Block chain enabled community public welfare point management method and system

Abstract

The invention relates to the technical field of point management, in particular to a block chain enabled community public welfare point management method and system, and the method comprises the following steps: obtaining point use records formed by community behaviors, extracting fields and labels, marking attribution, matching paths and node states, screening out abnormal paths, and extracting behaviors and path states. And marking repeated paths, writing the repeated paths into a cache, comparing behavior contents with density frequencies, checking permission registration writing field records, and obtaining an on-chain executable purpose list. According to the method, through affiliation labeling of a purpose field and a service behavior, a corresponding relation between behavior content and a path number is established, the path number is combined with a node state to complete purpose binding, in the execution process, the path state is extracted to be paired with a service label, and a repeated path is continuously identified by the behavior and is imported into a cache; and comparing and screening the calling density and the use content, and writing the path into a calling list after permission registration and checking, thereby completing integral use control and path calling management.

Description

Technical Field

[0001] The present invention relates to the field of point management technology, and in particular to a community public welfare point management method and system enabled by blockchain. Background Art

[0002] The field of points management technology involves quantifying and assigning values ​​to the behaviors of individuals or organizations in specific activities based on preset rules, and then using points as a management method to record behaviors, reward means, and basis for participation. This technical field mainly includes the setting of points generation rules, points recording mechanism, points circulation method, points usage standards, points cancellation method, and association processing mechanism with user behavior database. Points are widely used in commercial marketing, corporate incentives, urban governance, community construction and other scenarios. In recent years, with the increase in diversity of user behavior data and the increase in management complexity, this field has gradually introduced new technologies such as digital identity binding, distributed ledger records, and traceable transaction verification to improve the standardization and transparency of the points system.

[0003] Among them, traditional community public welfare points management refers to the use of reading check-ins, public welfare services, low-carbon travel, garbage sorting, charitable donations and other behaviors as points in community governance activities. Residents' behaviors are recorded and counted through community management platforms or manual registration methods. A centralized database system is usually used to store and retrieve user points data. Points accumulation and usage rules are mostly manually set by managers and announced through community notices or platform notifications. The use of points is mainly limited to redeeming physical goods or services, such as redeeming community items, deducting part of the fees or participating in lotteries. This method has certain deficiencies in the consistency of points data, verification accuracy and transparency of use. Especially in scenarios involving community fund co-construction, point transfer or cross-institutional linkage, it is easy to cause management loopholes and data credibility issues.

[0004] In the existing technology, the purpose field is often configured independently from the service behavior and separated from the management process. The path allocation relies on static settings of field rules. A dynamic relationship between the call path and the execution behavior is not established. The path call record cannot reflect the actual connection between the behavior content and the service goal. When multiple behaviors trigger the same purpose path, it is difficult to distinguish the corresponding usage scenarios. The path usage status lacks a call density reference standard. In frequent usage scenarios, it is impossible to judge the degree of path repetition. After a path conflict occurs, it can only rely on manual intervention in the background. There is no binding restriction mechanism between the permission registration record and the purpose path. When multiple service sources are concurrent or the path conditions are unclear, the execution boundary of the integral purpose is blurred, affecting the controllability of the path determination and the certainty of the call behavior. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the embodiment of the present invention provides a community public welfare points management method enabled by blockchain, including the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a blockchain-enabled community public welfare points management method, comprising the following steps: S1: Based on the point usage declaration records generated from community reading registration, service check-in, and fund incentives, extract the field position and behavior label, and mark the field ownership with the task number to obtain a service behavior usage attribution list; S2: Based on the purpose field in the service behavior purpose attribution list, extract the corresponding path number and match the node status, filter out abnormal paths, match the enabled paths with the fields and register them, and obtain a purpose path binding record table; S3: Based on the service behavior corresponding to the activated path number in the usage path binding record table, extract the behavior name and match it with the path status, mark the repeated path and write it into the cache, extract the abnormal section, and obtain a list of suspicious usage behavior paths; S4: Based on the path number in the suspicious usage behavior path list, query the blockchain status index table for the corresponding call density identifier, extract the usage field, and compare the frequency of the behavior content with the density situation to obtain a frozen status usage path list; S5: Based on the unblocked path segments in the frozen state usage path list, the corresponding usage fields and service behaviors are extracted, and after checking the registration status of the authority ownership table, the path segments are written into the field records to obtain the executable usage list on the chain.

[0006] As a further solution of the present invention, the service behavior usage attribution list includes the behavior type label, service behavior content, task number field attribution, and usage field position; the usage path binding record table includes the usage field, enabled path number, and path usage matching result; the suspicious usage behavior path list includes the duplicate binding path cache, path call status, and abnormal paragraph mark; the frozen state usage path list includes the path segment number, call density identifier, and frequency conflict status result; the on-chain executable usage list includes the usage field, service behavior, permission registration information, and the callable state field.

[0007] As a further solution of the present invention, the specific steps of S1 are: S101: Obtain the point usage declaration records from community reading task registration, service check-in, and community fund incentive initiatives, extract the usage field location, locate the behavior source in the field content, map the record source and usage fields accordingly, distinguish the behavior record entries and field pairings based on the task code, and obtain the task behavior field mapping structure; S102: Based on the task behavior field mapping structure, extract the behavior type label identified in the record, identify the service behavior content that matches the label, match the extracted behavior name with the field mapping information, mark the behavior content and field attribution of the unique identification relationship, and obtain a behavior field attribution label set; S103: Based on the behavior field attribution label set, extract the binding sequence of the task number and the purpose field, integrate the field attribution label and the task number sequence, number and identify each attribution mark in the integration result and write it into the field configuration list in a classified manner to obtain the service behavior purpose attribution list.

