Enforcement record data archive management system

Abstract

The invention discloses a law enforcement record data archive management system, and relates to the technical field of data management. Scientific partition storage of archive data is realized through keyword recognition and assignment classification in combination with a preset rule, the storage orderliness is improved, and the optimal partition storage efficiency is guaranteed; daily separate storage is convenient for later accident liability fixing and tracing; extracting stable features of data types of each classification partition, avoiding the problem of too slow index rate or insufficient stability, and remarkably improving partition index rate and reliability; the execution features are dynamically adjusted according to the stable features and the computing power features, it is ensured that different data types achieve efficient indexing under computing power adaptation, the indexing time is greatly shortened, and the system indexing burden is reduced; similar data are rapidly positioned through accurate matching logic, efficient retrieval under multiple scenes is supported, it is ensured that an operator can rapidly obtain needed archive information, and the law enforcement work efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of data management technology, specifically to a law enforcement record data archiving management system. Background Technology

[0002] Law enforcement record data (including body camera videos, interrogation transcripts, on-site photos, electronic case files, etc.) serves as a core carrier for reconstructing law enforcement scenes, securing key evidence, supervising law enforcement actions, and protecting the legitimate rights and interests of the parties involved, making its management increasingly important.

[0003] Currently, law enforcement agencies have generally recognized the value of law enforcement record data and are gradually transforming from traditional paper-based file management or simple electronic document storage to a digital management model, in order to improve data management efficiency and application value through technological means.

[0004] However, existing methods of managing law enforcement record data still have many technical challenges that urgently need to be addressed: Balancing partitioned storage and indexing performance is challenging. Law enforcement records contain a high proportion of large files, such as audio and video files, which are growing rapidly. The access frequency and indexing requirements for different data types vary significantly. Existing systems often employ a "one-size-fits-all" storage and indexing strategy, failing to optimize for the characteristics of different data types within partitions. This results in delayed retrieval of frequently accessed data, excessive resource consumption of low-frequency archived data, and even large fluctuations in indexing speed and instability, impacting data retrieval efficiency in law enforcement.

[0005] The allocation of computing resources lacks dynamic adaptability. The data types within each category partition are complex and diverse, and the indexing operations of different data types have different computing power requirements. Existing systems typically adopt a fixed computing power allocation model, which cannot be dynamically adjusted according to the actual indexing characteristics of data types (such as historical indexing rates and computing power demand patterns). This results in either resource waste due to excessive computing power or stagnation in the indexing process due to insufficient computing power, further exacerbating the efficiency bottleneck of data management.

