reference value, analyze the target activity state of the target object in the social activity.

[0157] In another embodiment, the analysis unit 602 can also be used for:

[0158] The validity of the state measurement value of the target object is validated.

[0159] If the state measurement value passes the validity check, then the step of selecting the reference digit from the unselected digits among the P digits is performed according to the selection order of digits from left to right.

[0160] If the state measurement value fails the validity check, the target object's target activity state in the social activity is determined to be the first activity state; the first activity state indicates that each of the M social accounts is in an invalid state under each attribute dimension.

[0161] In another embodiment, when the analysis unit 602 performs validity verification on the state measurement value of the target object, it may specifically be used for:

[0162] From the counting units of the N attribute dimensions, determine the counting unit with the highest unit level; and use the determined counting unit and preset value to calculate the effective measurement value;

[0163] If the state measurement value of the target object is less than the valid measurement value, then the state measurement value is determined to have failed the validity check.

[0164] If the state measurement value is greater than or equal to the valid measurement value, then the state measurement value is determined to have passed the validity check.

[0165] In another embodiment, when the analysis unit 602 analyzes the target activity state of the target object in the social activity based on at least one of the baseline value and the reference value, it may specifically be used to:

[0166] If the reference value is invalid, then the target activity state of the target object in the social activity is determined to be the second activity state;

[0167] Wherein, the second activity status indicates that: m out of the M social accounts are all in a valid state under the first attribute dimension, and all m social accounts are in an invalid state under the second attribute dimension.

[0168] Wherein, the value of m is less than or equal to M; the first attribute dimension includes: the attribute dimension corresponding to the reference digit, and the attribute dimension corresponding to each digit to the left of the reference digit; the second attribute dimension refers to: the attribute dimension corresponding to the reference digit.

[0169] In another embodiment, when analyzing the target activity state of the target object in the social activity based on at least one of the baseline value and the reference value, the analysis unit 602 can also be used to:

[0170] If the reference value is valid, then compare the size of the baseline value and the reference value;

[0171] If the benchmark value is greater than the reference value, the target activity state is determined to be the third activity state. The third activity state indicates that: all m social accounts are in a valid state under the first attribute dimension, and some of the m social accounts are in an invalid state under the second attribute dimension.

[0172] If the baseline value is not greater than the reference value, then the reference digit and the rightmost digit among the P digits are matched; if the match is successful, the target activity state is determined to be the fourth activity state, which indicates that m of the M social accounts are in a valid state under each attribute dimension; if the match fails, a new baseline digit is selected from the P digits.

[0173] In another implementation, N is equal to 1;

[0174] When none of the M social accounts are eligible for bonus points under the attribute dimension, the status measurement value is invalid.

[0175] When there is a social account among the M social accounts that is eligible for bonus points under the attribute dimension, the state measurement value is equal to the product of the number of social accounts eligible for bonus points under the attribute dimension and the state bonus value of the attribute dimension.

[0176] The attribute status under the attribute dimension includes valid status and invalid status; when the target attribute status of any social account under the attribute dimension is valid, the social account is eligible for bonus points under the attribute dimension.

[0177] In another implementation, when the analysis unit 602 analyzes the target activity state of the target object in the social activity based on the target object's state measurement value, it may specifically be used for:

[0178] Based on the state measurement value and the state score value of the attribute dimension, determine the number of social accounts that are eligible for bonus points under the attribute dimension;

[0179] If the number of accounts is less than M, then the target activity state is determined to be the fifth activity state; the fifth activity state indicates that some of the M social accounts are in an invalid state under the attribute dimension.

[0180] If the number of accounts equals M, then the target activity state is determined to be the sixth activity state; the sixth activity state indicates that each of the M social accounts is in a valid state under the attribute dimension.

[0181] In another embodiment, the analysis unit 602 can also be used for:

[0182] Determine the threshold for the measurement value;

[0183] When the state measurement value is less than or equal to the measurement value threshold, the target activity state of the target object in the social activity is determined to be the seventh activity state; the seventh activity state indicates that each of the M social accounts is in an invalid state under the attribute dimension.