[0008] As a further solution of the present invention, the specific steps of S2 are: S201: Based on the usage fields in the service behavior usage attribution list, extract the usage type corresponding to each field, call the path type configuration table to obtain the block path number associated with the usage type, exclude fields without associated paths, and obtain a usage field path number list; S202: Based on the path numbers listed in the path number list in the usage field, calling the path index table to read the node status of the path number, comparing the enabled state paths with the number of paths, calculating the path enabled ratio, and obtaining a set of enabled state path numbers; S203: Based on the reserved path number content in the enabled state path number set, extract the corresponding purpose field, pair the purpose field with the path number one by one and write it into the path purpose registration table to obtain a purpose path binding record table.

[0009] As a further solution of the present invention, the calculation formula of the path activation ratio is specifically: ; in, Represents the proportion of paths activated, Represents the total number of path numbers, Represents the path number The current enabled state in the path index table, Represents the path number The number of activations, Represents the path number Number of bans, Represents the path number The number of activations within the scheduling cycle, Represents the path number The number of disables during the scheduling period, Represents the path number The standard deviation in the enabled state, Represents the path number Offset in network traffic.

[0010] As a further solution of the present invention, the specific steps of S3 are: S301: extracting behavior names and path numbers based on the service behavior records corresponding to the activated path numbers in the usage path binding record table, pairing them one by one to form behavior path combinations, and sorting the paired items to obtain a path behavior pairing sequence; S302: Based on the path numbers and behavior names in the path-behavior pairing sequence, calculate the repetition distribution density of each behavior name in the corresponding path number, and express it as the ratio of the number of repetitions of the behavior name to the total number of behaviors with the number, to obtain a path-behavior density identifier set; S303: Based on the ratio data in the path behavior density identification set, the path numbers with different repeated distribution density values ​​are extracted, the corresponding segment information is called to compare the service behavior with the path status, and the path segments with abnormal combinations are extracted to obtain a list of suspicious use behavior paths.

[0011] As a further solution of the present invention, the calculation formula for the repeated distribution density of each behavior name in the corresponding path number is specifically: ; in, The representative path number is Name of the next line The repetitive distribution density, Represents the path number Name of China Bank The cumulative number of occurrences of Represents the path number Name of China Bank The cumulative number of occurrences of Representative behavior name In the sample with the path number The activity factor, Represents the path number The total number of behavior names in Represents the path number The middle behavior is the disturbance correction term of the frequency distribution, Representative behavior name distribution indicators.

[0012] As a further solution of the present invention, the specific steps of S4 are: S401: Based on the path segment number in the suspicious usage behavior path list, search for the entry with the corresponding number in the blockchain status index table, extract the call density identifier corresponding to each path segment, and pair it with the path number to obtain a path segment call density information set; S402: extracting the usage fields associated with the path segments based on the path segment call density information set, querying the service type and behavior range corresponding to each field, marking the correspondence between the path segments and the usage fields, and obtaining usage field behavior range marking results; S403: Based on the result of the usage field behavior range marking and in combination with the usage status value in the density information, determine whether there is a call frequency conflict in the path segment, mark the conflicting path segment status as frozen and record the path number, and obtain a frozen status usage path list.

[0013] As a further solution of the present invention, the specific steps of S5 are: S501: Based on the unblocked path segments in the frozen usage path list, extract the usage fields and service behaviors associated with each path segment, and extract the corresponding content according to the path number to obtain a path usage field mapping result; S502: Based on the path usage field mapping result, the registration status of the service behavior and usage field in the path segment in the permission attribution table is checked, and combinations where the behavior registration exists and the field is in a callable state are screened out to obtain a callable path permission matching result; S503: Based on the callable path permission matching result, the filtered path segments are written according to the corresponding purpose fields, and classified and recorded by fields to obtain an executable purpose list on the chain.

