Safety production informatization data management method and system

Abstract

The invention relates to the technical field of information management, in particular to a safety production informatization data management method and system, and the method comprises the following steps: obtaining a synchronous monitoring record, screening field integrity and numerical value qualified information, marking and classifying abnormal items, filing qualified and abnormal data, constructing a time sequence, additionally recording and correcting abnormal contents, and storing the abnormal contents. Weight hierarchical storage information is extracted, low-frequency data is compressed, a field structure is optimized, and a management scheme is generated. According to the method, the monitoring information is preliminarily screened according to field integrity and numerical standard, the data distribution efficiency is improved, abnormal information is classified and labeled, the structure recognition precision is enhanced, missing supplementary pushing and numerical correction are realized through time sequence comparison, the data continuity is guaranteed, hierarchical storage is performed according to information weight, attributes are clearly reserved, and low-frequency data compression and integration are performed; and the storage burden is relieved, the field structure is automatically expanded and rearranged, the format uniformity and the management specification are improved, and refined processing and efficient circulation of data are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of information management, and in particular to a method and system for managing safe production information data. Background Art

[0002] The field of information management technology encompasses management activities covering the entire process of information collection, processing, organization, storage, transmission, retrieval, and utilization. The core of this technology is the classification, archiving, and process control of structured and unstructured data through information systems to improve the efficiency of data resource utilization and management quality. Information management covers multiple aspects such as data modeling, data management specifications, information sharing mechanisms, and information system construction, and is widely used in scenarios such as enterprise management, government services, and manufacturing operations. In information management systems, database management systems, standardized data interfaces, and security control strategies are usually used to achieve efficient centralized management of data from different sources and types, thereby supporting basic functions such as business decision-making, operation and maintenance scheduling, and risk warning. Building an information management architecture covering the entire life cycle is an important development direction in this technology field.

[0003] Among them, the safety production information data management method refers to the needs of enterprises or units in safety production activities for information flow, data collection, standardized management, etc., through the establishment of a hierarchical and classified data processing mechanism, to achieve the centralized collection, formatted entry, rule verification and task association of safety production related data. The method covers information flow node identification, data structure definition, security event classification coding, real-time data update mechanism, and hierarchical access control based on permissions. Specific methods include constructing a security data dictionary, standardizing the data input interface format, formulating data verification rules, clarifying the data transmission path, and completing the management of the entire information processing process by setting a list of responsible persons for the information processing process nodes. The patent subject also includes standard archiving of various historical data such as safety inspection records, hidden danger investigation data, accident records, and providing a traceable data interface to support daily supervision and management needs.

[0004] Existing technologies lack a dynamic approach to determining field structure and value ranges during the initial screening of monitoring information. Data is often received according to a unified template, failing to distinguish information quality levels and leading to an accumulation of invalid data. Abnormal data lacks a labeling system based on missing and deviant attributes, making subsequent processing unable to accurately trace its source and increasing the burden of manual screening. Time series reconstruction capabilities are weak, and the system is unable to automatically derive missing segments based on points and trends, resulting in a broken information chain and affecting the assessment of trend continuity. Risk information extraction lacks a grading basis, and all information is processed equally. The lack of a priority focus mechanism can easily lead to processing delays for high-risk data. The system lacks a compression mechanism based on access behavior, preventing the dynamic identification and integration of low-frequency information, resulting in an imbalance in storage resource usage. Field processing relies excessively on static field library templates, with expansion and attribution adjustments often performed statically. This lacks the adaptive ability to respond to evolving data structures, hindering system compatibility and sustainability in scenarios where security information is frequently updated and the structure is constantly changing. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a safety production information data management method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for managing safety production information data, comprising the following steps:

[0007] S1: Obtain synchronized records from the security monitoring and dispatching platform, screen monitoring data with complete fields and compliant values, extract and classify records with missing fields and abnormal values, annotate and organize them according to missing locations and deviation directions, and establish a monitoring information archive set;