[0184] When the status measurement value is greater than the measurement value threshold, the step of determining the number of social accounts that are eligible for bonus points under the attribute dimension is executed based on the status measurement value and the status bonus value of the attribute dimension.

[0185] According to one embodiment of this application, Figure 2 or Figure 3 Each step involved in the method shown can be performed by... Figure 6 This is performed by the individual units in the state analysis device shown. For example, Figure 2 Both steps S201 and S203 shown can be performed by Figure 6 The acquisition unit 601 shown is used to execute the steps, and steps S202 and S204-S205 can be performed by... Figure 6 The analysis unit 602 shown is used to perform this; for example, Figure 3Steps S301 and S303-S305 shown can all be performed by... Figure 6 The acquisition unit 601 shown is used to execute the steps, and steps S302 and S306-S307 can be performed by... Figure 6 The analysis unit 602 shown is used to perform this, etc.

[0186] According to another embodiment of this application, Figure 6 The various units in the illustrated state analysis device can be individually or entirely merged into one or more other units, or some of the units can be further divided into multiple functionally smaller units. This achieves the same operation without affecting the technical effects of the embodiments of this application. The above-mentioned units are based on logical function division. In practical applications, the function of one unit can also be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of this application, the state analysis device may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0187] According to another embodiment of this application, the following can be achieved by running on a general-purpose computing device, such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM), a device capable of performing operations such as... Figure 2 or Figure 3 The computer program (including program code) for each step involved in the corresponding method shown, to construct such... Figure 6 The state analysis apparatus shown herein, and the state analysis method for implementing the embodiments of this application, are described. The computer program may be recorded on, for example, a computer-readable recording medium, loaded onto the aforementioned computing device via the computer-readable recording medium, and run therein.

[0188] This application embodiment first obtains the target attribute status of each social account used by the target object in social activities, under each of the N attribute dimensions; and based on the target attribute status of each social account under each attribute dimension, detects the bonus qualification of each social account under each attribute dimension. Then, based on the qualification detection results of each social account and the status bonus value of each attribute dimension, the state measurement value of the target object can be calculated, thereby analyzing the target activity state of the target object in social activities based on the state measurement value of the target object. Using this state analysis method can effectively reduce the complexity of state analysis, thereby improving the efficiency of state analysis and saving time; moreover, since it is not necessary to exhaustively enumerate all state combinations, it can not only further save time, but also reduce memory usage.

[0189] Based on the description of the above method and apparatus embodiments, this application also provides a computer device. Please refer to... Figure 7 The computer device includes at least a processor 701, an input interface 702, an output interface 703, and a computer storage medium 704. The processor 701, input interface 702, output interface 703, and computer storage medium 704 within the computer device can be connected via a bus or other means. The computer storage medium 704 can be stored in the computer device's memory. The computer storage medium 704 is used to store computer programs, which include program instructions. The processor 701 is used to execute the program instructions stored in the computer storage medium 704. The processor 701 (or CPU (Central Processing Unit)) is the computing and control core of the computer device, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or function.

[0190] In one embodiment, the processor 701 described in this application embodiment can be used to perform a series of state analysis processes, specifically including: obtaining attribute information of M social accounts used by a target object in social activities, wherein the attribute information of any social account among the M social accounts includes: the target attribute state of any social account under each of the N attribute dimensions; wherein M and N are both positive integers; based on the target attribute state of each social account under each attribute dimension, detecting the bonus qualification of each social account under each attribute dimension, and obtaining the qualification detection result of each social account; obtaining the state bonus value of each attribute dimension, and calculating the state measurement value of the target object according to the qualification detection result of each social account and the state bonus value of each attribute dimension; analyzing the target activity state of the target object in the social activities based on the state measurement value of the target object, etc.

