School affairs intelligent management method and system based on 5g core network

Abstract

The application discloses a school affair intelligent management method and system based on a 5G core network, relates to the technical field of school affair intelligent management, and specifically comprises the following steps: state data transmitted through different paths of the 5G core network is analyzed and processed, a constraint sequence is generated in combination with school affair states, reverse mapping of a generated sequence and an arrival order is performed, and whether there is an arrival time difference of state data of a same school affair object through different paths of the 5G core network is judged; in the case that there is an arrival time difference, the state data with the arrival time difference is continuously evolved and analyzed, and a discrete evolution sequence is constructed according to a reverse mapping result. The application solves the problem that state data timing mismatch caused by multi-path transmission based on the 5G core network cannot be corrected in consistency according to a discrete degree, realizes state data sequence reconstruction and dynamic regulation and control, and improves school affair management data accuracy and decision reliability.

Description

Technical Field

[0001] This invention relates to the field of intelligent school administration technology, specifically to a method and system for intelligent school administration based on a 5G core network. Background Technology

[0002] Smart campus management based on the 5G core network refers to a management model that leverages the high bandwidth, low latency, and network slicing capabilities provided by the 5G core network (5GC) to achieve integrated and intelligent operation of teaching, management, and services through real-time collection, transmission, processing, and decision-making of various business data within the campus. Essentially, it deeply embeds communication network capabilities into campus management processes, thereby improving management efficiency and response speed. In existing technologies, such systems typically collect campus-related data by deploying 5G-enabled terminal devices (such as smart attendance terminals, video surveillance equipment, and environmental sensors) on campus, and then connect to the 5G core network via the 5G wireless access network. Within the core network, data is processed according to different... By segmenting business types into network slices and scheduling service quality, teaching data, security data, and administrative data can be logically isolated and transmitted efficiently. Subsequently, through collaboration with edge computing nodes (MEC), some data with high real-time requirements are preprocessed and analyzed at the network edge. The processing results or summarized data are then uploaded to the cloud-based school management platform for unified storage, integrated analysis, and intelligent decision-making. Finally, the management platform issues control commands or early warning information to relevant terminals or systems, thus forming a complete closed-loop process including data collection, 5G transmission, network slice scheduling, edge computing processing, cloud analysis and decision-making, and result feedback and execution, realizing real-time, refined, and intelligent school management.

[0003] The existing technology has the following shortcomings: In the process of realizing intelligent school administration based on the 5G core network, when the status data of the same school administration object enters the school administration management system through the 5G core network via different transmission paths and is used to combine to form a complete status, the differences in resource scheduling and link load of different paths will lead to inconsistencies in the data transmission latency of status data. This will cause some data generated earlier to arrive later and some data generated later to arrive earlier, resulting in cross-time status splicing in the status combination process. Existing technology cannot correct the data consistency during status combination based on the dispersion of the arrival time difference of the status data of the same school administration object through different paths of the 5G core network. The reason is that existing technology assumes that the status data has a unified time base and directly performs combination processing, which will cause the system to construct a status result that does not conform to reality, thereby distorting the basis for school administration decision-making and affecting the accuracy of management execution.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a smart school administration method and system based on a 5G core network to solve the problems mentioned in the background.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a smart school administration method based on a 5G core network, specifically including the following steps: S1. Process the status data generated by the same school affairs object, and fuse and bind the status generation sequence, path identifier and generation trigger feature to construct a school affairs status generation constraint sequence, and embed the school affairs status generation constraint sequence into the status data. S2. Parse and process the status data transmitted through different paths of the 5G core network, combine the school status generation constraint sequence to perform the reverse mapping between the generation sequence and the arrival order, and determine whether there is a difference in arrival time for the status data of the same school object through different paths of the 5G core network. S3. In the case of arrival time differences, perform continuous evolution analysis on the state data with arrival time differences, and construct a discrete evolution sequence based on the reverse mapping results to determine the degree of dispersion of the state data of the same school affairs object when there are arrival time differences through different paths of the 5G core network. S4. Call the school affairs status to generate a constraint sequence and a discrete evolution sequence, perform rearrangement processing on the state data, correct the data consistency when combining states according to the degree of discreteness, and form a school affairs status reconstruction sequence; S5. Combining the change trajectory of the discrete evolution sequence and the school affairs state reconstruction sequence, adaptively adjust the school affairs state generation constraint sequence and state rearrangement processing to complete the dynamic control of the state combination process.

[0007] Preferably, S1 is as follows: The status data generated by the same school affairs object is processed. The status data is parsed according to the preset field structure, and the generation time information used to represent the order of status generation is extracted, the transmission path source information used to represent the path identifier is extracted, the trigger source information used to represent the generation trigger feature is extracted, and the status data is reorganized according to a unified field order. Sequential encoding is performed on the generation time information to form the generation sequence relationship of the status; path encoding is performed on the transmission path source information to form the path identifier; trigger encoding is performed on the trigger source information to form the generation trigger feature; and the generation sequence relationship of the status, the path identifier and the generation trigger feature are fused and bound according to the preset arrangement order to construct the school status generation constraint sequence. Write the constraint sequence for generating school status into the specified field position of the corresponding status data, and complete the embedding association between the constraint sequence for generating school status and the status data through field mapping relationship.

[0008] Preferably, S2 specifically includes the following steps: S201. Parse and process the status data transmitted through different paths of the 5G core network, read the school affairs status generation constraint sequence in the status data according to the field structure, extract the status generation sequence relationship, path identifier and generation trigger feature, and synchronously record the actual arrival order of the status data to form a status data set containing the generation sequence and arrival order. S202. After completing the construction of the state data set, combine the school affairs state generation constraint sequence to perform the reverse mapping between the generation sequence and the arrival order. By aligning the state generation sequence with the actual arrival order item by item, establish the correspondence sequence between the generation sequence and the arrival order, and mark the order deviation position in the correspondence sequence. S203. Based on the position of the order deviation in the corresponding relationship sequence, perform a consistency comparison on the status data of the same school affairs object. When there is a situation where the order of status generation is inconsistent with the order of arrival, it is determined that the status data of the same school affairs object has different arrival times through different paths of the 5G core network.

[0009] Preferably, S202 specifically refers to: For each state data in the state data set, extract the state generation sequence and the actual arrival sequence, sort them according to the state generation sequence to form a generation sequence, and sort them according to the actual arrival sequence to form an arrival sequence. After the generation sequence and arrival sequence are constructed, the reverse mapping between the generation sequence and arrival sequence is performed by combining the school status generation constraint sequence. By matching the status data of the corresponding position in the arrival sequence item by item with the generation sequence as the benchmark, a sequence of correspondences between the generation sequence and the arrival sequence is established, and the sequence number difference in each correspondence is recorded. The state data is subjected to order deviation determination based on the sequence number difference in the corresponding relation sequence. When the sequence number difference is not zero, the state data is marked with the order deviation position in the corresponding relation sequence, and the marking result is written into the corresponding relation sequence.

[0010] Preferably, S3 specifically includes the following steps: S301. In the case of arrival time differences, perform continuous evolution analysis on the state data with arrival time differences, extract the corresponding state data according to the order deviation position in the corresponding relationship sequence, and arrange the extracted state data in order according to the order of state generation to form a continuous state evolution sequence. S302. For the state data in the continuous state evolution sequence, extract the sequence number difference corresponding to each state data according to the reverse mapping result, and arrange the sequence number differences in order according to the order of state generation to construct a discrete evolution sequence. S303. Based on the arrangement and distribution of the sequence number difference in the discrete evolution sequence, the sequence number difference is divided into intervals and its distribution is statistically analyzed. The degree of dispersion is graded and judged according to the range of change of the sequence number difference within the interval, so as to determine the degree of dispersion when the status data of the same school affairs object has different arrival time differences through different paths of the 5G core network.