[0006] In summary, current law enforcement record data management still has significant shortcomings in terms of standardized classification, partition optimization, computing power adaptation, and accurate retrieval. There is an urgent need for a system solution that can achieve standardized and intelligent management throughout the entire process to solve existing technical pain points and improve the management efficiency and application value of law enforcement record data. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a law enforcement record data archiving management system that solves the problem of not optimizing for the characteristics of different data types within a partition.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a law enforcement record data archiving management system, comprising: The data acquisition end acquires the archival data generated during the law enforcement record process within a specified period and transmits the acquired archival data to the classification and processing end, where the specified period is a preset period; The classification processing end performs feature identification to lock keywords in the archival data acquired within a specified period, then assigns values ​​to the keywords. Based on the differences between the assigned values, the acquired archival data is classified and stored in different classification partitions. The specific method is as follows: Identify the different keywords associated with different archive data, and according to the preset assignment check table, identify the assignment associated with the corresponding characters in the corresponding keywords. Sort the associated assignments according to the original sorting method of the keywords, and identify the assignment column corresponding to the keywords in the archive data. Sort the assigned columns associated with different archive data within a specified period in ascending order of numerical values, and confirm the assigned sorting column; Based on the confirmed assignment sequence and the preset division range Y1, start from the minimum assignment value F in the assignment sequence. min Begin by identifying several classification ranges [F] min F min +Y1), [F min +Y1, F min+ 2Y1), ..., and so on. Based on the confirmed classification range, the archive data associated with each classification range is confirmed, and the confirmed archive data is stored in the same partition. Based on the associated classification range, the corresponding number of classification partitions is confirmed, and the storage process of the corresponding archive data is completed. The partition feature optimization end confirms the type of archive data stored in a single category partition, identifies the data type associated with each category partition, and then extracts stable features from the historical process based on the confirmed data type, transmitting them to the correction end. Specifically: Identify the different archive data stored within a single category partition, and determine the different data types from these archive data. Mark the identified data types as pending types. Identify the historical index data associated with these pending types from cloud data. Then, identify the index rates associated with these pending types under different computing power states from the historical index data. From several sets of associated index rates, select stable features. Specifically: An index rate sequence is generated in ascending order of index rate. Based on the minimum and maximum values ​​of the index rate sequence, the numerical ranges belonging to the index rate sequence are determined. A set of variable ranges is generated simultaneously, where each variable range ∈ the numerical range. Several change processes are executed, with the numerical range of the variable ranges differing in each change process. The total number of index rates G contained in each different variable range is determined. iWhere i represents different variable intervals, and the range of intervals associated with different variable intervals is denoted as F. i G i ÷F i =M i Confirm the density feature M associated with the corresponding variable interval i Then, from the different density features M associated with different variable intervals i In the middle, select the maximum value M. i max, M i The variable interval associated with max is denoted as the fixed interval, and the different execution computing power associated with the fixed interval is taken as the stable feature associated with the corresponding undetermined type. From historical index data, stable features associated with different undetermined types are confirmed sequentially, and several stable features confirmed by the corresponding classification partition are transmitted to the correction end. The correction end receives several stable features identified within a single category partition and, based on the computing power features assigned to the corresponding category partition, corrects the execution features associated with different data types within each category partition. Specifically: The data types associated with a single category partition are determined, and the execution computing power is confirmed from the stable characteristics associated with the corresponding data type. The average of the confirmed execution computing power groups is then processed to confirm the average computing power associated with the corresponding data type. From the computing power characteristics assigned to the corresponding classification partition, identify the execution characteristics associated with different data types within it, and determine whether the execution characteristics are consistent with the average computing power. If they are inconsistent, mark the corresponding data type as the adjustment type; if they are consistent, mark the corresponding data type as the standard type. If the computing power characteristics associated with the corresponding category partition are sufficient to complete the computing power adjustment process of the adjustment type, then the execution characteristics associated with the adjustment type are adjusted to the same computing power value as the average computing power. If not, the average computing power values ​​associated with several adjustment types are compared to determine the computing power ratio column. Then, the total execution characteristics associated with the adjustment type are evenly distributed according to the computing power ratio column, and the evenly distributed computing power value is used as the execution characteristics of the adjustment type.

[0009] Preferred options also include: The search index end identifies the specific assigned columns based on the search terms provided by the operator, and then identifies the file data associated with the search terms based on the specific assigned columns of the file data stored in different category partitions, and outputs the data in a timely manner.

[0010] The specific method for confirming the archive data associated with the search term is as follows: Based on the input search terms and the associated assignment checklist, confirm the assignment associated with the characters inside the corresponding search terms, and confirm the assignment column associated with the corresponding search terms, which is recorded as the search column. Next, confirm the category range associated with different category partitions, confirm the category range to which this assigned column belongs, mark the associated category partition as the partition to be searched, and confirm the assigned columns associated with different file data within the partition to be searched. Based on the confirmed lookup column and the different assignment columns associated with different file data, confirm the same assignment between the lookup column and the corresponding assignment column, record the percentage of the same assignment in the corresponding lookup column, select the assignment column with a percentage of 100% as the associated column, confirm the order of the corresponding assignment in the lookup column, and record the associated columns with the same order as the selected columns. If only one set exists in the selected column, the file data associated with this selected column will be output directly; If there are multiple groups of selected columns, select the selected column with the fewest assigned values ​​from the multiple groups of selected columns, and directly output the file data associated with this selected column. If there are selected columns with the fewest assigned values ​​and there are identical selected columns, output all the file data associated with the corresponding selected column.