[0014] A blockchain-enabled community public welfare points management system, including: The usage attribution module obtains the usage declaration records of points generated by community reading registration, service check-in and fund incentives, extracts the field position and marks the behavior attribution content in combination with the task number to obtain a service behavior usage attribution list; The path binding module extracts the corresponding path number based on the purpose field in the service behavior purpose attribution list and checks the node status. After filtering out unregistered and closed paths, the enabled path number is paired with the purpose field and written into the registration table to obtain a purpose path binding record table. The behavior identification module matches the behavior name with the path call status based on the service behavior record corresponding to the activated path in the usage path binding record table, marks the repeated path segments and extracts the abnormal segments into the cache to obtain a list of suspicious usage behavior paths; The frequency comparison module queries the call density identifier of the blockchain status index table based on the path number in the suspicious use behavior path list, compares the service type of the use field with the behavior range to screen the conflict frequency, obtains the frozen state use path list, and passes it to the call verification module; The call verification module extracts the usage fields and service behaviors based on the unblocked path segments in the frozen usage path list, checks the registration status and writes the field records to obtain the executable usage list on the chain.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, a correspondence between the behavior content and the path number is established through the attribution labeling of the purpose field and the service behavior. The path number is combined with the node status to complete the purpose binding. During the execution process, the path status is extracted and paired with the service label. Duplicate paths are identified by behavior continuity and imported into the cache. The call density is compared and screened in combination with the purpose content. The path is written into the call list after permission registration and verification, completing the point purpose control and path call management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 Schematic diagram of the steps of the present invention; Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0020] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0021] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0022] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0023] See also Figure 1 , an embodiment of the present invention provides a community public welfare points management method enabled by blockchain, comprising the following steps: S1: Obtain the point usage declaration records from community reading task registration, service check-in, and community fund incentive initiatives, extract the usage field location, identify the service behavior content based on the behavior type label, annotate the field ownership based on the task number, and obtain a service behavior usage ownership list; S2: Based on the purpose field in the service behavior purpose attribution list, extract the block path number corresponding to the purpose type, call the block path index table to match the path number with the node status, filter out the unregistered and closed paths, match the path number of the enabled path with the path field and write it into the path purpose registration table to obtain the purpose path binding record table; S3: Based on the service behavior records corresponding to the activated path numbers in the usage path binding record table, each behavior name is matched with the path call status. Repeated binding paths are marked and added to the cache. The marked paths are summarized and abnormal sections are extracted to obtain a list of suspicious usage behavior paths. S4: Based on the path segment number in the suspicious usage behavior path list, query the call density identifier in the blockchain status index table, extract the usage field bound to the path segment, compare the density identifier with the service type and behavior range contained in the usage, and if there is a frequency conflict, freeze the path segment and register the status result to obtain a frozen usage path list; S5: Based on the unblocked path segments in the frozen usage path list, extract the corresponding usage fields and service behaviors, check the registration status of the permissions in the attribution table, identify the registered service behaviors and usage fields in the callable state, write the path segments into the usage fields, and obtain the executable usage list on the chain.

[0024] The service behavior usage attribution list includes the behavior type label, service behavior content, task number field attribution, usage field location, usage path binding record table includes usage field, enabled path number, path usage matching result, suspicious usage behavior path list includes duplicate binding path cache, path call status, abnormal paragraph mark, frozen status usage path list includes path segment number, call density identifier, frequency conflict status result, on-chain executable usage list includes usage field, service behavior, permission registration information, and field in callable status.

[0025] The specific steps of S1 are: S101: Obtain the point usage declaration records from community reading task registration, service check-in, and community fund incentive initiatives, extract the usage field location, locate the behavior source in the field content, map the record source and usage fields accordingly, distinguish the behavior record entries and field pairings based on the task code, and obtain the task behavior field mapping structure; First, collect the purpose fields in each record form in turn. After reading the field position index, read the content of the corresponding field in each record one by one, extract the text fragment containing the task scenario identifier for the field value, and determine whether it contains the three types of behavioral keywords: "reading task", "check-in record", and "incentive initiation". If the extraction result is "community morning reading task-check-in", the source of the behavior is identified as service check-in. Subsequently, a mapping pair is constructed between the behavior source label and the purpose field, and the number and its combination constitute a unique key item. The coding data is read according to the task coding column in the form, and the code is paired with the field number to form an index table. Then, a combined index extraction is performed according to the three items of "behavior source-field content-task coding". The reading structure is as follows: "Reading task / field A / task A123", and the combined structure is written into the mapping form according to a unified path, and finally the task behavior field mapping structure is obtained.

[0026] S102: Based on the task behavior field mapping structure, extract the behavior type label identified in the record, identify the service behavior content that matches the label, match the extracted behavior name with the field mapping information, mark the behavior content and field attribution with a unique identification relationship, and obtain a behavior field attribution label set; First, obtain the behavior type label items contained therein. Each label item consists of a behavior description phrase, usually including operational terms such as "reading", "punching in", "service participation", and "task triggering". All label items are combined into a behavior label set. After extracting each label in the set one by one, match the service behavior description statement identified in the field content, identify the entry that matches the behavior description and the label, extract the matching behavior content as an independent service behavior name, and extract the task identification code and purpose field number data in the original field to form a behavior-field combination record. For each set of records, set a unique index rule based on the three elements of behavior name, field number, and task code, and use the splicing format " A unique key-value identification structure is established in the form of "behavior name-field number-task code". By performing a matching operation on all combined records, it is detected whether there are repeated annotations or field overlaps in the key value. If an entry "check-in behavior-A13-T20200509" only appears once and the field number "A13" is not referenced by other behavior names, then the behavior field is marked as a unique attribution relationship entry. On the contrary, if there are multiple behavior names bound to the same field number, it is temporarily not included in the attribution tag set. On this basis, the behavior content and field identification content corresponding to all unique key-value structures are sorted out, and the tag relationship pairs are archived to finally obtain the behavior field attribution tag set.