[0191] This application embodiment also provides a computer storage medium (memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer storage medium provides storage space that stores the operating system of the computer device. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by the processor 701. These instructions can be one or more computer programs (including program code). It should be noted that the computer storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device; optionally, it can also be at least one computer storage medium located remotely from the aforementioned processor.

[0192] In one embodiment, the processor 701 may load and execute one or more instructions stored in the computer storage medium to implement the aforementioned... Figure 2 or Figure 3 The corresponding steps of the method in the illustrated state analysis method embodiment; in specific implementation, one or more instructions in the computer storage medium are loaded by the processor 701 and executed as follows:

[0193] Obtain the attribute information of M social accounts used by the target object in social activities. The attribute information of any social account among the M social accounts includes: the target attribute state of any social account under each of the N attribute dimensions; where M and N are both positive integers.

[0194] Based on the target attribute status of each social account under each attribute dimension, the eligibility of each social account for bonus points under each attribute dimension is detected, and the eligibility detection result of each social account is obtained.

[0195] Obtain the status score for each of the aforementioned attribute dimensions;

[0196] Based on the eligibility test results of each social media account and the status score of each attribute dimension, the status measurement value of the target object is calculated;

[0197] Based on the state measurement value of the target object, the target activity state of the target object in the social activity is analyzed.

[0198] In one implementation, N is greater than 1, and the attribute state under any of the N attribute dimensions has a dependency relationship with the attribute state under one or more other attribute dimensions; wherein, the other attribute dimensions refer to the attribute dimensions other than the N attribute dimensions.

[0199] Accordingly, when obtaining the state score values ​​for each of the aforementioned attribute dimensions, one or more instructions can be loaded and executed by the processor 701:

[0200] Based on the dependencies between attribute states under different attribute dimensions, the weight priority of each attribute dimension is determined; wherein, in any two attribute dimensions involved in any dependency relationship, the weight priority of the attribute dimension corresponding to the dependent attribute state is higher than the weight priority of the other attribute dimension.

[0201] Based on the principle that the weight priority and the unit level of the counting unit are positively correlated, the corresponding counting unit is determined for each attribute dimension according to the weight priority of each attribute dimension.

[0202] Using unit numerical values ​​and counting units for each attribute dimension, state score values ​​for each attribute dimension are generated respectively.

[0203] In another implementation, when determining the corresponding counting unit for each attribute dimension based on the weight priority of each attribute dimension according to the principle that the weight priority and the counting unit level are positively correlated, the one or more instructions can be loaded and executed by the processor 701:

[0204] The N attribute dimensions are arranged in order of weight priority from low to high.

[0205] The counting unit of the attribute dimension that is first is determined as the base counting unit;

[0206] For an attribute dimension whose position is n, the count unit of the attribute dimension whose position is (n-1) is calculated based on the count unit of the attribute dimension whose position is (n-1) and the preset conversion rate; where n∈[2,N].

[0207] In another implementation, when calculating the state measurement value of the target object based on the qualification detection results of each social account and the state scores of each attribute dimension, the one or more instructions can be loaded and executed by the processor 701:

[0208] Based on the eligibility test results of each social media account and the status scores of each attribute dimension, a reference measurement value is calculated for each social media account.

[0209] The reference values ​​of each social media account are summed to obtain the state value of the target object.

[0210] In another implementation, the eligibility test result of each social media account is used to indicate whether each social media account is eligible for bonus points under each attribute dimension; correspondingly, when calculating the reference measurement value of each social media account based on the eligibility test result of each social media account and the status score of each attribute dimension, one or more instructions can be loaded and specifically executed by the processor 701:

[0211] Iterate through the M social media accounts and determine the current social media account being iterated over;

[0212] Based on the qualification test results of the current social account, one or more valid attribute dimensions are selected from the N attribute dimensions. The valid attribute dimension refers to the attribute dimension corresponding to the current social account's qualification for bonus points.