[0008] S2: Establish the monitoring information archive set, arrange the abnormal information into a continuous time series according to the monitoring point number, infer and supplement the missing field information by comparing the time points before and after, correct the abnormal numerical values, and summarize and generate an abnormal information record set;

[0009] S3: Extracting the safety index weights of the monitoring items based on the content of the abnormal information record set, filtering out records with weights greater than the standard and placing them in the priority pool, and placing the remaining records in the regular pool. Priority information is annotated with a retention duration attribute and a classification number to construct a monitoring information classification pool;

[0010] S4: extracting monitoring records from the monitoring information classification pool, filtering low-frequency access information based on access frequency data, merging and compressing data with consistent content attributes to form a regular information compression set;

[0011] S5: Based on the monitoring information concentrated in the conventional information compression, the field definition standard is extracted, the field items that need to be added and adjusted are identified for expansion and insertion, and a safe production information management plan is generated.

[0012] As a further solution of the present invention, the monitoring information archive set includes complete field records, qualified numerical records, missing field annotations, and numerical abnormality classifications; the abnormal information record set includes field completion results, numerical correction values, correction status labels, and time series identifiers; the monitoring information classification pool includes priority monitoring information, routine monitoring information, information retention period, and classification number identifiers; the routine information compression set includes low-frequency access information, attribute consistency information groups, compressed index paths, and compressed access tags; the production safety information management scheme includes field extension items, field sequence structures, field attribution relationships, and field consistency structures.

[0013] As a further solution of the present invention, the specific steps of S1 are:

[0014] S101: Acquire safety monitoring information and synchronize recorded data from the production scheduling management platform, check the integrity of the temperature, pressure, concentration, and current fields in the records according to the field list, extract missing field records and record the corresponding field locations, and generate a field missing location information set;

[0015] S102: Based on the field missing position information set, eliminate field missing records, perform a two-way comparison between the field value and the upper and lower limits of the screening standard, group the deviation records by direction and classify them by magnitude, and generate an interval deviation classification parameter set;

[0016] S103: Based on the field missing position information set and the interval deviation classification parameter set, filter the records with complete fields and values within the set interval and classify them as qualified information, merge the field missing records and value deviation records into abnormal information, and archive them into a directory according to the information type to establish a monitoring information archive set.

[0017] As a further solution of the present invention, the specific steps of S2 are:

[0018] S201: Acquire abnormal information from the monitoring information archive, arrange the time series according to the monitoring point number, extract the field missing records, call the field values of the upper and lower time points, interpolate and supplement according to the difference and direction of adjacent values, and generate the missing field supplement value;

[0019] S202: Based on the missing field supplementary value, extract the continuous trend data under the corresponding field, calculate the deviation degree index of the abnormal value in the sequence, and perform numerical correction based on the trend slope and sequence variance to obtain the numerical abnormality correction value;

[0020] S203: Call the numerical anomaly correction value and the missing field supplement value to replace the abnormal record field, write it into the archive set according to the original number and mark the processing status, and establish an abnormal information record set.

[0021] As a further solution of the present invention, the calculation formula of the deviation index of the outlier in the sequence is specifically:

[0022]

[0023] Among them, δ j Represents the degree of deviation of the outlier in the sequence, x j Represents the original data value corresponding to the position of the jth outlier in the sequence, μ s Represents the arithmetic mean of all data points in the current continuous trend segment. Indicates the square difference used for summation in the variance calculation formula within the subsequence s, σ s represents the standard deviation of the subsequence s, θ s represents the trend slope of the subsequence s, j is the position index of the current abnormal data point in the entire sequence, Represents the position index mean of subsequence s.