[0027] S103: Based on the behavior field attribution tag set, extract the binding sequence of the task number and the purpose field, integrate the field attribution tags and the task number sequence, number and identify each attribution tag in the integration result, and write them into the field configuration list in a classified manner to obtain the service behavior purpose attribution list; First, classify the attribute tag set according to the task number, and centrally process the field tags under the same task number. Assuming that there are three records in the tag set, namely "Reading Behavior-A12", "Punching Behavior-B06", and "Service Participation Behavior-C03", which correspond to task codes T001, T002, and T003 respectively, after clustering according to the task code, record the field number sequence and behavior content corresponding to each task, and then number the binding status of each behavior field, and assign unique numbers to the tags in the field using sequential bit marking. For example, "Reading Behavior-A12" is numbered F01, "Punching Behavior-B06" is numbered F02, and the number is used as the character The segment identifier is supplemented and written into the corresponding task number entry to complete the fusion of the field label and the task number. In the fused data set, the record entry format is such as "Task T001-Field F01-Behavior Reading Behavior". The structure is further judged whether there is a field ownership conflict in the field number under the task number. If a field number is bound to multiple behavior labels or referenced by multiple task numbers, it is marked as "Multi-value Conflict Identifier" in the field configuration list. If the binding relationship is unique, it is marked as "Single-value Attribution Identifier". It is classified and written into the corresponding area of ​​the field configuration list according to the identifier category. Finally, the field label number structure and attribution judgment results under all task numbers are integrated to obtain the service behavior usage attribution list.

[0028] The specific steps of S2 are: S201: Based on the usage fields in the service behavior usage attribution list, extract the usage type corresponding to each field, call the path type configuration table to obtain the block path number associated with the usage type, exclude fields without associated paths, and obtain a usage field path number list; First, read the field content of each record one by one, and parse out the associated use type from it. For example, in the record item "Health Promotion Activities - Residents Punch Card", extract the use type as "Health Promotion". Then, according to the use type, retrieve the corresponding path number in the path type configuration table. If there is a path type item corresponding to "Health Promotion" in the configuration table, record its corresponding path number information. For example, if the path number is "P201", then add the path number as the associated path of the use type to the mapping list. If the corresponding path item of the use type is not found in the configuration table, the field is deemed to have no path corresponding field and will no longer be included in the mapping range. After completing the traversal and path matching of all purpose fields, a one-to-one mapping table between purpose types and path numbers is constructed. For example, "health promotion" corresponds to the paths "P201, P203", and "education communication" corresponds to the paths "P118, P120". All path number sets are then repeatedly checked to confirm that there are no redundant records between path numbers under the same purpose type. The path number structure corresponding to each purpose field is sorted so that the path number list is consistent with the field reading order, further ensuring that the order in subsequent calls is traceable. Finally, the purpose fields and path number structures that have established mapping relationships are summarized to obtain a list of purpose field path numbers.

[0029] S202: Based on the path numbers listed in the path number list in the usage field, call the path index table to read the node status of the path number, compare the enabled state paths with the number of paths, calculate the path enabled ratio, and obtain a set of enabled state path numbers; The calculation formula for the path activation ratio is as follows: ; in, Represents the proportion of paths activated, Represents the total number of path numbers, Represents the path number The current enabled state in the path index table, Represents the path number The number of activations, Represents the path number Number of bans, Represents the path number The number of activations within the scheduling cycle, Represents the path number The number of disables during the scheduling period, Represents the path number The standard deviation in the enabled state, Represents the path number Offset in network traffic; Assumptions: when =3, the path numbers are 、 、 , the input data for each path is as follows: In the path, =1, obtained from the real-time recording of node status; =10, =2, =5, =3, =0.5, =1.1; Substituting into the formula: ; In the path, =0, obtained from the real-time recording of node status; =8, =1, =7, =2, =0.8, =0.9; Substituting into the formula: ; In the path, =1, obtained from the real-time recording of node status; =12, =3, =4, =5, =0.3, =1.2; Substituting into the formula: ; Substituting the three terms into the total formula for θ′: ; The results show that the path activation ratio It is 253.7%, reflecting that there are deviations between the activation status of multiple paths in the current path set and their historical behavior, status fluctuations, and network load changes.

[0030] S203: extracting the corresponding purpose field based on the retained path number content in the enabled state path number set, and pairing the purpose field with the path number one by one and writing it into the path purpose registration table to obtain a purpose path binding record table; First, confirm whether the enabled status of all path numbers in the set is "Y" one by one, and only retain the numbers with the status confirmation item "Y". Then sort the path numbers in lexicographic order to ensure the order consistency of subsequent pairing operations. Then, extract the usage field content corresponding to each enabled path number one by one. This extraction operation needs to be matched with the path usage type registration record. For example, for a path numbered "P302", the usage field is marked as "Community Health Check-in" in the configuration record. In this case, a pairing relationship is directly established between the path and the field. If the path number cannot find a corresponding one in the usage registration, Item, then remove the number from the matching list. After completing the one-by-one matching between all path numbers and purpose fields, the matching results will be written into the path purpose registration table in sequence. The registration table fields should include path number, purpose field, matching time, registration source, etc., and a unique matching identification number will be set for each written item for subsequent tracking. In actual operation, if the path number "P302" is successfully matched with the field "Community Health Check-in", the matching number "B0782" will be registered in the data item of this row. All completed matching items will be merged and summarized to finally obtain the purpose path binding record table.