[0213] The state scores of each selected valid attribute dimension are summed to obtain the reference measurement value of the current social account.

[0214] In another implementation, N is greater than 1, and different attribute dimensions have different counting units; the state measurement value is represented by P digits, with each digit corresponding to an attribute dimension through a corresponding counting unit, and P being a positive integer.

[0215] For any given number, if there is no social account among the M social accounts that is eligible for bonus points under the attribute dimension corresponding to that number, the value of that number is invalid.

[0216] When there is a social account among the M social accounts that is eligible for bonus points under the attribute dimension corresponding to any of the digits, the value of any of the digits is a valid value, and the value of any of the digits is equal to the product of the number of social accounts that are eligible for bonus points under the attribute dimension corresponding to any of the digits and the status bonus value of the attribute dimension corresponding to any of the digits.

[0217] Among them, the attribute status under any attribute dimension includes a valid status and an invalid status, and when the target attribute status of any social account under any attribute dimension is a valid status under any attribute dimension, the social account is eligible for bonus points under any attribute dimension.

[0218] In another implementation, when analyzing the target activity state of the target object in the social activity based on the target object's state measurement value, the one or more instructions can be loaded and executed by the processor 701:

[0219] According to the selection order of digits from left to right, a reference digit is selected from the unselected digits among the P digits;

[0220] When the reference digit has an associated reference digit, the value on the reference digit is determined to be a reference value, and the value on the reference digit is determined to be a reference value; the reference digit refers to the digit located to the right of the reference digit and adjacent to the reference digit.

[0221] Based on at least one of the baseline value and the reference value, analyze the target activity state of the target object in the social activity.

[0222] In another implementation, the one or more instructions may also be loaded and executed by the processor 701:

[0223] The validity of the state measurement value of the target object is validated.

[0224] If the state measurement value passes the validity check, then the step of selecting the reference digit from the unselected digits among the P digits is performed according to the selection order of digits from left to right.

[0225] If the state measurement value fails the validity check, the target object's target activity state in the social activity is determined to be the first activity state; the first activity state indicates that each of the M social accounts is in an invalid state under each attribute dimension.

[0226] In another implementation, when validating the state measurement value of the target object, the one or more instructions can be loaded and executed by the processor 701:

[0227] From the counting units of the N attribute dimensions, determine the counting unit with the highest unit level; and use the determined counting unit and preset value to calculate the effective measurement value;

[0228] If the state measurement value of the target object is less than the valid measurement value, then the state measurement value is determined to have failed the validity check.

[0229] If the state measurement value is greater than or equal to the valid measurement value, then the state measurement value is determined to have passed the validity check.

[0230] In another implementation, when analyzing the target activity state of the target object in the social activity based on at least one of the baseline value and the reference value, the one or more instructions can be loaded and executed by the processor 701:

[0231] If the reference value is invalid, then the target activity state of the target object in the social activity is determined to be the second activity state;

[0232] Wherein, the second activity status indicates that: m out of the M social accounts are all in a valid state under the first attribute dimension, and all m social accounts are in an invalid state under the second attribute dimension.

[0233] Wherein, the value of m is less than or equal to M; the first attribute dimension includes: the attribute dimension corresponding to the reference digit, and the attribute dimension corresponding to each digit to the left of the reference digit; the second attribute dimension refers to: the attribute dimension corresponding to the reference digit.

[0234] In another implementation, when analyzing the target activity state of the target object in the social activity based on at least one of the baseline value and the reference value, the one or more instructions can be loaded and executed by the processor 701:

[0235] If the reference value is valid, then compare the size of the baseline value and the reference value;

[0236] If the benchmark value is greater than the reference value, the target activity state is determined to be the third activity state. The third activity state indicates that: all m social accounts are in a valid state under the first attribute dimension, and some of the m social accounts are in an invalid state under the second attribute dimension.