[0011] Preferably, S303 is as follows: Based on the arrangement and distribution of the index differences in the discrete evolution sequence, the index differences are segmented according to the preset interval division standard, dividing the index differences in the discrete evolution sequence into continuous intervals, and performing distribution statistics on the index differences in each continuous interval to form an interval distribution set; After the interval distribution set is constructed, the range of the index difference in each continuous interval is extracted to obtain the maximum and minimum values ​​of the index difference in each continuous interval. Then, the range of each interval is serialized and arranged according to the order of the continuous intervals in the discrete evolution sequence to form the interval change sequence. Based on the range of changes in the sequence number difference of each continuous interval in the interval change sequence, the distribution state of the sequence number difference in the discrete evolution sequence is classified into levels. When the range of changes in the sequence number difference is a single position offset, it is determined to be low dispersion. When the range of changes in the sequence number difference is a continuous offset of multiple adjacent positions, it is determined to be medium dispersion. When the range of changes in the sequence number difference is a non-continuous offset across intervals, it is determined to be high dispersion. Based on the dispersion classification results, the dispersion of the status data of the same school affairs object when there are differences in arrival time through different paths of the 5G core network is determined.

[0012] Preferably, S4 is as follows: The system calls the school status generation constraint sequence and discrete evolution sequence, performs association matching processing on the status data, and binds the status generation sequence in the school status generation constraint sequence with the sequence number difference in the discrete evolution sequence to form a set of status data generation order and offset relationship. After the set of generation order and offset relationship is constructed, the state data is rearranged. The state data is sorted according to the order of state generation in the school affairs state generation constraint sequence. The position of the state data with order offset is adjusted according to the sequence difference in the discrete evolution sequence so that the arrangement order of the state data is consistent with the order of state generation. After the state data rearrangement is completed, the data consistency during state combination is corrected according to the degree of dispersion. When the degree of dispersion is low, the state data is directly combined in the rearranged order. When the degree of dispersion is medium, the state data with continuous sequential offset is locally rearranged according to the constraint sequence generated by the school administration state before being combined. When the degree of dispersion is high, the state data with cross-interval sequential offset is divided into multiple sub-sequences according to the discrete evolution sequence, rearranged separately, and then combined in segments. The sub-sequences are then sequentially spliced ​​according to the constraint sequence generated by the school administration state to form the school administration state reconstruction sequence.

[0013] Preferably, S5 is as follows: By combining the change trajectory of the discrete evolution sequence and the school affairs status reconstruction sequence, the change trend of the sequence difference in the discrete evolution sequence within the continuous interval is extracted, and the offset between the arrangement order of the state data and the order of state generation in the school affairs status reconstruction sequence is compared. The change trend of the sequence difference is correlated with the arrangement offset of the state data to form a set of associated data between the change trajectory and the reconstruction sequence. After the associated data set between the change trajectory and the reconstruction sequence is constructed, the order of state generation in the school affairs state generation constraint sequence is adjusted, the position adjustment parameters in the state rearrangement process are updated, the arrangement weight of the order of state generation is corrected according to the trend of the sequence difference, and the position adjustment magnitude is adaptively corrected according to the state data arrangement offset, so as to complete the dynamic control of the state combination process.

[0014] The preferred campus smart management system based on the 5G core network includes a state constraint module, a difference determination module, a discrete analysis module, a consistency correction module, and a dynamic control module. The state constraint module processes the state data generated by the same school affairs object, integrates and binds the state generation sequence, path identifier and generation trigger features to construct a school affairs state generation constraint sequence, and embeds the school affairs state generation constraint sequence into the state data. The difference determination module parses and processes the status data transmitted through different paths of the 5G core network, and combines the school status generation constraint sequence to perform the reverse mapping between the generated sequence and the arrival order to determine whether there is a difference in arrival time for the status data of the same school object through different paths of the 5G core network. The discrete analysis module performs continuous evolution analysis on state data with arrival time differences, and constructs a discrete evolution sequence based on the reverse mapping results to determine the degree of dispersion of the state data of the same school affairs object when there are arrival time differences through different paths of the 5G core network. The consistency correction module calls the constraint sequence and discrete evolution sequence generated by the school status, performs rearrangement processing on the status data, corrects the data consistency when combining states according to the degree of dispersion, and forms the school status reconstruction sequence. The dynamic control module, by combining the change trajectory of the discrete evolution sequence and the school affairs state reconstruction sequence, adaptively adjusts the school affairs state generation constraint sequence and state rearrangement processing to complete the dynamic control of the state combination process.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention introduces a constraint sequence for generating school affairs status, establishing a generation sequence identifier for the status data of the same school affairs object during the generation stage, independent of the transmission path. In subsequent processing, this generation sequence is used as a unified benchmark. Combined with the actual arrival order after transmission through different paths of the 5G core network, reverse mapping analysis is performed, thereby accurately identifying the arrival time differences caused by multi-path transmission. Furthermore, by constructing a discrete evolution sequence, the time differences are continuously modeled and hierarchically characterized, so that the order disturbance of status data during transmission is no longer coarsely processed with a single timestamp, but is transformed into discrete change information with structured characteristics. This enables refined identification and quantitative expression of the timing mismatch problem caused by multi-path transmission, avoiding the status splicing distortion problem caused by the default unified time benchmark in the prior art.

[0016] 2. This invention calls upon the school administration status generation constraint sequence and discrete evolution sequence to perform hierarchical rearrangement and consistency correction processing on the status data. Different processing strategies, such as direct combination, local rearrangement, and segmented reconstruction, are adopted according to different degrees of dispersion. Furthermore, the state generation constraint sequence and rearrangement parameters are adaptively adjusted by combining the change trajectory of the discrete evolution sequence and the school administration status reconstruction sequence. This enables the state combination process to have dynamic control capabilities, thereby not only restoring the true generation order of the status data but also continuously optimizing the state combination results under complex network environment changes. This ensures a balance between real-time performance and accuracy in school administration data, improving the reliability of decision-making basis and the stability of execution results in the process of intelligent school administration management. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a schematic diagram of the process of the present invention.

[0019] Figure 2This is a schematic diagram of the modules of the present invention. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] This invention provides, for example Figure 1 The intelligent campus management method based on the 5G core network shown includes the following steps: S1. Process the status data generated by the same school affairs object, and fuse and bind the status generation sequence, path identifier and generation trigger feature to construct a school affairs status generation constraint sequence, and embed the school affairs status generation constraint sequence into the status data. In this embodiment, S1 specifically refers to: The status data generated by the same school affairs object is processed. The status data is parsed according to the preset field structure, and the generation time information used to represent the order of status generation is extracted, the transmission path source information used to represent the path identifier is extracted, the trigger source information used to represent the generation trigger feature is extracted, and the status data is reorganized according to a unified field order. Processing status data generated by the same school affairs object can be achieved by uniformly collecting data from the data source and performing field parsing. For example, in a campus scenario, the same school affairs object can be a student, whose status data can include attendance records, location change records, or behavior trigger records. When the data enters the processing flow, the raw data is first split according to a preset field structure, dividing the raw data into time fields, path fields, and trigger fields. The generation time information can be obtained by the timestamp recorded by the terminal device and sorted and marked according to time sequence. The transmission path source information can be extracted and encoded by the path identifier or access node identifier in the 5G core network. The trigger source information can be classified and marked by the event trigger type, such as attendance trigger or abnormal behavior trigger. Then, these information are arranged according to a unified field order, such as generation time information, path identifier, and trigger source information, and recombined to form a new status data structure. This ensures that data from different sources have a unified structure and clear semantics when entering the subsequent processing stage, thus providing a computable basis for subsequent time difference judgment and consistency correction.