[0031] The specific steps of S3 are: S301: Based on the service behavior record corresponding to the activated path number in the usage path binding record table, extract the behavior name and path number, pair them one by one to form a behavior path combination, sort the paired items, and obtain a path behavior pairing sequence; First, extract the path numbers contained in the activated path number set, and confirm whether there is a paired behavior record for each number in the binding record table. After confirming its existence, call the corresponding behavior record, extract the behavior name field marked in it, and establish an association between the behavior name and the path number. For example, if the path number is "P1103" and its paired behavior name is "Health Check-in", then the pair is confirmed as a combination item, and then all combination items are sorted in ascending order by path number. The path numbers need to be standardized before sorting. If there is a mixture of letters and numbers in the number format, a unified dictionary order needs to be used. The sorting method is compared. For example, "P1103" should be placed before "P1110". After the sorting is completed, all matching items are renumbered. The numbering rule can be set to ascending order. The matching sequence is numbered from "B001" to "Bn". The number and the corresponding matching item are synchronously recorded and written into the matching sequence list field item. For example, the matching item corresponding to the number "B003" is the combination of the path "P1103" and the behavior "health check-in". The record is displayed in the sequence table as "B003: P1103-health check-in". After all matching relationships are sorted and coded, the path-behavior matching sequence is obtained.

[0032] S302: Based on the path numbers and behavior names in the path-behavior pairing sequence, calculate the repetition distribution density of each behavior name in the corresponding path number, and express it as the ratio of the number of repeated occurrences of the behavior name to the total number of behaviors with the number, to obtain a path-behavior density identifier set; The calculation formula for the repeated distribution density of each behavior name in the corresponding path number is as follows: ; in, The representative path number is Name of the next line The repetitive distribution density, Represents the path number Name of China Bank The cumulative number of occurrences of Represents the path number Name of China Bank The cumulative number of occurrences of Representative behavior name In the sample with the path number The activity factor, Represents the path number The total number of behavior names in Represents the path number The middle behavior is the disturbance correction term of the frequency distribution, Representative behavior name Distribution indicators of; Assumptions: Set path number in the scene ; The set of behavior names is A, B, C, D; Let the corresponding behavior numbers be 1 to 4; Total number of behavior names in path 08 ; The specific number of times the behavior occurs is: (Behavior A); (Behavior B); (Behavior C); (Behavior D); For the name of the behavior (i.e. behavior A), the goal is to calculate ; The behavioral activity factor (the frequency of each behavior in history and its proportion of the total number of similar behaviors) is introduced as follows: ; ; ; ; Calculate the weighted average part: ; Weighted average: ; Disturbance correction value The setting basis is the standard deviation of path behavior frequency Multiply by the average path activity: ; Average number of behaviors ; Therefore ; Behavioral Complexity Index : Behavior A belongs to a 3-level trigger behavior chain (hierarchy), is associated with 2 dependent fields, and uses weighted settings: ; Final substitution: ; The results show that the repetition density of behavior A in path 08 is 1.212, indicating that there is a deviation in the weighted activity and the behavioral mean after disturbance in the corresponding path number. This value will be used in the future to determine the significance of the difference in behavior distribution and to locate abnormal behavior combinations.

[0033] S303: Based on the ratio data in the path behavior density identification set, the path numbers with different repeated distribution density values ​​are extracted, the corresponding segment information is called to compare the service behavior with the path status, and the path segments with abnormal combinations are extracted to obtain a list of suspicious use behavior paths; First, extract the path number content where there is a difference in the density of repeated distribution of behavior names. When extracting, it is necessary to determine the degree of difference between the ratio corresponding to the path number and the numerical value of other path numbers. If the difference exceeds the preset deviation ratio threshold range, it is considered that there is an abnormality in the density distribution. Continue to call the path segment information associated with the corresponding path number, and perform field-level comparison processing on the service behavior content registered in the path segment and the path status label. For example, the service behavior bound to the path number "P2304" is "Community Lecture", and its path status is "Activated", but the number of repeated bindings of the behavior in multiple paths has exceeded 2 times the average frequency, then it will be used as a difference path identifier. During the processing process, it is also necessary to compare the different paths. The distribution range of service behaviors. If the same behavior is found to be repeatedly registered in multiple non-continuous path segments and the status identifiers are all enabled, it is necessary to combine the behavior name and path status to compare them item by item to determine whether their combination structure violates the path continuity setting rules. In the rule setting, the continuous distribution of behaviors must be located in adjacent numbered items in the path segment sequence. For example, "P2203, P2204, P2205" are continuous path segments. If "P2203" and "P2205" have the same behavior but "P2204" has not registered the behavior, then the path structure should be marked as an abnormal combination. Finally, the path number items that meet the abnormal combination conditions are extracted from all compared path segments to obtain a list of suspicious use behavior paths.

[0034] The specific steps of S4 are: S401: Based on the path segment number in the suspicious use behavior path list, search for the entry with the corresponding number in the blockchain status index table, extract the call density identifier corresponding to each path segment, and pair it with the path number to obtain a path segment call density information set; First, the registration items of each number in the blockchain status index table are retrieved in sequence. During the retrieval process, the path segment number is input as the query condition into the path field of the index table, and the status value identified under each registration item is extracted. The status value field contains multiple sub-field parameters. The call density item is located by the field name, and the number of calls, number of service types, and path activity duration of the path segment within the specified time period are extracted. The extracted density value items are further identified by the path segment number and re-paired with the original path number. In actual application, if the path segment numbered P3307 shows a call density of 18 times, 3 service types, and a total activity duration of 20 minutes in the index table, then the path density value item should be marked as [Path P3307, Density 18]. All path segment density items form a paired list of path numbers and call frequencies, which are sorted and output in combination with the number order to obtain a path segment call density information set.