[0237] If the baseline value is not greater than the reference value, then the reference digit and the rightmost digit among the P digits are matched; if the match is successful, the target activity state is determined to be the fourth activity state, which indicates that m of the M social accounts are in a valid state under each attribute dimension; if the match fails, a new baseline digit is selected from the P digits.

[0238] In another implementation, N is equal to 1;

[0239] When none of the M social accounts are eligible for bonus points under the attribute dimension, the status measurement value is invalid.

[0240] When there is a social account among the M social accounts that is eligible for bonus points under the attribute dimension, the state measurement value is equal to the product of the number of social accounts eligible for bonus points under the attribute dimension and the state bonus value of the attribute dimension.

[0241] The attribute status under the attribute dimension includes valid status and invalid status; when the target attribute status of any social account under the attribute dimension is valid, the social account is eligible for bonus points under the attribute dimension.

[0242] In another implementation, when analyzing the target activity state of the target object in the social activity based on the target object's state measurement value, the one or more instructions can be loaded and executed by the processor 701:

[0243] Based on the state measurement value and the state score value of the attribute dimension, determine the number of social accounts that are eligible for bonus points under the attribute dimension;

[0244] If the number of accounts is less than M, then the target activity state is determined to be the fifth activity state; the fifth activity state indicates that some of the M social accounts are in an invalid state under the attribute dimension.

[0245] If the number of accounts equals M, then the target activity state is determined to be the sixth activity state; the sixth activity state indicates that each of the M social accounts is in a valid state under the attribute dimension.

[0246] In another implementation, the one or more instructions may also be loaded and executed by the processor 701:

[0247] Determine the threshold for the measurement value;

[0248] When the state measurement value is less than or equal to the measurement value threshold, the target activity state of the target object in the social activity is determined to be the seventh activity state; the seventh activity state indicates that each of the M social accounts is in an invalid state under the attribute dimension.

[0249] When the status measurement value is greater than the measurement value threshold, the step of determining the number of social accounts that are eligible for bonus points under the attribute dimension is executed based on the status measurement value and the status bonus value of the attribute dimension.

[0250] This application embodiment first obtains the target attribute status of each social account used by the target object in social activities, under each of the N attribute dimensions; and based on the target attribute status of each social account under each attribute dimension, detects the bonus qualification of each social account under each attribute dimension. Then, based on the qualification detection results of each social account and the status bonus value of each attribute dimension, the state measurement value of the target object can be calculated, thereby analyzing the target activity state of the target object in social activities based on the state measurement value of the target object. Using this state analysis method can effectively reduce the complexity of state analysis, thereby improving the efficiency of state analysis and saving time; moreover, since it is not necessary to exhaustively enumerate all state combinations, it can not only further save time, but also reduce memory usage.

[0251] It should be noted that, according to one aspect of this application, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the aforementioned... Figure 2 or Figure 3 The methods provided are among the various alternative approaches in the illustrated embodiments of the state analysis method.

[0252] Furthermore, it should be understood that the above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application are still within the scope of this application.

Claims

1. A state analysis method, characterized in that, include: Obtain attribute information of M social accounts used by the target object in social activities. The attribute information of any social account among the M social accounts includes: the target attribute state of the social account under each of the N attribute dimensions; where M and N are both positive integers; N is greater than 1; the attribute state under any of the N attribute dimensions has a dependency relationship with the attribute state under one or more other attribute dimensions; where the other attribute dimensions refer to: attribute dimensions other than the stated attribute dimension among the N attribute dimensions. Based on the target attribute status of each social account under each attribute dimension, the eligibility of each social account for bonus points under each attribute dimension is detected, and the eligibility detection result of each social account is obtained. Based on the dependencies between attribute states under different attribute dimensions, the weight priority of each attribute dimension is determined; wherein, in any two attribute dimensions involved in any dependency relationship, the weight priority of the attribute dimension corresponding to the dependent attribute state is higher than the weight priority of the other attribute dimension. Based on the principle that the weight priority and the unit level of the counting unit are positively correlated, the corresponding counting unit is determined for each attribute dimension according to the weight priority of each attribute dimension. Using unit values ​​and counting units for each attribute dimension, a status score for each attribute dimension is generated, and the status measurement value of the target object is calculated based on the qualification detection results of each social account and the status score for each attribute dimension. Based on the state measurement value of the target object, the target activity state of the target object in the social activity is analyzed.