[0022] The same school affairs object refers to a management entity with a unique identifier in the school affairs management process, such as a student or a teacher. Status data is a collection of descriptive information generated around this entity at different points in time. Preset field structure refers to the field composition form and arrangement specification defined before data processing to ensure that different data have a consistent data format. Generation time information, which is used to characterize the order of status generation, refers to the field that records the time when the status is generated. Its function is to reflect the time sequence between statuses. Transmission path source information, which is used to characterize the path identifier, refers to the identifier content describing the data through different transmission paths of the 5G core network to distinguish different path sources. Trigger source information, which is used to characterize the generation trigger feature, refers to the field that records the reason for the status generation, such as being triggered by attendance actions or by monitoring behaviors. Unified field order refers to arranging various types of information in a fixed order to form a standardized structure. Reorganization refers to recombining the fields into new data units after completing field extraction and arrangement, so that the status data has structural consistency and parsability.

[0023] Sequential encoding is performed on the generation time information to form the generation sequence relationship of the status; path encoding is performed on the transmission path source information to form the path identifier; trigger encoding is performed on the trigger source information to form the generation trigger feature; and the generation sequence relationship of the status, the path identifier and the generation trigger feature are fused and bound according to the preset arrangement order to construct the school status generation constraint sequence. Sequential encoding of generation time information can be achieved by sorting multiple status data generated by the same school affairs object within a continuous time period and assigning them incremental numbers. For example, earlier generated data can be marked with smaller numbers, and later generated data can be marked with larger numbers, thus forming a relationship between the order of status generation. Path encoding of transmission path source information can be achieved by extracting access node identifiers, core network path identifiers, or service flow identifiers and mapping them to fixed code values ​​to distinguish different transmission path sources. Trigger encoding of trigger source information can be achieved by classifying and mapping trigger event types. For example, attendance triggers, location change triggers, and abnormal behavior triggers can be assigned different codes. After completing the three types of encoding, the three types of encoding are concatenated according to a preset arrangement order. For example, first arrange the sequential encoding, then the path encoding, and finally the trigger encoding. By concatenating fields or combining bits, a unified data unit is formed, achieving fusion and binding, and thus constructing a constraint sequence for school affairs status generation. This transforms the originally scattered time, path, and trigger information into a structured sequence for subsequent unified characterization and constraint of time differences generated by cross-path transmission.

[0024] Generation time information refers to the field that records the time when a state is generated, reflecting the chronological order of state occurrences. Sequence encoding converts the temporal sequence into a comparable numbering format, thus establishing the chronological order of state generation. Transmission path source information describes the identification content of the path data traverses in the 5G core network. After being converted into a unified number through path encoding, it forms a path identifier, used to distinguish different path sources. Trigger source information records the reason for state generation. After being converted into a standardized identifier through trigger encoding, it forms a generation trigger feature, used to distinguish different trigger types. Preset arrangement order refers to the rule of arranging sequence encoding, path encoding, and trigger encoding in a fixed order. Fusion binding refers to combining multiple types of encoding into a unified structure according to the arrangement order. The school status generation constraint sequence is a serialized data structure formed by combining sequence encoding, path identifier, and generation trigger feature, used to uniformly constrain the generation logic and transmission attributes of status data.

[0025] Write the constraint sequence for generating school status into the specified field position of the corresponding status data, and complete the embedding association between the constraint sequence for generating school status and the status data through field mapping relationship.

[0026] Writing the school status generation constraint sequence to a specified field position in the corresponding status data can be achieved by pre-allocating a dedicated storage area in the status data structure. For example, a fixed field can be reserved in each status data entry to store sequence information. After the school status generation constraint sequence is constructed, the sequence is written to this field according to a preset encoding format. At the same time, a field mapping relationship is established to indicate the corresponding position of each part of the sequence in the status data. For example, the sequence code is mapped to the time field position, the path identifier is mapped to the path field position, and the generation trigger feature is mapped to the trigger field position. During the writing process, field overwriting or field expansion can be used to complete the embedding. For example, a sequence field can be appended to the original data structure or a subfield can be expanded in the original field. In this way, it can be ensured that each status data entry carries the generation constraint information at the time of storage, so that the sequence content can be directly parsed without additional lookup in the subsequent processing stage, thereby improving the continuity and consistency of data processing.

[0027] The school status generation constraint sequence is a structured sequence formed by combining the status generation sequence, path identifiers, and generation trigger features. It is used to describe the generation logic and transmission attributes of status data. The specified field position corresponding to the status data refers to the field area specifically allocated in the status data structure for storing sequence information. The field mapping relationship refers to the correspondence rules between each component in the sequence and each field in the status data, which is used to clarify the storage location of different types of information. Embedded association refers to binding the school status generation constraint sequence and the status data in the same data structure, so that the sequence information and the original status information form an integrated data unit, thereby ensuring that the status content and its constraint information can be obtained simultaneously in subsequent processing.

[0028] S2. Parse and process the status data transmitted through different paths of the 5G core network, combine the school status generation constraint sequence to perform the reverse mapping between the generation sequence and the arrival order, and determine whether there is a difference in arrival time for the status data of the same school object through different paths of the 5G core network. In this embodiment, S2 specifically includes the following steps: S201. Parse and process the status data transmitted through different paths of the 5G core network, read the school affairs status generation constraint sequence in the status data according to the field structure, extract the status generation sequence relationship, path identifier and generation trigger feature, and synchronously record the actual arrival order of the status data to form a status data set containing the generation sequence and arrival order. Parsing and processing status data transmitted through different paths via the 5G core network can be achieved by performing structured reading and sequential recording on the data entering the processing flow. For example, for data streams generated by the same school affairs object on different terminals and uploaded through different paths, when the data arrives, the status data content is first parsed one by one according to the predefined field structure. From this, the school affairs status is read to generate a constraint sequence, and the status generation sequence, path identifier, and generation trigger feature are extracted from the sequence. At the same time, at the data receiving end, each status data is assigned an arrival sequence identifier according to the receiving time, such as numbering according to the order in which it enters the processing queue. Then, the extracted status generation sequence is arranged according to its encoding order to form a generation sequence, and the arrival sequence identifier is arranged according to the receiving order to form an arrival order. In this way, the original scattered data can be transformed into an information set that has both generation logic and transmission order. For example, when the behavioral data of a student generated on different devices arrives through different paths, the actual generation order and receiving order can be clearly distinguished, thus providing basic data support for subsequent order deviation analysis.

[0029] The status data transmitted through different paths in the 5G core network refers to the data set of status information of the same school affairs object entering the processing flow through different access nodes or different network paths. Parsing and processing refers to the process of splitting and reading the status data according to the preset field structure. The field structure refers to the arrangement form and field division rules of various types of information within the status data. The school affairs status generation constraint sequence in the status data refers to the serialized identification information attached to the status data, which is used to describe the status generation logic. The status generation sequence relationship is the data generation sequence information represented by sequential encoding. The path identifier is the encoded information used to distinguish the source of different transmission paths. The generation trigger feature is the classification information used to identify the cause of status generation. The actual arrival order refers to the order in which the status data is received when it enters the processing flow. The status data set containing the generation sequence and the arrival order refers to the combined data structure that simultaneously contains generation order information and receiving order information, which is used to support the subsequent comparative analysis of the generation sequence and the arrival order.

[0030] S202. After completing the construction of the state data set, combine the school affairs state generation constraint sequence to perform the reverse mapping between the generation sequence and the arrival order. By aligning the state generation sequence with the actual arrival order item by item, establish the correspondence sequence between the generation sequence and the arrival order, and mark the order deviation position in the correspondence sequence. S203. Based on the position of the order deviation in the corresponding relationship sequence, perform a consistency comparison on the status data of the same school affairs object. When there is a situation where the order of status generation is inconsistent with the order of arrival, it is determined that the status data of the same school affairs object has different arrival times through different paths of the 5G core network.