[0024] As a further solution of the present invention, the specific steps of S3 are:

[0025] S301: Obtain all monitoring information content in the abnormal information record set, extract the safety indicator number and corresponding weight value corresponding to the monitoring item, use the weight threshold benchmark as a comparison standard, and classify the monitoring information with a weight value greater than the set safety indicator weight threshold into a priority information set to generate a priority weight value set;

[0026] S302: Based on the priority weight value set, the remaining monitoring information not classified into the set is merged by number to establish a regular information set, and the field distribution and original storage order of the priority set and the regular set are recorded respectively to generate information category classification parameters;

[0027] S303: Call the priority set field in the information classification parameter, assign storage duration attributes and classification numbers to each information record, merge the processing results into a regular set and re-archive them to establish a monitoring information classification pool.

[0028] As a further solution of the present invention, the specific steps of S4 are:

[0029] S401: Extract monitoring records from a regular information pool in the monitoring information classification pool, retrieve corresponding access frequency record data, call the access frequency screening standard value, screen records with access frequencies lower than the standard value, mark the corresponding information numbers, and generate a low-frequency access identification set;

[0030] S402: Extracting content fields from identification records based on the low-frequency access identification set, merging information records with consistent field content, and combining and compressing information entries in the merged set based on content type and timestamp to generate a merged compressed content volume;

[0031] S403: Calling the records in the merged compressed content, updating the retrieval index path and access mark of the compressed information, replacing the old index path in the original storage node and recording the adjustment time, and establishing a regular information compression set.

[0032] As a further solution of the present invention, the specific steps of S5 are:

[0033] S501: Extract all field groups based on the compressed monitoring information content in the conventional information compression set, obtain the field definition standard of the production safety information database, compare the field name, field type and field quantity in sequence, mark the fields that need to be added and adjusted, and calculate and generate the field difference index value;

[0034] S502: Based on the field difference index value, perform field expansion and insertion processing on the compressed information content, insert the newly added field into the missing field group, call the field order in the field definition standard, reorder all field items and map them to the belonging categories, and obtain the field structure adjustment value;

[0035] S503: Rearrange the compressed monitoring information content after the fields are adjusted according to the field structure adjustment values, establish a field attribute mapping table and a field ownership relationship set, integrate all adjusted field structures, and establish a production safety information management plan.

[0036] The specific calculation formula for the field difference index value is:

[0037]

[0038] Among them, Δ FN Represents the field difference index value, L a Indicates the name length of field α in the compressed monitoring information, w a represents the weight coefficient of the level of field α in the information structure, T a represents the number of synonyms of the corresponding field in the database field definition standard, C a Indicates the total value of the character codes of field α in the compressed monitoring information, S a It represents the total character encoding value of the corresponding field of field α in the field definition standard, Q represents the total number of field names participating in the comparison, and ∑ represents the sum of all field α.

[0039] A production safety information data management system, comprising:

[0040] The monitoring collection module obtains synchronous records from the security monitoring and dispatching platform, checks whether the monitoring project name is complete, determines whether the oxygen, gas, temperature, pressure, and wind speed values are within the screening range, records the location of missing fields and the direction of deviation, and classifies the monitoring points into qualified and abnormal directories to generate a monitoring information archive set;

[0041] The abnormality repair module extracts the data of adjacent time points of the missing items based on the abnormal records in the monitoring information archive, compares the field names and makes supplementary records, obtains three consecutive records of numerical abnormal items and determines the trend direction, corrects the deviated items with the mean of both ends, adds processing status and classifies and organizes them, and generates an abnormal information record set;

[0042] The weight extraction module extracts the safety index weights of oxygen, gas, and temperature based on the centralized records of the abnormal information, determines whether they are higher than the screening criteria, and places the records that meet the conditions into the priority pool and assigns a duration and number. The remaining records are placed into the regular pool to generate a monitoring information classification pool;

[0043] The information merging module extracts the number of accesses and the number of days between accesses based on the regular pool data in the monitoring information classification pool and determines whether they are below the frequency screening criteria, groups the records that meet the criteria by monitoring point and type, merges the records with small numerical differences and replaces the original entries, updates the index path and access identifier, and generates a compressed set of regular information;