2. The method as described in claim 1, characterized in that, The principle of positive correlation between weight priority and counting unit level is followed, and the corresponding counting unit is determined for each attribute dimension according to its weight priority, including: The N attribute dimensions are arranged in order of weight priority from low to high. The counting unit of the attribute dimension that is first is determined as the base counting unit; For an attribute dimension whose position is n, the count unit of the attribute dimension whose position is (n-1) is calculated based on the count unit of the attribute dimension whose position is (n-1) and the preset conversion rate; where n∈[2,N].

3. The method as described in claim 1, characterized in that, The step of calculating the state measurement value of the target object based on the qualification detection results of each social account and the state score values ​​of each attribute dimension includes: Based on the qualification test results of each social account and the status bonus values ​​of each attribute dimension, a reference measurement value is calculated for each social account. The reference measurement value of any social account is the sum of the status bonus values ​​of the attribute dimensions corresponding to which the account is qualified to receive bonus points. The reference values ​​of each social media account are summed to obtain the state value of the target object.

4. The method as described in claim 3, characterized in that, The qualification test results for each social media account are used to indicate whether each social media account is eligible for bonus points under each attribute dimension. The step of calculating a reference metric for each social media account based on the eligibility test results and the status scores for each attribute dimension includes: Iterate through the M social media accounts and determine the current social media account being iterated over; Based on the qualification test results of the current social account, one or more valid attribute dimensions are selected from the N attribute dimensions. The valid attribute dimension refers to the attribute dimension corresponding to the current social account's qualification for bonus points. The status scores of each selected valid attribute dimension are summed to obtain the reference measurement value of the current social account.

5. The method according to any one of claims 1-4, characterized in that, The value of N is greater than 1, and different attribute dimensions have different counting units; the state measurement value is represented by the value of P digits, and each digit corresponds to an attribute dimension through the corresponding counting unit, where P is a positive integer; For any given number, if there is no social account among the M social accounts that is eligible for bonus points under the attribute dimension corresponding to that number, the value of that number is invalid. When there is a social account among the M social accounts that is eligible for bonus points under the attribute dimension corresponding to any of the digits, the value of any digit is a valid value, and the value of any digit is equal to the number of social accounts that are eligible for bonus points under the attribute dimension corresponding to any of the digits. Among them, the attribute status under any attribute dimension includes a valid status and an invalid status, and when the target attribute status of any social account under any attribute dimension is a valid status under any attribute dimension, the social account is eligible for bonus points under any attribute dimension.

6. The method as described in claim 5, characterized in that, The analysis of the target activity state of the target object in the social activity based on the target object's state measurement value includes: According to the selection order of digits from left to right, a reference digit is selected from the unselected digits among the P digits; When the reference digit has an associated reference digit, the value on the reference digit is determined to be a reference value, and the value on the reference digit is determined to be a reference value; the reference digit refers to the digit located to the right of the reference digit and adjacent to the reference digit. Based on at least one of the baseline value and the reference value, analyze the target activity state of the target object in the social activity.

7. The method as described in claim 6, characterized in that, The method further includes: The validity of the state measurement value of the target object is validated. If the state measurement value passes the validity check, then the step of selecting the reference digit from the unselected digits among the P digits is performed according to the selection order of digits from left to right. If the state measurement value fails the validity check, the target object's target activity state in the social activity is determined to be the first activity state; the first activity state indicates that each of the M social accounts is in an invalid state under each attribute dimension.