[0031] Consistency comparison of the status data of the same school affairs object based on the order deviation position in the correspondence sequence can be achieved by comparing the original arrangement order of all status data in the generation sequence with the actual arrangement order in the arrival sequence item by item. In specific implementation, firstly, the marked order deviation positions in the correspondence sequence are traversed. For each status data with deviation, its position in the generation sequence and its position in the arrival sequence are read. Then, an overall consistency check is performed on all status data of the same school affairs object. When any status data has an inconsistency between the generation position and the arrival position, it can be determined that the status data of the school affairs object has an order disturbance during transmission, thereby further identifying a difference in arrival time. For example, if a school affairs object has multiple status data, with the generation order being first, second, and third, and the arrival order being second, third, and first, then the deviation position of the first status data is marked in the correspondence sequence. By performing a consistency comparison on this deviation position, it can be confirmed that there is an inconsistency between the generation order and the arrival order. Thus, it can be determined that the data has a time difference when transmitted through different paths of the 5G core network. This determination process can directly reflect the time misalignment caused by the difference in transmission paths from the sequence relationship.

[0032] The positional deviation in the correspondence sequence refers to the set of state data positions marked by changes in position after establishing a correspondence between the generation sequence and the arrival sequence. Consistency comparison refers to the process of conducting an overall comparative analysis of all state data of the same school object between the generation sequence and the arrival sequence. Inconsistency between the state generation sequence and the arrival sequence means that there is a difference between the arrangement order of state data at the time of generation and the arrangement order at the time of reception. The 5G core network is a network environment involving multi-path scheduling and forwarding during the state data transmission process. Arrival time difference refers to the phenomenon that the order of state data changes at the receiving end due to the transmission delay difference of different paths. These factors together constitute the basis for judging whether there is a cross-path transmission time difference in state data.

[0033] In this embodiment, S202 specifically refers to: For each state data in the state data set, extract the state generation sequence and the actual arrival sequence, sort them according to the state generation sequence to form a generation sequence, and sort them according to the actual arrival sequence to form an arrival sequence. Extracting the generation sequence and actual arrival order of each state data entry in a state dataset can be achieved through a two-dimensional sorting process. Specifically, firstly, the generation sequence code and the actual arrival order identifier recorded during the receiving process are read from each data entry. Then, all state data are sorted based on the generation sequence code, placing data with smaller codes first and larger codes last, thus forming a generation sequence. Simultaneously, the same batch of state data is sorted again based on the actual arrival order identifier, placing earlier arriving data first and later arriving data last, thus forming an arrival order sequence. For example, if a school administration object generates three state data entries, their generation order would be: first, second, and third. Third, if the second data arrives first, the third data arrives second, and the first data arrives last due to path differences during transmission, the generated sequence will be arranged as first, second, and third, while the arrival order sequence will be arranged as second, third, and first. This dual-sequence construction can intuitively reflect the difference between the generation logic and the transmission order. The status data set refers to the collection of multiple status data for the same school affairs object. The status generation order refers to the data generation time order represented by the encoding form. The actual arrival order refers to the order in which the status data enters the processing flow at the receiving end. The generation sequence is the sequence structure formed by sorting according to the status generation order, and the arrival order sequence is the sequence structure formed by sorting according to the actual arrival order. The two types of sequences together constitute the data basis for subsequent order deviation analysis.

[0034] After the generation sequence and arrival sequence are constructed, the reverse mapping between the generation sequence and arrival sequence is performed by combining the school status generation constraint sequence. By matching the status data of the corresponding position in the arrival sequence item by item with the generation sequence as the benchmark, a sequence of correspondences between the generation sequence and the arrival sequence is established, and the sequence number difference in each correspondence is recorded. After the generation sequence and arrival sequence are constructed, a reverse mapping between them can be achieved by establishing an index mapping table. Specifically, the order of the state data in the generation sequence is used as a benchmark. Each state data point is numbered in its position within the generation sequence. Then, the corresponding position of the same state data is found in the arrival sequence. The positional difference between the same state data in the two sequences is determined through item-by-item matching, establishing a one-to-one correspondence between the generated sequence position and the arrival sequence position. This forms a sequence of correspondences between the generation sequence and the arrival sequence. The difference in the sequence number between each pair of positions is also recorded. For example, if a school administration object has three state data points, arranged as first, second, and third in the generation sequence, and second, third, and first in the arrival sequence, then the first state data point is positioned first in the generation sequence and second in the arrival sequence. The third step involves using the sequence number difference in the correspondence to represent the positional offset. By performing this matching process on all state data, the sequence offset caused during transmission can be fully characterized, thus providing a quantitative basis for subsequent sequence deviation identification. The reverse mapping between the generated sequence and the arrival sequence refers to using the generated sequence as a reference to reverse locate the corresponding positional relationship in the arrival sequence. Using the generated sequence as a benchmark means comparing the sequence of state generation as the main sequence. Item-by-item matching means searching for and matching the position of each state data one by one. The state data that reaches the corresponding position in the sequence refers to the element in the arrival sequence that corresponds to the same state data in the generated sequence. The correspondence sequence between the generated sequence and the arrival sequence refers to the sequence structure composed of the positional correspondence of the same data in the two sequences. The sequence number difference refers to the difference in the position number of the same state data in the two sequences, which is used to characterize the degree of sequence offset.

[0035] The state data is subjected to order deviation determination based on the sequence number difference in the corresponding relation sequence. When the sequence number difference is not zero, the state data is marked with the order deviation position in the corresponding relation sequence, and the marking result is written into the corresponding relation sequence.

[0036] Determining the order deviation of state data based on the sequence difference in the corresponding relationship sequence can be achieved by comparing the positional differences of each state data item in the generation sequence and the arrival sequence item by item. Specifically, it iterates through each pair of positional relationships in the corresponding relationship sequence, reads the position number of the same state data in the generation sequence and the position number in the arrival sequence, and calculates the sequence difference between them. When the sequence difference is not zero, it indicates that the state data has undergone an order change during transmission. In this case, the original position of the state data in the generation sequence is used as the reference position, and its actual position in the arrival sequence is used as the offset position. The order deviation position identifier of the state data is recorded in the corresponding relationship sequence, for example, in the form of a position index pair as "generation position - arrival position". The identifier result is also written into the corresponding relationship sequence. The corresponding data entries in the relational sequence are used to form an extended sequence containing deviation information. For example, if a certain state data is first in the generation sequence and third in the arrival sequence, then a deviation is determined, and the corresponding deviation position identifier is recorded. By performing this determination process on all state data, the order changes caused by different path transmissions can be fully identified. The order deviation determination refers to the process of identifying whether the position of state data has changed in different sequences. A non-zero sequence number difference indicates that there is a positional difference between the generation sequence and the arrival sequence. The order deviation position refers to the specific positional difference of the state data in the generation sequence and the arrival sequence. The order deviation position identifier is the recorded information used to describe this difference. The identifier result is the specific record of the deviation of each state data, which is used for subsequent analysis and processing.

[0037] S3. In the case of arrival time differences, perform continuous evolution analysis on the state data with arrival time differences, and construct a discrete evolution sequence based on the reverse mapping results to determine the degree of dispersion of the state data of the same school affairs object when there are arrival time differences through different paths of the 5G core network. In this embodiment, S3 specifically includes the following steps: S301. In the case of arrival time differences, perform continuous evolution analysis on the state data with arrival time differences, extract the corresponding state data according to the order deviation position in the corresponding relationship sequence, and arrange the extracted state data in order according to the order of state generation to form a continuous state evolution sequence. Continuous evolution analysis of state data with arrival time differences can be achieved by extracting segments and reconstructing timelines from data with order deviations. Specifically, the marked order deviation positions are first read from the correspondence sequence. The corresponding state data is then extracted based on the index of each deviation position in the generation sequence. Multiple state data with order deviations are divided into several continuous segments according to the continuity of the indices. The state data within each segment is then reordered based on the order of state generation to restore the data to its true generation order. Furthermore, the continuous segments are concatenated according to their generation order to form a continuous state evolution sequence. For example, if a school administration object has multiple state data points, some of which are out of order during transmission, extracting these misaligned data and rearranging them according to their generation order can construct a continuous sequence reflecting the actual state change process, thus providing a basis for subsequent discreteness analysis of continuous change trajectories.