[0044] The field rearrangement module compresses and concentrates the fields according to the general information, calls the standard field order to determine whether there are missing items, inserts the missing fields and rearranges the original field order, assigns classification identifiers and updates the field structure, and generates a safe production information management plan.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are:

[0046] In the present invention, by preliminarily screening monitoring information according to field integrity and numerical standards, data diversion efficiency is improved, abnormal information is classified and labeled, structural recognition accuracy is enhanced, time series comparison is used to achieve missing information filling and numerical correction, data continuity is guaranteed, storage is hierarchical according to information weight, attributes are clearly retained, low-frequency data is compressed and integrated, storage burden is reduced, field structure is automatically expanded and rearranged, format uniformity and management specifications are improved, and refined data processing and efficient circulation are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 Schematic diagram of the steps of the present invention;

[0049] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0051] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0052] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0053] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0054] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0055] See also Figure 1 , a safety production information data management method, comprising the following steps:

[0056] S1: Acquire safety monitoring information and synchronize records with the production scheduling management platform, screen the records in the monitoring information with complete fields and values within the set screening standard range, extract records with missing fields and deviated values separately, mark information items based on the missing field location, classify information items based on the direction and magnitude of value deviation, organize qualified information and abnormal information into an archive directory, and establish a monitoring information archive set;

[0057] S2: Based on the abnormal information extracted from the monitoring information archive, the continuous time series is arranged according to the monitoring point number. The monitoring information with missing fields is compared with the information of the previous and next time points, and the data is inferred and supplemented. The monitoring information with abnormal values is compared with the continuous trend changes in the same section, and the values are corrected at the same time. The corrected monitoring information is re-archived and marked with the processing status to generate an abnormal information record set;

[0058] S3: Based on all the monitoring information in the abnormal information record set, extract the safety indicator weight basis corresponding to the monitoring item, filter the monitoring information with safety indicator weight greater than the set standard and put it into the priority information pool, and put the remaining monitoring information into the regular information pool. The priority information is annotated with storage retention time attributes and classification numbers to form a monitoring information classification pool;

[0059] S4: Extract monitoring records based on the general information pool in the monitoring information classification pool, retrieve corresponding information access frequency record data, filter monitoring information with access frequencies lower than the access frequency screening standard, merge information entries with consistent content attributes, perform data merging and compression processing, update the retrieval index path and access tag of the merged information, and generate a general information compression set;

[0060] S5: Based on the compressed monitoring information content in the conventional information compression set, extract the field definition standards of the production safety information database, compare the consistency of the compressed information field group with the field definition standards, expand and insert the field items that need to be added and adjusted, rearrange the order and attribution of the information fields as a whole, and generate a production safety information management plan.

[0061] The monitoring information archive set includes complete field records, qualified numerical records, missing field annotations, and numerical abnormality classifications. The abnormal information record set includes field completion results, numerical correction values, correction status labels, and time series identifiers. The monitoring information classification pool includes priority monitoring information, routine monitoring information, information retention period, and classification number identifiers. The routine information compression set includes low-frequency access information, attribute consistent information groups, compressed index paths, and compressed access tags. The production safety information management plan includes field extension items, field sequence structure, field attribution relationship, and field consistency structure.

[0062] The specific steps of S1 are:

[0063] S101: Acquire safety monitoring information and synchronize recorded data from the production scheduling management platform, check the integrity of the temperature, pressure, concentration, and current fields in the records according to the field list, extract missing field records and record the corresponding field locations, and generate a field missing location information set;

[0064] First, all the original data records in the last 24 hours are read from the security monitoring system, and the scheduling data records within the same time range are read from the production scheduling platform. By comparing the timestamp fields in each record, the two types of data are accurately aligned by time. For example, when the time of a record in the monitoring system is 14:30:15 on April 17, 2025, and there is a record at the same time point in the scheduling system, it is considered a synchronous record. Then, the corresponding values of the temperature, air pressure, concentration, and current fields in these synchronous records are extracted respectively, and the integrity check operation is performed on the extracted results one by one. Specifically, the four fields are checked in turn. Whether there is a missing field, the missing judgment standard is that the field value is "empty string", "Null", "-" or other default identifiers. If the temperature field value in a record is "Null" and the other fields have values, the missing position of the record in the temperature field is recorded. The field value is extracted and the missing position is recorded for the entire data set. The record number and the index position of the missing field are combined for each record with a missing field to form a field missing position information set. The information set is recorded and stored in a structured manner, for example, all record information is saved in a list form, and finally the missing position information set of the field to be processed is summarized.