8. The method as described in claim 7, characterized in that, The validity verification of the state measurement value of the target object includes: From the counting units of the N attribute dimensions, determine the counting unit with the highest unit level; and use the determined counting unit and preset value to calculate the effective measurement value; If the state measurement value of the target object is less than the valid measurement value, then the state measurement value is determined to have failed the validity check. If the state measurement value is greater than or equal to the valid measurement value, then the state measurement value is determined to have passed the validity check.

9. The method as described in claim 6, characterized in that, The step of analyzing the target activity state of the target object in the social activity based on at least one of the baseline value and the reference value includes: If the reference value is invalid, then the target activity state of the target object in the social activity is determined to be the second activity state; Wherein, the second activity status indicates that: m out of the M social accounts are all in a valid state under the first attribute dimension, and all m social accounts are in an invalid state under the second attribute dimension. Wherein, the value of m is less than or equal to M; the first attribute dimension includes: the attribute dimension corresponding to the reference digit, and the attribute dimension corresponding to each digit to the left of the reference digit; the second attribute dimension refers to: the attribute dimension corresponding to the reference digit.

10. The method as described in claim 9, characterized in that, The step of analyzing the target activity state of the target object in the social activity based on at least one of the benchmark value and the reference value further includes: If the reference value is valid, then compare the size of the baseline value and the reference value; If the benchmark value is greater than the reference value, the target activity state is determined to be the third activity state. The third activity state indicates that: all m social accounts are in a valid state under the first attribute dimension, and some of the m social accounts are in an invalid state under the second attribute dimension. If the baseline value is not greater than the reference value, then the reference digit and the rightmost digit among the P digits are matched to detect whether the reference digit and the rightmost digit are the same; if the match is successful, the target activity state is determined to be the fourth activity state, which indicates that m of the M social accounts are in a valid state under each attribute dimension; if the match fails, a new baseline digit is selected from the P digits.

11. A state analysis device, characterized in that, include: The acquisition unit is used to acquire attribute information of M social accounts used by the target object in social activities. The attribute information of any social account among the M social accounts includes: the target attribute state of the social account under each of the N attribute dimensions; where M and N are both positive integers; N is greater than 1; the attribute state under any of the N attribute dimensions has a dependency relationship with the attribute state under one or more other attribute dimensions; where the other attribute dimensions refer to: attribute dimensions other than the stated attribute dimension among the N attribute dimensions. The analysis unit is used to detect the bonus eligibility of each social account under each attribute dimension based on the target attribute status of each social account under each attribute dimension, and obtain the eligibility detection result of each social account. The acquisition unit is further configured to determine the weight priority of each attribute dimension based on the dependency relationship between attribute states under different attribute dimensions; wherein, in any two attribute dimensions involved in any dependency relationship, the weight priority of the attribute dimension corresponding to the dependent attribute state is higher than the weight priority of the other attribute dimension. Based on the principle that the weight priority and the unit level of the counting unit are positively correlated, the corresponding counting unit is determined for each attribute dimension according to the weight priority of each attribute dimension. Using the unit numerical value and the counting unit of each attribute dimension, the state score value of each attribute dimension is generated respectively; The analysis unit is also used to calculate the state measurement value of the target object based on the qualification detection results of each social account and the state score values ​​of each attribute dimension; The analysis unit is also used to analyze the target activity state of the target object in the social activity based on the state measurement value of the target object.

12. A computer device, characterized in that, The computer device includes an input interface and an output interface, and the computer device further includes: A processor, adapted to implement one or more instructions; and, A computer storage medium storing one or more instructions, said one or more instructions being adapted to be loaded by the processor and executed as described in any one of claims 1-10.

13. A computer storage medium, characterized in that, The computer storage medium stores one or more instructions, which are adapted to be loaded by a processor and executed by the state analysis method as described in any one of claims 1-10.

14. A computer program product, characterized in that, The computer program product includes computer instructions; when the computer instructions are executed by a processor, they implement the state analysis method as described in any one of claims 1-10.

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