[0038] State data with arrival time differences refers to a set of data whose positions are inconsistent in the generation sequence and arrival sequence. Continuous evolution analysis processing refers to the process of constructing the time change trajectory of these data and restoring the sequence. The order deviation position in the correspondence sequence refers to the position marker that records the offset of the state data between the generation sequence and the arrival sequence. The corresponding state data refers to the specific data item that corresponds one-to-one with the order deviation position. The state generation sequence is the encoded information used to describe the order of state generation. The continuous state evolution sequence is the state data sequence that is rearranged and concatenated according to the state generation sequence. This sequence is used to describe the continuous process of state change over time, thus providing a basic data structure for subsequent analysis.

[0039] S302. For the state data in the continuous state evolution sequence, extract the sequence number difference corresponding to each state data according to the reverse mapping result, and arrange the sequence number differences in order according to the order of state generation to construct a discrete evolution sequence. Extracting the sequence difference of state data in a continuous state evolution sequence can be achieved by mapping the positional relationship of each state data in the generation sequence and the arrival sequence. Specifically, each state data in the continuous state evolution sequence is first traversed, and its position number in the generation sequence and its position number in the arrival sequence are obtained based on the reverse mapping result. Then, the difference between the two position numbers is calculated to form the corresponding sequence difference. Subsequently, all sequence differences are sorted according to the order of state generation, so that the sequence differences form a continuous distribution structure according to the actual generation order of the states, thereby constructing a discrete evolution sequence. For example, a certain school object contains three state data in a continuous state evolution sequence, whose positions in the generation sequence are first, second, and third, while their positions in the arrival sequence are third, first, and second. The corresponding sequence differences reflect the shift changes. By arranging these differences according to the generation order, a discrete evolution sequence reflecting the trend of sequence perturbation can be formed for subsequent discreteness analysis.

[0040] In a continuous state evolution sequence, state data refers to the set of data rearranged according to the order of state generation. The reverse mapping result refers to the data structure that records the positional correspondence between state data in the generation sequence and the arrival sequence. The sequence difference refers to the difference between the position number of the same state data in the generation sequence and the position number in the arrival sequence, which is used to characterize the degree of sequence offset. The discrete evolution sequence refers to the sequence structure formed by arranging the sequence difference according to the order of state generation, which is used to describe the distribution of sequence offset over time. This sequence can reflect the discrete influence characteristics of different path transmission on the state order.

[0041] S303. Based on the arrangement and distribution of the sequence number difference in the discrete evolution sequence, the sequence number difference is divided into intervals and its distribution is statistically analyzed. The degree of dispersion is graded and judged according to the range of change of the sequence number difference within the interval, so as to determine the degree of dispersion when the status data of the same school affairs object has different arrival time differences through different paths of the 5G core network.

[0042] In this embodiment, S303 specifically refers to: Based on the arrangement and distribution of the index differences in the discrete evolution sequence, the index differences are segmented according to the preset interval division standard, dividing the index differences in the discrete evolution sequence into continuous intervals, and performing distribution statistics on the index differences in each continuous interval to form an interval distribution set; Segmentation based on the arrangement and distribution of index differences in a discrete evolution sequence can be achieved by continuously scanning and dividing the index difference sequence into intervals. Specifically, each index difference is first read sequentially according to its arrangement in the discrete evolution sequence. The variation range between adjacent index differences is compared according to a preset interval division standard. When the variation between adjacent index differences is within a preset range, they are grouped into the same continuous interval; when the variation exceeds the preset range, they are divided into new continuous intervals, thus completing the segmentation process. Subsequently, the distribution of index differences within each continuous interval is statistically analyzed, such as the frequency of occurrence, value range, and trend of index differences within the interval. This ultimately forms an interval distribution set. For example, if the index differences of a certain school object are arranged into several continuous variation segments, the portion with smaller variations between adjacent differences is divided into a continuous interval, while the location of abrupt changes is divided into a new interval. By statistically analyzing the data distribution within each interval, the sequence shift characteristics at different stages can be clearly characterized, thus providing a basis for subsequent discreteness division.

[0043] The arrangement and distribution of index differences in a discrete evolution sequence refers to the overall change structure of index differences after they are arranged according to the order of their state generation. The preset interval division standard is the criterion used to determine whether adjacent index differences belong to the same continuous interval. Segmentation processing is the process of dividing the index difference sequence into multiple intervals according to the criterion. A continuous interval is a set of index differences that are arranged continuously under the condition that the change amplitude meets the requirements. Distribution statistics is the process of statistically analyzing the number, range, and change characteristics of index differences in each continuous interval. The interval distribution set is a data set composed of multiple continuous intervals and their corresponding statistical results, used to describe the distribution state of index differences in different intervals.

[0044] After the interval distribution set is constructed, the range of the index difference in each continuous interval is extracted to obtain the maximum and minimum values ​​of the index difference in each continuous interval. Then, the range of each interval is serialized and arranged according to the order of the continuous intervals in the discrete evolution sequence to form the interval change sequence. After the interval distribution set is constructed, range extraction can be achieved by scanning and extracting the boundaries of the index differences within each continuous interval. Specifically, for each continuous interval, all index differences within that interval are traversed sequentially. The index difference with the largest value is selected as the upper boundary of the interval, and the index difference with the smallest value is selected as the lower boundary of the interval, thus forming the range of the continuous interval. Then, according to the original arrangement order of the continuous intervals in the discrete evolution sequence, each interval range is arranged and combined sequentially to form an interval change sequence with an ordered relationship. For example, if the index difference of a certain school object is divided into multiple continuous intervals, and the index difference in one interval is distributed with several values, then the maximum and minimum values ​​of that interval constitute the range of change of that interval. After multiple interval ranges are arranged in chronological order, the fluctuation trend of the index difference with the state change can be reflected, thus providing a continuous change basis for subsequent discreteness classification. Range extraction refers to the process of extracting the boundary values ​​of the sequence difference from each continuous interval. The maximum and minimum values ​​of the sequence difference within each continuous interval refer to the maximum and minimum values ​​among all sequence differences within that interval. The interval range is a range of values ​​formed by the maximum and minimum values, used to describe the variation range of the sequence difference within that interval. Serialization arrangement refers to the orderly arrangement of each interval range according to the order of the continuous intervals in the discrete evolution sequence. The interval change sequence is a sequence structure composed of multiple interval ranges in sequence, used to describe the changing trend of the sequence difference in different continuous intervals.

[0045] Based on the range of changes in the sequence number difference of each continuous interval in the interval change sequence, the distribution state of the sequence number difference in the discrete evolution sequence is classified into levels. When the range of changes in the sequence number difference is a single position offset, it is determined to be low dispersion. When the range of changes in the sequence number difference is a continuous offset of multiple adjacent positions, it is determined to be medium dispersion. When the range of changes in the sequence number difference is a non-continuous offset across intervals, it is determined to be high dispersion. Based on the dispersion classification results, the dispersion of the status data of the same school affairs object when there are differences in arrival time through different paths of the 5G core network is determined.

[0046] The discrete evolution sequence is hierarchically divided based on the range of changes in the index differences of each continuous interval in the interval change sequence. This can be achieved by classifying the range characteristics of each continuous interval. Specifically, firstly, the maximum and minimum values ​​of each continuous interval in the interval change sequence are read sequentially to determine the range of changes in the index differences within that interval. Then, the type of this range is identified. When the maximum and minimum values ​​within the interval only show a single positional shift, the interval is classified as a single positional shift type. When the index differences within the interval show a continuous increase or decrease and cover multiple adjacent positions, the interval is classified as... For adjacent multi-position continuous offset types, when the difference of the sequence number in an interval jumps between different intervals, the interval is judged as a cross-interval non-continuous offset type. After completing the determination of each interval type, the overall dispersion is classified according to the proportion or dominant type of different interval types in the entire interval change sequence. For example, when the change of the sequence number difference of a certain school object presents a single offset, it is classified as low dispersion. If there are multiple position continuous offsets in a continuous interval, it is classified as medium dispersion. If there is a significant jump distribution, it is classified as high dispersion, thereby realizing a structured characterization of the distribution characteristics of arrival time differences.