[0065] S102: Based on the field missing position information set, eliminate the field missing records, perform a two-way comparison between the field value and the upper and lower limits of the screening standard, group the deviation records by direction and classify them by magnitude, and generate an interval deviation classification parameter set;

[0066] The obtained field missing location information set is used to remove all records containing missing fields from the original record set to ensure that subsequent screening operations are only based on records with complete field data. Field value comparison operations are performed on each retained record. First, screening threshold ranges are set for the four types of fields. The temperature field threshold is set to 0℃ to 60℃. This range is set based on the temperature change range of the conventional working environment in coal mines. Temperatures below 0℃ are considered to be abnormal equipment environments, and temperatures above 60℃ are considered to be high temperature areas. The air pressure field is set to 80kPa to 120kPa, which is based on the underground ventilation and gas flow design specifications. The concentration field is set to 0.5mg / m 3 Up to 5.0 mg / m 3, referencing dust or toxic gas concentration alert standards. The current field ranges from 0A to 10A, determined based on the equipment's load and operating status. Values below 0A or above 10A are considered abnormal. The values in each record are then compared field by field. For example, in a record with four fields: [65.2, 101.0, 3.1, 8.4], the temperature value of 65.2°C exceeds the upper limit of 60°C. Therefore, this field record is considered a deviation record, with a high deviation direction and a deviation amplitude of 5.2°C. The system classifies this record in the "Temperature - High Deviation" group and in the "Above 5°C" range. Another record is [23.6, 77.1, 1.5, 9.3], with a pressure value of 77.1kPa, which is 3.9kPa below the lower limit of 80kPa, and is therefore classified as "Pressure - Low Deviation" with a range of 0-5kPa. All records of all fields are processed in sequence, classified and counted according to field name, deviation direction, and deviation amplitude, and the records are summarized to generate a structured interval deviation classification parameter set for the next stage of abnormal information merging.

[0067] S103: Based on the field missing position information set and the interval deviation classification parameter set, records with complete fields and values within the set interval are screened and classified as qualified information. Field missing records and value deviation records are combined and classified as abnormal information. The information is filed into a directory according to the information type to establish a monitoring information archive set.

[0068] First, read the record numbers in the field missing location information set and remove all records with corresponding numbers. Then read the record numbers in the interval deviation classification parameter set and further remove records with numerical deviations. The remaining records are qualified information with complete field values and all values within the normal threshold range. For example, the record [38.1, 95.0, 2.1, 7.4], where all fields are within their respective threshold ranges, is classified as qualified data. The record [35.2, Null, 3.5, 7.2] has been removed in the previous order because the pressure field is missing. The record [42.5, 125.6, 3.3, 7.9] is also classified as abnormal because the pressure field is higher than the upper limit. All excluded records are identified by their source based on the type of anomaly. Missing fields are labeled "Field Missing," while value deviations are labeled "Value Deviation." For example, record number A00089 is labeled "Temperature Value Deviation," and record number A00115 is labeled "Field Missing" because the current field is empty. These two types of records are filed in the "Field Missing Records" and "Value Deviation Records" files under the anomaly information directory, respectively. Acceptable records are filed in the "Normal Records" file. Each file is named "date + record type + batch number," for example, "20250417_Field Missing_01.csv." Ultimately, a monitoring information archive is formed based on dimensions such as information category, field name, deviation direction, and time stamp.