[0035] S402: Based on the path segment call density information set, the purpose field associated with the path segment is extracted, the service type and behavior range corresponding to each field are queried, the correspondence between the path segment and the purpose field is annotated, and the purpose field behavior range annotation result is obtained; First, read the density records matched with the path number one by one, identify the purpose field information of the path segment in the record, enter the purpose field as the query condition into the service type behavior field table, retrieve the service type label associated with the field and the behavior range label it covers one by one, classify and identify the task category defined in each service type label, and combine the original behavior call record of the path number to determine the correspondence between the actual task involved in the path segment and the field definition behavior range. In operation, if the path number is L5121 and the corresponding purpose field is "literature review", and the service type defined in the field includes "daily tasks" and "content review", and the behavior range is "document evaluation, report annotation", then all service behaviors appearing in the path segment need to be checked one by one to see if they fall within the above behavior range. Then, the matched relationship after judgment is matched one by one according to the path number and purpose field. Finally, the matching results of all path segments and purpose fields are summarized and annotated to obtain the purpose field behavior range annotation result.

[0036] S403: Based on the result of the behavior range marking of the usage field and the usage status value in the density information, determine whether there is a call frequency conflict in the path segment, mark the conflicting path segment as frozen and record the path number, and obtain a frozen status usage path list; First, read the field behavior label and covered task scope corresponding to each path number one by one. The number of task calls corresponding to the behavior label is compared with the usage status value identified by the path segment in the density information set to identify whether the number of calls for the path segment within the specified time period is significantly higher than the regular execution frequency of the corresponding task behavior. During the judgment process, the path segment call data within the past seven days is selected as the base value. Combined with the daily call upper limit defined in the task configuration standard, the threshold is set to 150 times. If the total number of calls for the usage field "Graphic and Text Review" corresponding to path segment number P1026 reaches 1100 times within seven days, its average daily call value is calculated to be 157 times. This value exceeds the set threshold and is determined to be a call frequency conflict. Then, a frozen status mark is added to the path segment, and the status field is written to "frozen". The path number is simultaneously recorded and filed in the frozen path list record table. Finally, all path numbers that meet the conflict judgment criteria are summarized and output to obtain a frozen status usage path list.

[0037] The specific steps of S5 are: S501: Based on the unblocked path segments in the frozen usage path list, extract the usage fields and service behaviors associated with each path segment, extract the corresponding content according to the path number, and obtain the path usage field mapping result; First, a number extraction operation is performed on each path segment. By traversing the path segment number set, the block registration content associated with the number is retrieved one by one. When retrieving the registration content, the purpose field identification information in the field item is read, and a one-to-one mapping is formed between the purpose field and the path number. Then, the service behavior category bound to each purpose field is identified, and the keyword content corresponding to the service behavior label is extracted from the field to serve as the basis for behavior identification. For example, if the purpose "Graphic and Text Review" is extracted from the path segment field numbered R8801, the service behavior is identified as "Auditing". The corresponding path segment number record and service behavior are jointly annotated to form a pairing structure between the path segment number, purpose field, and service behavior. Then, the pairing structure is numbered and grouped according to the path segment number sequence to ensure that all pairing structures are uniquely indexed. If the extraction result of the corresponding field of a path segment is incomplete, the field value must be supplemented by consulting the associated log in the original frozen status list, or the abnormal path registration table is called to match the field correction information in the path where the field registration failed. In this way, the required purpose field data of the path segment is supplemented. Finally, a unified set of pairing structures between all path segments, corresponding purpose fields, and service behaviors is output to obtain the path purpose field mapping result.

[0038] S502: Based on the path usage field mapping result, the registration status of the service behavior and usage field in the path segment in the permission attribution table is checked, and combinations where the behavior registration exists and the field is in a callable state are screened out to obtain a callable path permission matching result; First, extract the service behavior identifier and purpose field content corresponding to each path segment, and use the two contents as a joint composite key for subsequent permission verification operations. Then read the record entries in the permission attribution table in turn, locate the record row that is consistent with the service behavior field, and extract the behavior permission mark status field value in the row. Determine whether the behavior is registered or not using a binary mark as the judgment condition, where the status value "1" indicates that it has been registered, and the status value "0" indicates that it has not been registered. For example: if the service behavior is "audit", retrieve the corresponding status value of "audit" in the permission attribution table. If the value is "1", proceed to the next judgment step. Otherwise, mark the path segment combination as permission exception and exclude it. Then perform the same verification action on the purpose field, locate the field record in the permission attribution table by matching the field name, and read its corresponding callable status field value. If the status field If the value is "active" or "adjustable", the field is marked as callable. If the field status is "locked" or "frozen", it is marked as non-callable and the current combination is excluded. If the service behavior is registered and the purpose field is in the callable state, the path number, service behavior name, and purpose field name are paired and written into the temporary storage area to form a path segment combination that passes the permission check. In the example, the path number R6632 pairs the behavior "punch in" with the field "community service points", and the "punch in" behavior status is "1", and the "community service points" field status is "adjustable". Therefore, this combination will be written into the result list, and the above verification actions will be performed on all path mapping records in turn until all records are traversed. Finally, a set of path segment combinations that have passed the permission verification is constructed, and a callable path permission matching result is obtained.