[0047] The range of changes in the index difference of each continuous interval in the interval change sequence refers to the range of changes formed by the upper and lower bounds of the index difference within each continuous interval. The distribution state of the index difference in the discrete evolution sequence refers to the arrangement and trend of the index difference in the entire sequence. The hierarchical division refers to the process of dividing the distribution of index difference into different categories based on different change characteristics. Single position offset refers to the case where the index difference changes only at a single position, corresponding to low dispersion, and is used to characterize the state of small order offset. Adjacent multi-position continuous offset refers to the case where the index difference changes continuously at multiple adjacent positions, corresponding to medium dispersion, and is used to characterize the state of continuous order perturbation. Cross-interval discontinuous offset refers to the case where the index difference is distributed in jumps between different intervals, corresponding to high dispersion, and is used to characterize the state of scattered and discontinuous order offset. These hierarchical results together constitute the basis for judging the degree of dispersion.

[0048] S4. Call the school affairs status to generate constraint sequences and discrete evolution sequences, perform rearrangement processing on the state data, correct the data consistency when combining states according to the degree of discreteness, and form a school affairs status reconstruction sequence; In this embodiment, S4 specifically refers to: The system calls the school status generation constraint sequence and discrete evolution sequence, performs association matching processing on the status data, and binds the status generation sequence in the school status generation constraint sequence with the sequence number difference in the discrete evolution sequence to form a set of status data generation order and offset relationship. The association and matching of state data by calling the school affairs status generation constraint sequence and discrete evolution sequence can be achieved by constructing a dual-sequence index mapping table. Specifically, each state data item is first read from its corresponding school affairs status generation constraint sequence, from which the state generation sequence code is extracted. Simultaneously, the sequence number difference corresponding to the state data in the discrete evolution sequence is read. Then, using the unique identifier of the state data as the connection key, the state generation sequence and sequence number difference are bound one-to-one, forming a mapping record. Based on this, all mapping records of state data are sorted according to the state generation sequence, and the sequence number difference in each record is appended as an offset marker to the corresponding state data, thus constructing a set of state data generation order and offset relationships. For example, if a school affairs object has multiple state data items, with the state generation sequence arranged as first, second, and third, and the corresponding sequence number differences reflecting different position offsets, binding these two types of information clarifies the position of each state data item in the actual generation order and its offset during transmission, providing a direct basis for subsequent rearrangement processing.

[0049] The school affairs status generation constraint sequence and discrete evolution sequence are two types of sequence structures used to describe the status generation logic and sequence offset characteristics, respectively. Status data refers to the data set generated around the same school affairs object at different times. Association matching processing refers to the process of binding the relevant information in the two types of sequences according to a unified identifier. The status generation sequence relationship is the encoded information used to represent the order of status generation. The sequence number difference is the numerical identifier used to represent the positional difference between the status generation order and the arrival order. The status data generation order and offset relationship set refers to the structured data set formed by combining the generation order information and offset information of each status data. This set is used to describe the offset of status data based on the actual generation order, providing basic data support for subsequent rearrangement and consistency correction.

[0050] After the set of generation order and offset relationship is constructed, the state data is rearranged. The state data is sorted according to the order of state generation in the school affairs state generation constraint sequence, and the position of the state data with order offset is adjusted according to the sequence difference in the discrete evolution sequence so that the arrangement order of the state data is consistent with the order of state generation. Reordering state data can be achieved by constructing a position adjustment table based on the generation order and offset relationship set. Specifically, firstly, all state data are initially sorted according to the generation sequence in the school affairs state generation constraint sequence, forming a standard generation order. Then, for state data with order offsets, the corresponding sequence number difference is read. Based on the sequence number difference, the offset direction and magnitude in the current sequence are determined, and the position of the state data is adjusted accordingly. For example, state data with a positive sequence number difference is moved backward, and state data with a negative sequence number difference is moved forward. Simultaneously, during the adjustment process, item-by-item swaps or overall movement are used to ensure that the relative order of other state data in the sequence is not disrupted. For example, if the state data of a certain school affairs object is first, second, and third in the generation order, but there is an order offset upon arrival, the position of the offset data is corrected by reading the sequence number difference, restoring the adjusted sequence to an arrangement consistent with the generation sequence, thereby eliminating order disturbances caused by differences in transmission paths.

[0051] Rearrangement refers to the process of rearranging state data according to a predetermined order. State data with order offset refers to data items whose position differs between the generation order and the arrival order. Position adjustment refers to the operation of moving or exchanging the position of state data in the sequence based on the sequence number difference. This is used to correct the order offset and make the arrangement order of state data consistent with the order of state generation. This process restores the overall sequence by directional adjustment of the offset data, thereby ensuring that the subsequent state combination process is based on the correct time order.

[0052] After the state data rearrangement is completed, the data consistency during state combination is corrected according to the degree of dispersion. When the degree of dispersion is low, the state data is directly combined in the rearranged order. When the degree of dispersion is medium, the state data with continuous sequential offset is locally rearranged according to the constraint sequence generated by the school administration state before being combined. When the degree of dispersion is high, the state data with cross-interval sequential offset is divided into multiple sub-sequences according to the discrete evolution sequence, rearranged separately, and then combined in segments. The sub-sequences are then sequentially spliced ​​according to the constraint sequence generated by the school administration state to form the school administration state reconstruction sequence.

[0053] After completing the state data rearrangement process, the data consistency correction for state combinations based on the degree of dispersion can be achieved through a hierarchical strategy-driven combination process. Specifically, the dispersion indicator corresponding to the current administrative object is first read. When the dispersion is low, it indicates that the sequence number differences are concentrated and the offset amplitude is small. In this case, the state data can be directly combined sequentially according to the rearranged order. When the dispersion is medium, continuous sequential offset state data segments are identified, for example, by determining the continuous offset range through the interval of continuous sequence number differences. Then, only the state data within this segment is rearranged locally based on the constraint sequence generated by the administrative state. Then, it is merged and combined with the un-offset part; when the dispersion is high, the state data with cross-interval sequential offset is identified. For example, multiple discontinuous offset segments are extracted from the interval division results in the discrete evolution sequence. These state data are divided into multiple sub-sequences, and each sub-sequence is independently rearranged according to the constraint sequence generated by the school affairs status. Finally, the sub-sequences are sequentially spliced ​​according to their arrangement relationship in the generation order. For example, if the state data of a certain school affairs object has a large-scale jump offset in different intervals, the reasonable structure of the overall state sequence can be restored by segment rearrangement and splicing, thereby achieving effective correction of complex sequence disturbances.

[0054] Data consistency during state combination refers to the characteristic of maintaining the generation order and logical relationship without conflict when integrating state data. Continuously shifted state data refers to a set of state data that shows continuous changes in the sequence of sequence difference values. Local rearrangement refers to the operation of adjusting the order of data only within the continuous segment. Cross-interval shifted state data refers to a set of data that jumps between multiple discrete intervals. Subsequence refers to a set of local data obtained from the overall state data according to interval division. Sequential splicing refers to the process of connecting multiple subsequences in sequence according to the generation order to form a complete sequence. The school state reconstruction sequence refers to the final state data sequence formed after hierarchical correction and combination processing, which is used to reflect the actual state change process.

[0055] S5. Combining the change trajectory of the discrete evolution sequence and the school affairs state reconstruction sequence, adaptively adjust the school affairs state generation constraint sequence and state rearrangement processing to complete the dynamic control of the state combination process.

