A law enforcement record data archiving management system

Abstract

This invention discloses a law enforcement record data archival management system. In the field of data management technology, this invention achieves scientific partitioned storage of archival data through keyword recognition and value assignment classification, combined with preset rules. This improves storage organization, ensures optimal partitioned storage efficiency, and facilitates later accident liability determination and tracing by storing data separately by day. It extracts stable features of data types in each category, avoiding slow indexing speeds or insufficient stability, significantly improving partitioned indexing speed and reliability. Based on stable features and computing power characteristics, it dynamically adjusts execution features to ensure efficient indexing of different data types under computing power adaptation, greatly reducing indexing time and lowering the system's indexing burden. Through precise matching logic, it quickly locates similar data, supporting efficient retrieval in multiple scenarios, ensuring operators can quickly obtain the required archival information and improving law enforcement efficiency.

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:

[0005] 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.

[0006] 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.

[0007] 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

[0008] 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.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a law enforcement record data archiving management system, comprising:

[0010] 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;

[0011] 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:

[0012] 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.

[0013] 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;

[0014] 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.

[0015] 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:

[0016] 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 the pending types from cloud data. Then, identify the index rates associated with the pending types under different computing power states from the historical index data. From several sets of associated index rates, select stable features. Specifically:

[0017] 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.

[0018] 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.

[0019] 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:

[0020] 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.

[0021] 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.

[0022] 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.

[0023] Preferred options also include:

[0024] 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.

[0025] The specific method for confirming the archive data associated with the search term is as follows:

[0026] 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.

[0027] 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.

[0028] 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.

[0029] If only one set exists in the selected column, the file data associated with this selected column will be output directly;

[0030] 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.

[0031] This invention provides a data archiving management system for law enforcement records. Compared with existing technologies, it has the following advantages:

[0032] 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.

[0033] 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.

[0034] 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;

[0035] 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

[0036] Figure 1 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0037] 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.

[0038] First Embodiment

[0039] 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;

[0040] 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.

[0041] 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.

[0042] 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.

[0043] The classification processing end categorizes the acquired archival data in the following specific way:

[0044] 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.

[0045] 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;

[0046] 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.

[0047] 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.

[0048] 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.

[0049] The specific methods for extracting stable features from historical processes are as follows:

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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:

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] Second Embodiment

[0060] 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;

[0061] 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.

[0062] The specific methods for confirming similar data are as follows:

[0063] 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.

[0064] 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.

[0065] 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.

[0066] If only one set exists in the selected column, the file data associated with this selected column will be output directly;

[0067] 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.

[0068] Specifically, in the index output search 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.

[0069] 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.

[0070] 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 archival management system, comprising: The application comprises: a data acquisition end, which acquires archive data generated in a law enforcement record process in a specified period, and transmits the acquired archive data to a classification processing end, wherein the specified period is a preset period; the classification processing end, which performs feature recognition and locks keywords on the archive data acquired in the specified period, performs value assignment processing on the keywords, classifies the acquired archive data according to the difference characteristics between different value assignment columns, and stores the archive data in different classification partitions; a partition feature optimization end, which performs type confirmation on the archive data stored in a single classification partition, confirms the data types associated with each classification partition, extracts stable features from historical processes according to the confirmed data types, and transmits the stable features to a correction end, in a specific manner as follows: confirming different archive data stored in a single classification partition, confirming different data types from the different archive data, and marking the confirmed data types as pending types, confirming historical index data associated with the pending types from cloud data, identifying index rates associated with the pending types in different execution power states from the historical index data, and selecting stable features from the associated index rates; confirming the stable features associated with different pending types from the historical index data in sequence, and transmitting the stable features confirmed for the corresponding classification partition to the correction end; the correction end, which receives the stable features confirmed for a single classification partition, and corrects the execution features associated with different data types in each classification partition according to the power features assigned to the corresponding classification partition.