[0011] This invention provides a data archiving management system for law enforcement records. Compared with existing technologies, it has the following advantages: This invention achieves scientific partitioned storage of archival data by identifying and classifying keywords and combining them with preset rules, thereby improving the organization of storage, ensuring optimal partitioned storage efficiency, and facilitating later accident liability determination and tracing by storing data separately by day. Extract stable features of data types in each category partition to avoid problems such as slow indexing speed or insufficient stability, and significantly improve the indexing speed and reliability of partitions. Execution characteristics are dynamically adjusted based on stability and computing power characteristics to ensure efficient indexing of different data types under computing power adaptation, significantly reducing indexing time and lowering the system indexing burden; By using precise matching logic to quickly locate similar data, it supports efficient retrieval in multiple scenarios, ensuring that operators can quickly obtain the required file information and improve the efficiency of law enforcement work. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] First Embodiment Please see Figure 1 This application provides a law enforcement record data archiving management system, including a data acquisition end, a classification and processing end, a partition feature optimization end, cloud data, a correction end, and a search index end; The data acquisition end, classification processing end, partition feature optimization end, and search index end are electrically connected sequentially from the output node to the input node, and the partition feature optimization end and the correction end input node are electrically connected. The data acquisition end acquires the archival data generated during the law enforcement record process within a specified period and transmits the acquired archival data to the classification and processing end. The specified period is a preset period, and its specific value is determined by the operator based on experience. The classification processing end identifies and locks keywords in the archival data acquired within a specified period, then assigns values ​​to the keywords. Based on the differences between the different assigned values, the acquired archival data is classified and stored in different classification partitions. Specifically, different archival data have different keywords. During the law enforcement recording process, there are assigned keywords (assigned by law enforcement personnel). By using a specific assignment checklist, the specific assigned values ​​associated with the corresponding keywords can be effectively confirmed. Based on the associated specific assigned values, the archival data is classified to ensure that the storage processing of the partitions is in an optimized state. The classification processing end categorizes the acquired archival data in the following specific way: Identify the different keywords associated with different archive data, and according to the preset assignment check table, identify the assignment associated with the corresponding characters in the corresponding keywords. Sort the associated assignments according to the original sorting method of the keywords, and identify the assignment column corresponding to the keywords in the archive data. Sort the assigned columns associated with different archive data within a specified period in ascending order of numerical values, and confirm the assigned sorting column; Based on the confirmed assignment sequence and the preset division range Y1, start from the minimum assignment value F in the assignment sequence. min Begin by identifying several classification ranges [F] min F min +Y1), [F min +Y1, F min+2Y1), ..., and so on. Based on the confirmed classification range, the archive data associated with each classification range is confirmed, and the confirmed archive data is stored in the same partition. Based on the associated classification range, the corresponding number of classification partitions is confirmed, and the storage process of the corresponding archive data is completed. Specifically, based on the values ​​assigned to different keywords, the corresponding assignment sequence is determined. Then, based on the specific division method of the corresponding assignment sequence, different classification ranges are determined. Based on the divided classification ranges, the partitioning process of the corresponding file data is completed. The specified cycle is generally 24 hours. In the specific law enforcement process, the law enforcement data associated with each day is stored separately to facilitate data indexing later and to facilitate accident liability determination. The partition feature optimization end confirms the type of archive data stored in a single category partition, identifies the data type associated with each category partition, and then extracts stable features from the historical process based on the confirmed data type and transmits them to the correction end. Specifically, the archive data stored in each category partition contains different types of data. In order to achieve the optimal indexing process for different types of data, it is necessary to select the optimal feature from the data features generated by different data types in the historical data, and then make reasonable optimizations and corrections based on the optimal feature to ensure the overall feature optimization effect. The specific methods for extracting stable features from historical processes are as follows: Identify the different archive data stored within a single category partition, and determine the different data types from these archive data. Label the identified data types as pending types. Identify the historical index data associated with these pending types from cloud data. Then, identify the index rates associated with these pending types under different computing power conditions from the historical index data. From the several associated index rates, select stable features. An index rate sequence is generated in ascending order of index rate. Based on the minimum and maximum values ​​of the index rate sequence, the numerical ranges belonging to the index rate sequence are determined. A set of variable ranges is generated simultaneously, where each variable range ∈ the numerical range. Several change processes are executed, with the numerical range of the variable ranges differing in each change process. The total number of index rates G contained in each different variable range is determined. i Where i represents different variable intervals, and the range of intervals associated with different variable intervals is denoted as F. i G i ÷F i =M i Confirm the density feature M associated with the corresponding variable interval i Then, from the different density features M associated with different variable intervals i In the middle, select the maximum value M. i max, Mi The variable interval associated with max is denoted as the fixed interval, and the different execution computing power associated with the fixed interval is taken as the stable feature associated with the corresponding undetermined type. From historical index data, stable features associated with different undetermined types are confirmed sequentially, and several stable features confirmed by the corresponding classification partition are transmitted to the correction end. Specifically, within each category partition, there are different historical feature data, and different historical feature data are associated with different numerical features. From the confirmed numerical features, the associated stable features can be effectively determined, which makes it easier to index the data associated with such category partitions in the future without the indexing speed being too slow or the indexing stability being unstable, thus improving the indexing speed of the category partitions.