[0056] In this embodiment, S5 specifically refers to: By combining the change trajectory of the discrete evolution sequence and the school affairs status reconstruction sequence, the change trend of the sequence difference in the discrete evolution sequence within the continuous interval is extracted, and the offset between the arrangement order of the state data and the order of state generation in the school affairs status reconstruction sequence is compared. The change trend of the sequence difference is correlated with the arrangement offset of the state data to form a set of associated data between the change trajectory and the reconstruction sequence. By combining the change trajectory of the discrete evolution sequence and the school affairs status reconstruction sequence, this can be achieved by performing synchronous scanning and position offset extraction on the two types of sequences. Specifically, the discrete evolution sequence is first read segment by segment according to continuous intervals, and the changing trend of the sequence difference within each interval is extracted according to the sequence order. For example, it is identified whether the sequence difference is increasing, decreasing, or fluctuating. At the same time, the state data in the school affairs status reconstruction sequence is traversed, and the current position of each state data in the reconstruction sequence is read. The reference position of the state data is determined by combining the state generation sequence in the school affairs status generation constraint sequence. The arrangement offset is obtained by comparing the difference between the current position and the reference position. Then, the changing trend of the sequence difference and the arrangement offset are matched one by one according to the state data identifier to form a corresponding relationship record. For example, if the sequence difference continuously increases in a certain continuous interval, the corresponding state data gradually shifts backward in the reconstruction sequence. By associating these two types of information, a mapping relationship between the change trajectory and the reconstruction result can be established to describe the correspondence between the change of the order perturbation over time and the rearrangement result.

[0057] The change trajectory of a discrete evolution sequence refers to the trend structure formed by the change of the sequence difference in a continuous interval with the change of the arrangement order. The school status reconstruction sequence refers to the status data sequence formed after rearrangement. The change trend of the sequence difference in a continuous interval refers to the direction and magnitude of the change of the sequence difference in the same interval. The arrangement order of the status data refers to the arrangement position of the status data in the reconstruction sequence. The state generation sequence refers to the original order information of the status data in the generation process. The offset between the arrangement order and the state generation sequence refers to the degree of difference between the two orders. The correlation correspondence refers to the process of matching the change trend and the offset according to the same status data. The correlation data set between the change trajectory and the reconstruction sequence refers to the data structure formed by the combination of the change trend of the sequence difference and the arrangement offset, which is used to describe the correspondence between the order change and the rearrangement result.

[0058] After the associated data set between the change trajectory and the reconstruction sequence is constructed, the order of state generation in the school affairs state generation constraint sequence is adjusted, the position adjustment parameters in the state rearrangement process are updated, the arrangement weight of the order of state generation is corrected according to the trend of the sequence difference, and the position adjustment magnitude is adaptively corrected according to the state data arrangement offset, so as to complete the dynamic control of the state combination process.

[0059] After constructing the associated data set between the change trajectory and the reconstruction sequence, adaptive adjustment can be achieved by establishing a linkage model of weight correction and position adjustment. In specific implementation, firstly, each record in the associated data set is traversed, and the trend of the sequence number difference is read. For example, it is determined whether there is an increasing, decreasing, or fluctuating change in the continuous interval. Based on the trend of change, different arrangement weights are assigned to the corresponding state data. For example, data with stable changes are given higher weights, and data with drastic changes are given lower weights. Then, based on the order of state generation in the school affairs state generation constraint sequence, the weight information is embedded in the sorting criteria to form a new sorting order. At the same time, position offset analysis is performed on the state data in the school affairs state reconstruction sequence. The position adjustment range is determined according to the arrangement offset of each state data. For example, a larger adjustment step size is set for data with a larger offset, and a smaller adjustment step size is set for data with a smaller offset. The position of the state data is corrected in multiple rounds through iteration. For example, if a certain state data has a continuous offset in the continuous interval and a large offset range in the reconstruction sequence, its priority is reduced and the position adjustment range is increased in subsequent sorting, so as to gradually converge to a stable arrangement state. Through this linkage of weight correction and position adjustment, dynamic control of the state combination process can be achieved.

[0060] The order of state generation in the school status generation constraint sequence is used to determine the basic sorting order of state data. The position adjustment parameter in the state rearrangement process refers to the numerical setting used to control the movement of state data in the sequence. The trend of sequence difference change refers to the direction and magnitude of change of sequence difference in a continuous interval. The arrangement weight is a control factor used to adjust the degree of influence of the order of state generation in the sorting process. The state data arrangement offset refers to the degree of difference between the position of state data in the reconstruction sequence and the generation order. The position adjustment magnitude refers to the distance parameter for moving the position of state data during the rearrangement process. Adaptive correction refers to the process of dynamically updating the arrangement weight and position adjustment magnitude based on the trend of sequence difference change and arrangement offset. Dynamic control refers to the processing method that makes the state combination result gradually tend to stabilize during continuous adjustment.

[0061] like Figure 2 The campus smart management system based on the 5G core network shown includes a state constraint module, a difference determination module, a discrete analysis module, a consistency correction module, and a dynamic control module. The state constraint module processes the state data generated by the same school affairs object, integrates and binds the state generation sequence, path identifier and generation trigger features to construct a school affairs state generation constraint sequence, and embeds the school affairs state generation constraint sequence into the state data. The difference determination module parses and processes the status data transmitted through different paths of the 5G core network, and combines the school status generation constraint sequence to perform the reverse mapping between the generated sequence and the arrival order to determine whether there is a difference in arrival time for the status data of the same school object through different paths of the 5G core network. The discrete analysis module performs continuous evolution analysis on state data with arrival time differences, and constructs a discrete evolution sequence based on the reverse mapping results to determine the degree of dispersion of the state data of the same school affairs object when there are arrival time differences through different paths of the 5G core network. The consistency correction module calls the constraint sequence and discrete evolution sequence generated by the school status, performs rearrangement processing on the status data, corrects the data consistency when combining states according to the degree of dispersion, and forms the school status reconstruction sequence. The dynamic control module, by combining the change trajectory of the discrete evolution sequence and the school affairs state reconstruction sequence, adaptively adjusts the school affairs state generation constraint sequence and state rearrangement processing to complete the dynamic control of the state combination process.

[0062] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0063] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0064] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0065] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0066] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0067] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0068] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A smart school administration method based on a 5G core network, characterized in that: Specifically, the following steps are included: S1. Process the status data generated by the same school affairs object, and fuse and bind the status generation sequence, path identifier and generation trigger feature to construct a school affairs status generation constraint sequence, and embed the school affairs status generation constraint sequence into the status data. S2. Parse and process the status data transmitted through different paths of the 5G core network, combine the school status generation constraint sequence to perform the reverse mapping between the generation sequence and the arrival order, and determine whether there is a difference in arrival time for the status data of the same school object through different paths of the 5G core network. S3. In the case of arrival time differences, perform continuous evolution analysis on the state data with arrival time differences, and construct a discrete evolution sequence based on the reverse mapping results to determine the degree of dispersion of the state data of the same school affairs object when there are arrival time differences through different paths of the 5G core network. S4. Call the school affairs status to generate a constraint sequence and a discrete evolution sequence, perform rearrangement processing on the state data, correct the data consistency when combining states according to the degree of discreteness, and form a school affairs status reconstruction sequence; S5. Combining the change trajectory of the discrete evolution sequence and the school affairs state reconstruction sequence, adaptively adjust the school affairs state generation constraint sequence and state rearrangement processing to complete the dynamic control of the state combination process.

2. The intelligent school administration method based on a 5G core network according to claim 1, characterized in that, S1 specifically refers to: The status data generated by the same school affairs object is processed. The status data is parsed according to the preset field structure, and the generation time information used to represent the order of status generation is extracted, the transmission path source information used to represent the path identifier is extracted, the trigger source information used to represent the generation trigger feature is extracted, and the status data is reorganized according to a unified field order. Sequential encoding is performed on the generation time information to form the generation sequence relationship of the status; path encoding is performed on the transmission path source information to form the path identifier; trigger encoding is performed on the trigger source information to form the generation trigger feature; and the generation sequence relationship of the status, the path identifier and the generation trigger feature are fused and bound according to the preset arrangement order to construct the school status generation constraint sequence. Write the constraint sequence for generating school status into the specified field position of the corresponding status data, and complete the embedding association between the constraint sequence for generating school status and the status data through field mapping relationship.