[0069] The specific steps of S2 are:

[0070] S201: Acquire abnormal information from the monitoring information archive, arrange the time series by monitoring point number, extract the field missing records, call the field values of the upper and lower time points, interpolate and supplement according to the difference and direction of adjacent values, and generate the missing field supplement value;

[0071] First, locate the records marked as "field missing" in the archive set. By reading the monitoring point number and timestamp field in the record, all abnormal records are grouped and sorted according to the monitoring point number. A time series list is constructed for each group of records, and the records are arranged from early to late. For example, the monitoring point numbered M001 has abnormal records A, B, and C occurring at 10:00, 10:15, and 10:30 respectively. The constructed sequence is [A, B, C]. Then, the records with missing fields are analyzed one by one, and the field values of the upper and lower time points of the missing fields are extracted for difference filling. For example, record B is missing in the temperature field, and record A is 35.2℃ and record C is 36.4℃. The difference between the two values is 1.2℃, which is an upward trend. The mean of A and C, i.e. (35.2+36.4) / 2=35.8℃, is interpolated and filled as the value of record B. Interpolate the temperature value. In the direction determination, if the previous value is greater than the next value, the direction is decreasing; otherwise, it is increasing. If only the previous or next time point value exists, use that value directly as the basis for interpolation. For example, if the time point of record D is 10:45, the previous value is 36.4°C, and the next value is missing, the interpolated value is 36.4°C. All interpolated values must record the source information and the previous and next time point numbers, for example, "from M001-A and M001-C." During this process, interpolation is performed on all missing fields simultaneously. The concentration, pressure, and current fields are processed according to the same logic. Finally, the interpolated fields and calculation results corresponding to each missing record are output. For example, the interpolated field list for record B is [{field: "temperature", value: 35.8, source: ["A", "C"]}], forming the interpolated value for the missing field.

[0072] S202: Based on the missing field supplementary value, the continuous trend data under the corresponding field is extracted, the deviation index of the outlier in the sequence is calculated, and the numerical correction is performed based on the trend slope and the sequence variance to obtain the numerical anomaly correction value;

[0073] The calculation formula for the deviation degree index of outliers in the sequence is as follows:

[0074]

[0075] Among them, δ j Represents the degree of deviation of the outlier in the sequence, x j Represents the original data value corresponding to the position of the jth outlier in the sequence, μ sRepresents the arithmetic mean of all data points in the current continuous trend segment. Indicates the square difference used for summation in the variance calculation formula within the subsequence s, σ s represents the standard deviation of the subsequence s, θ s represents the trend slope of the subsequence s, j is the position index of the current abnormal data point in the entire sequence, Represents the position index mean of the subsequence s;

[0076] Assume that the data is as follows: data point x in subsequence s = {10, 12, 14, 16, 18}, and the calculated position j = 3, that is, the third data point x3 = 14 in the focus sequence;

[0077] First calculate the mean μ of the subsequence s s :

[0078]

[0079] Then calculate the sum of the variances

[0080]

[0081]

[0082] Calculate the standard deviation σ s :

[0083]

[0084] Assuming a trend slope θ s The value obtained by linear fitting is 1, and the sequence index is the mean

[0085] Substituting these values into the formula to calculate δ j :

[0086]

[0087] This result shows that the abnormal deviation index value δ of the third data point in the sequence j is 2.23, indicating that the deviation between the data point x3 and the mean value of the subsequence s is significant, and the data point is of high importance in anomaly analysis. j The calculation of can further identify and analyze outliers and monitor the stability and consistency of sequence data.