[0015] The correction end receives several stable features identified within a single classification partition and, based on the computing power features assigned to the corresponding classification partition, corrects the execution features associated with different data types within each classification partition. The specific method is as follows: The data types associated with a single category partition are determined, and the execution computing power is confirmed from the stable characteristics associated with the corresponding data type. The average of the confirmed execution computing power groups is then processed to confirm the average computing power associated with the corresponding data type. From the computing power characteristics assigned to the corresponding classification partition, identify the execution characteristics associated with different data types within it, and determine whether the execution characteristics are consistent with the average computing power. If they are consistent, directly label the corresponding data type as the standard type; if they are inconsistent, label the corresponding data type as the adjustment type. If the computing power characteristics associated with the corresponding category partition are sufficient to complete the computing power adjustment process of the adjustment type, then the execution characteristics associated with the adjustment type are adjusted to the same computing power value as the average computing power value. If not, the average computing power values ​​associated with several adjustment types are processed by ratio analysis to confirm the computing power ratio column. Then, the total execution characteristics associated with the adjustment type are evenly distributed according to the computing power ratio column, and the computing power value after even distribution is used as the execution characteristics of the adjustment type. Specifically, by evenly distributing the computing power characteristics associated with the corresponding partitions, it is possible to effectively ensure that each partition has the execution characteristics associated with different data types, thereby ensuring that each data type can be indexed quickly and efficiently, guaranteeing the corresponding indexing speed, significantly reducing the corresponding indexing time, and lowering the indexing burden.

[0016] Second Embodiment In the specific implementation process, compared with the above embodiments, this embodiment mainly focuses on the specific search process, and its main execution end is the search index end; In the search index section, based on the search terms provided by the operator, the specific assigned columns are identified, and then based on the specific assigned columns of the archive data stored in different category partitions, the similar data associated with the search terms are identified and output in a timely manner. The specific methods for confirming similar data are as follows: Based on the input search terms and the associated assignment checklist, confirm the assignment associated with the characters inside the corresponding search terms, and confirm the assignment column associated with the corresponding search terms, which is recorded as the search column. Next, confirm the category range associated with different category partitions, confirm the category range to which this assigned column belongs, mark the associated category partition as the partition to be searched, and confirm the assigned columns associated with different file data within the partition to be searched. Based on the confirmed lookup column and the different assignment columns associated with different file data, confirm the same assignment between the lookup column and the corresponding assignment column, record the percentage of the same assignment in the corresponding lookup column, select the assignment column with a percentage of 100% as the associated column, confirm the order of the corresponding assignment in the lookup column, and record the associated columns with the same order as the selected columns. If only one set exists in the selected column, the file data associated with this selected column will be output directly; If there are multiple groups of selected columns, select the selected column with the fewest assigned values ​​from the multiple groups of selected columns, and directly output the file data associated with this selected column. If there are selected columns with the fewest assigned values ​​and there are identical selected columns, output all the file data associated with the corresponding selected column.

[0017] Specifically, in the index output lookup process, there are corresponding associated file data. Each different associated file data has a different selected process. The selected columns associated with the corresponding selected process can be output.

[0018] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0019] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A law enforcement record data archiving management system, characterized in that, include: The data acquisition end acquires the archival data generated during the law enforcement record process within a specified period and transmits the acquired archival data to the classification and processing end, where the specified period is a preset period; The classification processing end performs feature recognition to lock keywords in the archive data acquired within a specified period, then assigns values ​​to the keywords, and classifies the acquired archive data according to the differences between different assigned columns, storing them in different classification partitions; The partition feature optimization end confirms the type of archive data stored in a single category partition, confirms the data type associated with each category partition, and then extracts stable features from the historical process based on the confirmed data type and transmits them to the correction end. The correction end receives several stable features identified within a single classification partition and, based on the computing power features assigned to the corresponding classification partition, performs correction processing on the execution features associated with different data types within each classification partition.

2. The law enforcement record data archiving management system according to claim 1, characterized in that, The specific method by which the classification processing terminal classifies archival data is as follows: Identify the different keywords associated with different archive data, and according to the preset assignment check table, identify the assignment associated with the corresponding characters in the corresponding keywords. Sort the associated assignments according to the original sorting method of the keywords, and identify the assignment column corresponding to the keywords in the archive data. Sort the assigned columns associated with different archive data within a specified period in ascending order of numerical values, and confirm the assigned sorting column; Based on the confirmed assignment sequence and the preset division range Y1, start from the minimum assignment value F in the assignment sequence. min Begin by identifying several classification ranges [F] min F min +Y1), [F min +Y1, F min+ 2Y1), ..., and so on. Based on the confirmed classification range, the archive data associated with each classification range is confirmed, and the confirmed archive data is stored in the same partition. Based on the associated classification range, the corresponding number of classification partitions is confirmed, and the storage process of the corresponding archive data is completed.