3. The intelligent school administration method based on a 5G core network according to claim 1, characterized in that, S2 specifically includes the following steps: S201. Parse and process the status data transmitted through different paths of the 5G core network, read the school affairs status generation constraint sequence in the status data according to the field structure, extract the status generation sequence relationship, path identifier and generation trigger feature, and synchronously record the actual arrival order of the status data to form a status data set containing the generation sequence and arrival order. S202. After completing the construction of the state data set, combine the school affairs state generation constraint sequence to perform the reverse mapping between the generation sequence and the arrival order. By aligning the state generation sequence with the actual arrival order item by item, establish the correspondence sequence between the generation sequence and the arrival order, and mark the order deviation position in the correspondence sequence. S203. Based on the position of the order deviation in the corresponding relationship sequence, perform a consistency comparison on the status data of the same school affairs object. When there is a situation where the order of status generation is inconsistent with the order of arrival, it is determined that the status data of the same school affairs object has different arrival times through different paths of the 5G core network.

4. The intelligent school administration method based on a 5G core network according to claim 3, characterized in that, S202 specifically refers to: For each state data in the state data set, extract the state generation sequence and the actual arrival sequence, sort them according to the state generation sequence to form a generation sequence, and sort them according to the actual arrival sequence to form an arrival sequence. After the generation sequence and arrival sequence are constructed, the reverse mapping between the generation sequence and arrival sequence is performed by combining the school status generation constraint sequence. By matching the status data of the corresponding position in the arrival sequence item by item with the generation sequence as the benchmark, a sequence of correspondences between the generation sequence and the arrival sequence is established, and the sequence number difference in each correspondence is recorded. The state data is subjected to order deviation determination based on the sequence number difference in the corresponding relation sequence. When the sequence number difference is not zero, the state data is marked with the order deviation position in the corresponding relation sequence, and the marking result is written into the corresponding relation sequence.

5. The intelligent school administration method based on a 5G core network according to claim 1, characterized in that, S3 specifically includes the following steps: S301. In the case of arrival time differences, perform continuous evolution analysis on the state data with arrival time differences, extract the corresponding state data according to the order deviation position in the corresponding relationship sequence, and arrange the extracted state data in order according to the order of state generation to form a continuous state evolution sequence. S302. For the state data in the continuous state evolution sequence, extract the sequence number difference corresponding to each state data according to the reverse mapping result, and arrange the sequence number differences in order according to the order of state generation to construct a discrete evolution sequence. S303. Based on the arrangement and distribution of the sequence number difference in the discrete evolution sequence, the sequence number difference is divided into intervals and its distribution is statistically analyzed. The degree of dispersion is graded and judged according to the range of change of the sequence number difference within the interval, so as to determine the degree of dispersion when the status data of the same school affairs object has different arrival time differences through different paths of the 5G core network.

6. The intelligent school administration method based on a 5G core network according to claim 5, characterized in that, S303 specifically refers to: Based on the arrangement and distribution of the index differences in the discrete evolution sequence, the index differences are segmented according to the preset interval division standard, dividing the index differences in the discrete evolution sequence into continuous intervals, and performing distribution statistics on the index differences in each continuous interval to form an interval distribution set; After the interval distribution set is constructed, the range of the index difference in each continuous interval is extracted to obtain the maximum and minimum values ​​of the index difference in each continuous interval. Then, the range of each interval is serialized and arranged according to the order of the continuous intervals in the discrete evolution sequence to form the interval change sequence. Based on the range of changes in the sequence number difference of each continuous interval in the interval change sequence, the distribution state of the sequence number difference in the discrete evolution sequence is classified into levels. When the range of changes in the sequence number difference is a single position offset, it is determined to be low dispersion. When the range of changes in the sequence number difference is a continuous offset of multiple adjacent positions, it is determined to be medium dispersion. When the range of changes in the sequence number difference is a non-continuous offset across intervals, it is determined to be high dispersion. Based on the dispersion classification results, the dispersion of the status data of the same school affairs object when there are differences in arrival time through different paths of the 5G core network is determined.

7. The intelligent school administration method based on a 5G core network according to claim 1, characterized in that, S4 specifically refers to: The system calls the school status generation constraint sequence and discrete evolution sequence, performs association matching processing on the status data, and binds the status generation sequence in the school status generation constraint sequence with the sequence number difference in the discrete evolution sequence to form a set of status data generation order and offset relationship. After the set of generation order and offset relationship is constructed, the state data is rearranged. The state data is sorted according to the order of state generation in the school affairs state generation constraint sequence. The position of the state data with order offset is adjusted according to the sequence difference in the discrete evolution sequence so that the arrangement order of the state data is consistent with the order of state generation. After the state data rearrangement is completed, the data consistency during state combination is corrected according to the degree of dispersion. When the degree of dispersion is low, the state data is directly combined in the rearranged order. When the degree of dispersion is medium, the state data with continuous sequential offset is locally rearranged according to the constraint sequence generated by the school administration state before being combined. When the degree of dispersion is high, the state data with cross-interval sequential offset is divided into multiple sub-sequences according to the discrete evolution sequence, rearranged separately, and then combined in segments. The sub-sequences are then sequentially spliced ​​according to the constraint sequence generated by the school administration state to form the school administration state reconstruction sequence.

8. The intelligent school administration method based on a 5G core network according to claim 1, characterized in that, S5 specifically refers to: By combining the change trajectory of the discrete evolution sequence and the school affairs status reconstruction sequence, the change trend of the sequence difference in the discrete evolution sequence within the continuous interval is extracted, and the offset between the arrangement order of the state data and the order of state generation in the school affairs status reconstruction sequence is compared. The change trend of the sequence difference is correlated with the arrangement offset of the state data to form a set of associated data between the change trajectory and the reconstruction sequence. After the associated data set between the change trajectory and the reconstruction sequence is constructed, the order of state generation in the school affairs state generation constraint sequence is adjusted, the position adjustment parameters in the state rearrangement process are updated, the arrangement weight of the order of state generation is corrected according to the trend of the sequence difference, and the position adjustment magnitude is adaptively corrected according to the state data arrangement offset, so as to complete the dynamic control of the state combination process.

9. A smart school administration management system based on a 5G core network, used to implement the smart school administration management method based on a 5G core network as described in any one of claims 1-8, characterized in that, It includes a state constraint module, a difference determination module, a discrete analysis module, a consistency correction module, and a dynamic control module; The state constraint module processes the state data generated by the same school affairs object, integrates and binds the state generation sequence, path identifier and generation trigger features to construct a school affairs state generation constraint sequence, and embeds the school affairs state generation constraint sequence into the state data. The difference determination module parses and processes the status data transmitted through different paths of the 5G core network, and combines the school status generation constraint sequence to perform the reverse mapping between the generated sequence and the arrival order to determine whether there is a difference in arrival time for the status data of the same school object through different paths of the 5G core network. The discrete analysis module performs continuous evolution analysis on state data with arrival time differences, and constructs a discrete evolution sequence based on the reverse mapping results to determine the degree of dispersion of the state data of the same school affairs object when there are arrival time differences through different paths of the 5G core network. The consistency correction module calls the constraint sequence and discrete evolution sequence generated by the school status, performs rearrangement processing on the status data, corrects the data consistency when combining states according to the degree of dispersion, and forms the school status reconstruction sequence. The dynamic control module, by combining the change trajectory of the discrete evolution sequence and the school affairs state reconstruction sequence, adaptively adjusts the school affairs state generation constraint sequence and state rearrangement processing to complete the dynamic control of the state combination process.

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