[0039] S503: Based on the callable path permission matching result, the filtered path segments are written according to the corresponding purpose fields, and classified and recorded by fields to obtain an executable purpose list on the chain; First, read the path number, purpose field, and service behavior in each record in the dataset, and establish a field mapping index table one by one. Then, use the purpose field as the main classification identifier and aggregate and group the path numbers according to the fields to which they belong. Each purpose field corresponds to a set of path numbers as a grouping label. Then, perform a number sorting operation on the path numbers in each set of path numbers to ensure that the numbers are continuous or the sorting results based on the timestamp are consistent. In the example, if the purpose field is "user points synchronization", and its corresponding path segments are R103, R215, and R403, then these three numbers are aggregated and written under the "user points synchronization" label. At the same time, the path numbers R103, R215, and R403 are sorted in ascending order and written into the list record row. If there are multiple purpose fields, such as "user points synchronization", "equipment status calibration", and "abnormal log upload", the three sets of path sets are classified independently. , ensure that each path segment only belongs to the corresponding purpose field and remains isolated and non-intersecting in the record, and then construct the output header according to the field grouping results. Each field item is accompanied by its path number set content, and each record behavior is written in the form of field name + path number set pair. The example output is: "Field = User Points Synchronization, Path Segment Number = [R103, R215, R403]", and another example: "Field = Equipment Status Calibration, Path Segment Number = [R116, R118]". In this way, the record row content of all field dimensions is generated in sequence, and the service behavior in each record is mounted and labeled to ensure that the path segment number not only records its corresponding field, but also clarifies the service behavior name. For example, if the R103 path binding behavior is "points update", the output item also records "behavior = points update". Finally, the path segment behavior binding list under all field dimensions is constructed to obtain the executable purpose list on the chain.

[0040] See also Figure 2 , a blockchain-enabled community public welfare points management system, including: The usage attribution module obtains the usage declaration records of points generated by community reading registration, service check-in and fund incentives, extracts the field position and marks the behavior attribution content in combination with the task number to obtain a service behavior usage attribution list; The path binding module extracts the corresponding path number based on the purpose field in the service behavior purpose attribution list and checks the node status. After filtering out unregistered and closed paths, it pairs the enabled path number with the purpose field and writes it into the registration table to obtain the purpose path binding record table. The behavior identification module matches the behavior name with the path call status based on the service behavior records corresponding to the activated paths in the usage path binding record table, marks repeated path segments, and extracts abnormal segments into the cache to obtain a list of suspicious usage behavior paths; The frequency comparison module queries the call density identifier of the blockchain status index table based on the path number in the suspicious use behavior path list, compares the service type of the use field with the behavior range to screen the conflict frequency, obtains the frozen state use path list, and passes it to the call verification module; The verification module is called based on the unblocked path segments in the frozen state usage path list, extracts the usage fields and service behaviors, checks the registration status and writes the field records to obtain the executable usage list on the chain.

[0041] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. The community public welfare points management method enabled by blockchain is characterized by: The following steps are involved: S1: Based on the point usage declaration records generated from community reading registration, service check-in, and fund incentives, extract the field position and behavior label, and mark the field ownership with the task number to obtain a service behavior usage attribution list; S2: Based on the purpose field in the service behavior purpose attribution list, extract the corresponding path number and match the node status, filter out abnormal paths, match the enabled paths with the fields and register them, and obtain a purpose path binding record table; S3: Based on the service behavior corresponding to the activated path number in the usage path binding record table, extract the behavior name and match it with the path status, mark the repeated path and write it into the cache, extract the abnormal section, and obtain a list of suspicious usage behavior paths; S4: Based on the path number in the suspicious usage behavior path list, query the blockchain status index table for the corresponding call density identifier, extract the usage field, and compare the frequency of the behavior content with the density situation to obtain a frozen status usage path list; S5: Based on the unblocked path segments in the frozen state usage path list, the corresponding usage fields and service behaviors are extracted, and after checking the registration status of the authority ownership table, the path segments are written into the field records to obtain the executable usage list on the chain.

2. The blockchain-enabled community public welfare points management method according to claim 1 is characterized in that: The service behavior usage attribution list includes the behavior type label, service behavior content, task number field attribution, and usage field location. The usage path binding record table includes the usage field, enabled path number, and path usage matching result. The suspicious usage behavior path list includes the duplicate binding path cache, path call status, and abnormal paragraph mark. The frozen state usage path list includes the path segment number, call density identifier, and frequency conflict status result. The on-chain executable usage list includes the usage field, service behavior, permission registration information, and the callable state field.

3. The blockchain-enabled community public welfare points management method according to claim 1 is characterized in that: The specific steps of S1 are: S101: Obtain the point usage declaration records from community reading task registration, service check-in, and community fund incentive initiatives, extract the usage field location, locate the behavior source in the field content, map the record source and usage fields accordingly, distinguish the behavior record entries and field pairings based on the task code, and obtain the task behavior field mapping structure; S102: Based on the task behavior field mapping structure, extract the behavior type label identified in the record, identify the service behavior content that matches the label, match the extracted behavior name with the field mapping information, mark the behavior content and field attribution of the unique identification relationship, and obtain a behavior field attribution label set; S103: Based on the behavior field attribution label set, extract the binding sequence of the task number and the purpose field, integrate the field attribution label and the task number sequence, number and identify each attribution mark in the integration result and write it into the field configuration list in a classified manner to obtain the service behavior purpose attribution list.