3. The law enforcement record data archiving management system according to claim 2, characterized in that, The specific method by which the partition feature optimization end extracts stable features from the historical process is as follows: Identify the different archive data stored within a single category partition, identify different data types from the different archive data, and label the identified data types as undetermined types. Identify the historical index data associated with the undetermined types from cloud data, and then identify the index rate associated with the undetermined types under different execution computing power states from the historical index data. Select stable features from several associated index rates. From the historical index data, the stable features associated with different undetermined types are confirmed in sequence, and several stable features confirmed by the corresponding classification partition are transmitted to the correction end.

4. The law enforcement record data archiving management system according to claim 3, characterized in that, The specific method for selecting stable features from the associated sets of index rates is as follows: An index rate sequence is generated in ascending order of index rate. Based on the minimum and maximum values ​​of the index rate sequence, the numerical ranges belonging to the index rate sequence are determined. A set of variable ranges is generated simultaneously, where each variable range ∈ the numerical range. Several change processes are executed, with the numerical range of the variable ranges differing in each change process. The total number of index rates G contained in each different variable range is determined. i Where i represents different variable intervals, and the range of intervals associated with different variable intervals is denoted as F. i G i ÷F i =M i Confirm the density feature M associated with the corresponding variable interval i Then, from the different density features M associated with different variable intervals i In the middle, select the maximum value M. i max, M i The variable interval associated with max is denoted as the fixed interval, and the different execution computing power associated with the fixed interval is taken as the stable feature associated with the corresponding undetermined type.

5. The law enforcement record data archiving management system according to claim 1, characterized in that, The specific method by which the correction end corrects the execution characteristics of different data types is as follows: The data types associated with a single category partition are determined, and the execution computing power is confirmed from the stable characteristics associated with the corresponding data type. The average of the confirmed execution computing power groups is then processed to confirm the average computing power associated with the corresponding data type. From the computing power characteristics assigned to the corresponding classification partition, identify the execution characteristics associated with different data types, and determine whether the execution characteristics are consistent with the average computing power. If they are inconsistent, mark the corresponding data type as the adjustment type. If the computing power characteristics associated with the corresponding category partition are sufficient to complete the computing power adjustment process of the adjustment type, then the execution characteristics associated with the adjustment type are adjusted to the same computing power value as the average computing power. If not, the average computing power values ​​associated with several adjustment types are compared to determine the computing power ratio column. Then, the total execution characteristics associated with the adjustment type are evenly distributed according to the computing power ratio column, and the evenly distributed computing power value is used as the execution characteristics of the adjustment type.

6. The law enforcement record data archiving management system according to claim 5, characterized in that, If the execution characteristics are consistent with the average computing power, then the corresponding data type is directly labeled as the standard type.

7. The law enforcement record data archiving management system according to claim 1, characterized in that, Also includes: The search index end identifies the specific assigned columns based on the search terms provided by the operator, and then identifies the file data associated with the search terms based on the specific assigned columns of the file data stored in different category partitions, and outputs the data in a timely manner.

8. The law enforcement record data archiving management system according to claim 7, characterized in that, The specific method by which the search index confirms the file data associated with the search term is as follows: Based on the input search terms and the associated assignment checklist, confirm the assignment associated with the characters inside the corresponding search terms, and confirm the assignment column associated with the corresponding search terms, which is recorded as the search column. Next, confirm the category range associated with different category partitions, confirm the category range to which this assigned column belongs, mark the associated category partition as the partition to be searched, and confirm the assigned columns associated with different file data within the partition to be searched. Based on the confirmed lookup column and the different assignment columns associated with different file data, confirm the same assignment between the lookup column and the corresponding assignment column, record the percentage of the same assignment in the corresponding lookup column, select the assignment column with a percentage of 100% as the associated column, confirm the order of the corresponding assignment in the lookup column, and record the associated columns with the same order as the selected columns. If only one set exists in the selected column, the file data associated with this selected column will be output directly; If there are multiple groups of selected columns, select the selected column with the fewest assigned values ​​from the multiple groups of selected columns, and directly output the file data associated with this selected column. If there are selected columns with the fewest assigned values ​​and there are identical selected columns, output all the file data associated with the corresponding selected column.

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