2. The law enforcement record data archival management system of claim 1, wherein, The classification processing end classifies the archive data in a specific manner as follows: confirming different keywords associated with different archive data, confirming the values associated with corresponding characters in the corresponding keywords according to a preset value assignment check table, sorting the associated values according to the original sorting manner of the keywords, and confirming the value assignment column corresponding to the keywords of the corresponding archive data; sorting the value assignment columns associated with different archive data in the specified period in ascending order of numerical value, and confirming the value assignment sorting column; According to the confirmed value order column and the preset division range Y1, the minimum value F min Initially, several classification ranges [F min , F min +Y1], [F min +Y1, F min+ 2Y1], and the like are confirmed, and according to the confirmed classification ranges, the archive data associated with each classification range is confirmed and stored in the same partition, and according to the associated classification range, a corresponding number of classification partitions are confirmed, and the storage process of the corresponding archive data is completed.

3. The law enforcement record data archival management system of claim 1, wherein, The specific manner of selecting stable features from the associated index rates is as follows: According to the index rate from small to large, the index rate sequence is generated, and according to the minimum value and the maximum value of the index sequence, the numerical interval belonging to the index rate sequence is confirmed, a set of variable intervals is synchronously generated, the variable interval ∈ the numerical interval, a plurality of change processes are executed, the numerical range of the variable interval in each change process is inconsistent, the total number of index rates contained in each different variable interval is G i , wherein i represents different variable intervals, and the interval range associated with different variable intervals is recorded as F i , and the following is adopted: G i ÷F i =M i The density characteristics M i associated with the corresponding variable interval are confirmed, and from the different density characteristics M i associated with different variable intervals, the maximum value M i max is selected, the variable interval associated with M i max is recorded as the determined interval, and the different execution power associated with the determined interval is taken as the stable characteristics associated with the corresponding undetermined type.

4. The law enforcement record data archival management system of claim 1, wherein, The specific manner of the correction end for correcting the execution features of different data types is as follows: determining the data types associated with a single classification partition, confirming execution power from the stable features associated with the corresponding data types, performing mean value processing on the confirmed execution power, and confirming the power mean value associated with the corresponding data types; from the power features assigned to the corresponding classification partition, confirming the execution features associated with different data types inside the classification partition, identifying whether the execution features are consistent with the power mean value, and if not, marking the corresponding data type as an adjustment type. If the computing power feature associated with the corresponding classification partition is sufficient to complete the adjustment type of computing power adjustment process, the execution feature associated with the adjustment type is adjusted to the same computing power value as the computing power average, if not, the computing power averages associated with several adjustment types are processed by ratio, the computing power ratio column is confirmed, and the total execution feature associated with the adjustment type is divided according to the computing power ratio column. The computing power value after the division is taken as the execution feature of the adjustment type.

5. The law enforcement record data archival management system of claim 4, wherein, If the execution feature is consistent with the computing power average, the corresponding data type is directly marked as the standard type.

6. The law enforcement record data archival management system of claim 1, wherein, Also includes: The search index end confirms the specific assignment column associated with the search word input by the operator, and then confirms the archive data associated with the search word according to the specific assignment column stored in the archive data of different classification partitions, and outputs in time.

7. The law enforcement record data archival management system of claim 6, wherein, The specific way of confirming the archive data associated with the search word by the search index end is: According to the search word input and the assignment check table associated with it, the assignment associated with the internal character of the corresponding search word is confirmed, and the assignment column associated with the corresponding search word is confirmed, which is recorded as the search column; Then confirm the classification range associated with different classification partitions, confirm the classification range of this assignment column, mark the associated classification partition as the partition to be searched, and confirm the assignment column associated with different archive data in the partition to be searched; According to the confirmed search column and different assignment columns associated with different archive data, the same assignment between the search column and the corresponding assignment column is confirmed again, the proportion of the same assignment located in the corresponding search column is recorded, the assignment column with a proportion of 100% is selected as the associated column, and the arrangement order of the corresponding assignment in the search column is confirmed. The associated column with the same arrangement order is recorded as the selected column; If there is only one group of selected columns, the archive data associated with the selected column is directly outputted; If there are multiple groups of selected columns, select the selected column with the least number of assignments from the multiple groups of selected columns, and output the archive data associated with the selected column, if the number of assignments is the least and there are the same selected columns, the archive data associated with the corresponding selected column is outputted.

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