4. The blockchain-enabled community public welfare points management method according to claim 1 is characterized in that: The specific steps of S2 are: S201: Based on the usage fields in the service behavior usage attribution list, extract the usage type corresponding to each field, call the path type configuration table to obtain the block path number associated with the usage type, exclude fields without associated paths, and obtain a usage field path number list; S202: Based on the path numbers listed in the path number list in the usage field, calling the path index table to read the node status of the path number, comparing the enabled state paths with the number of paths, calculating the path enabled ratio, and obtaining a set of enabled state path numbers; S203: Based on the reserved path number content in the enabled state path number set, extract the corresponding purpose field, pair the purpose field with the path number one by one and write it into the path purpose registration table to obtain a purpose path binding record table.

5. The blockchain-enabled community public welfare points management method according to claim 4 is characterized in that: The calculation formula for the path activation ratio is specifically: ； in, Represents the proportion of paths activated, Represents the total number of path numbers, Represents the path number The current enabled state in the path index table, Represents the path number The number of activations, Represents the path number Number of bans, Represents the path number The number of activations within the scheduling cycle, Represents the path number The number of disables during the scheduling period, Represents the path number The standard deviation in the enabled state, Represents the path number Offset in network traffic.

6. The blockchain-enabled community public welfare points management method according to claim 1 is characterized in that: The specific steps of S3 are: S301: extracting behavior names and path numbers based on the service behavior records corresponding to the activated path numbers in the usage path binding record table, pairing them one by one to form behavior path combinations, and sorting the paired items to obtain a path behavior pairing sequence; S302: Based on the path numbers and behavior names in the path-behavior pairing sequence, calculate the repetition distribution density of each behavior name in the corresponding path number, and express it as the ratio of the number of repetitions of the behavior name to the total number of behaviors with the number, to obtain a path-behavior density identifier set; S303: Based on the ratio data in the path behavior density identification set, the path numbers with different repeated distribution density values ​​are extracted, the corresponding segment information is called to compare the service behavior with the path status, and the path segments with abnormal combinations are extracted to obtain a list of suspicious use behavior paths.

7. The blockchain-enabled community public welfare points management method according to claim 6 is characterized in that: The calculation formula for the repeated distribution density of each behavior name in the corresponding path number is specifically: ； in, The representative path number is Name of the next line The repetitive distribution density, Represents the path number Name of China Bank The cumulative number of occurrences of Represents the path number Name of China Bank The cumulative number of occurrences of Representative behavior name In the sample with the path number The activity factor, Represents the path number The total number of behavior names in Represents the path number The middle behavior is the disturbance correction term of the frequency distribution, Representative behavior name distribution indicators.

8. The blockchain-enabled community public welfare points management method according to claim 1 is characterized in that: The specific steps of S4 are: S401: Based on the path segment number in the suspicious usage behavior path list, search for the entry with the corresponding number in the blockchain status index table, extract the call density identifier corresponding to each path segment, and pair it with the path number to obtain a path segment call density information set; S402: Based on the path segment call density information set, extract the usage fields associated with the path segment, query the service type and behavior range corresponding to each field, mark the correspondence between the path segment and the usage field, and obtain the usage field behavior range marking result; S403: Based on the result of the usage field behavior range marking and in combination with the usage status value in the density information, determine whether there is a call frequency conflict in the path segment, mark the conflicting path segment status as frozen and record the path number, and obtain a frozen status usage path list.

9. The blockchain-enabled community public welfare points management method according to claim 1 is characterized in that: The specific steps of S5 are: S501: Based on the unblocked path segments in the frozen usage path list, extract the usage fields and service behaviors associated with each path segment, and extract the corresponding content according to the path number to obtain a path usage field mapping result; S502: Based on the path usage field mapping result, the registration status of the service behavior and usage field in the path segment in the permission attribution table is checked, and combinations where the behavior registration exists and the field is in a callable state are screened out to obtain a callable path permission matching result; S503: Based on the callable path permission matching result, the filtered path segments are written according to the corresponding purpose fields, and classified and recorded by fields to obtain an executable purpose list on the chain.

10. A blockchain-enabled community public welfare points management system, characterized by: The system is used to implement the blockchain-enabled community public welfare points management method according to any one of claims 1 to 9, and the system includes: The usage attribution module obtains the usage declaration records of points generated by community reading registration, service check-in and fund incentives, extracts the field position and marks the behavior attribution content in combination with the task number to obtain a service behavior usage attribution list; The path binding module extracts the corresponding path number based on the purpose field in the service behavior purpose attribution list and checks the node status. After filtering out unregistered and closed paths, the enabled path number is paired with the purpose field and written into the registration table to obtain a purpose path binding record table. The behavior identification module matches the behavior name with the path call status based on the service behavior record corresponding to the activated path in the usage path binding record table, marks the repeated path segments and extracts the abnormal segments into the cache to obtain a list of suspicious usage behavior paths; The frequency comparison module queries the call density identifier of the blockchain status index table based on the path number in the suspicious use behavior path list, compares the service type of the use field with the behavior range to screen the conflict frequency, obtains the frozen state use path list, and passes it to the call verification module; The call verification module extracts the usage fields and service behaviors based on the unblocked path segments in the frozen usage path list, checks the registration status and writes the field records to obtain the executable usage list on the chain.

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