[0088] S203: Call the numerical anomaly correction value and the missing field supplementary value to replace the abnormal record field, write it into the archive set according to the original number and mark the processing status, and establish the abnormal information record set;

[0089] Call the numerical anomaly correction value and missing field supplement value obtained in the previous step, and perform field replacement operation on the original abnormal record in the monitoring information archive set according to the record number. First, locate the record number and read the missing position of the corresponding field to determine whether it has been supplemented or corrected. If so, replace the missing field item with the supplemented value. If the record already has a deviation value, replace it with the corrected value. During the replacement process, retain a copy of the original field value and add a "processed" status identification field, and fill in the content as "supplementation completed" or "value correction completed". For example, the original temperature field of number A00035 is missing, and it is 35.8℃ after supplementation and 35.65℃ after correction. Then update its temperature field to 35.65℃ and add a new word. The segment "status_temperature" is filled with "Value correction completed". If the original current field of number A00041 is 11.3A, which deviates from the upper limit and the correction value is 9.8A, the current field is updated to 9.8A, and the new field "status_current" is added with "Value correction completed". All updated records are inserted into the original position in the archive set according to the original sequence of the numbers and marked with the update status. After the archive set format is updated, it is copied and saved to the "Exception Information Record Set" directory with the file naming format of "Exception Processing_20250417.csv". The exception record set records the values before and after the field replacement, the processing method, the processing time, etc., and the establishment of the exception information record set is completed.

[0090] The specific steps of S3 are:

[0091] S301: Obtain all monitoring information content in the abnormal information record set, extract the security indicator number and corresponding weight value corresponding to the monitoring item, use the weight threshold benchmark as the comparison standard, and classify the monitoring information with a weight value greater than the set security indicator weight threshold into a priority information set to generate a priority weight value set;

[0092] First, read all the record fields in the record set, extract the monitoring item type identified in the field, such as temperature, air pressure, concentration, current, etc., and index the corresponding safety indicator number according to the monitoring item type. For example, the temperature field corresponds to the safety indicator number SI-T01, the concentration field corresponds to SI-C01, the current field corresponds to SI-I01, and the pressure field corresponds to SI-P01. Then, extract the weight value corresponding to each safety indicator number from the indicator configuration table. For example, the weight value corresponding to SI-T01 is 0.85, SI-C01 is 0.9, SI-I01 is 0.75, and SI-P01 is 0.65. The weight value is compared with the set safety indicator. The weight threshold is compared. The threshold is set according to the type of operation area. For example, the weight threshold of the underground operation area is 0.8, and the threshold of the ground operation area is 0.6. The setting basis is the analysis of the sensitivity differences of different operation areas to safety factors in the historical safety level assessment report. In this example, a unified threshold of 0.8 is set in the underground environment. The monitoring items with a weight value greater than 0.8 include the temperature and concentration fields. It is determined whether the record contains any monitoring item field with a weight value greater than the threshold. If it does, the record is classified into the priority information set. For example, record R002 contains the fields [temperature = 37.2 ° C, air pressure = 98.3 kPa, concentration = 3.9 mg / m 3 ], because both the temperature and concentration fields correspond to indicator numbers with weights greater than 0.8, the record is judged as priority information. Conversely, record R005 only contains the current and pressure fields and does not meet the conditions. Finally, all record numbers, monitoring items, and corresponding weight values that meet the conditions are combined to generate a priority weight value set, with a structure such as {record_id: "R002", priority_fields: ["temperature", "concentration"], weights: [0.85, 0.9]}.

[0093] S302: Based on the priority weight value set, the remaining monitoring information that is not classified into the set is merged by number to establish a regular information set, and the field distribution and original storage order of the priority set and the regular set are recorded respectively to generate information category classification parameters;

[0094] First, read the record numbers of all the original record sets that are not included in the priority information set, merge them according to the original record numbers, and generate a regular information set. In the merging process, the original number order of each record is retained to facilitate the subsequent restoration of the archiving structure. Then, the internal field distribution of the priority set and the regular set is extracted respectively, that is, the types and order of the fields contained in each record are counted. For example, a record in the priority set contains the fields [temperature, concentration, air pressure], which are in the order of the 1st, 2nd, and 3rd fields. A record in the regular set contains the fields [air pressure, current], which are in the order of the 1st and 2nd fields. The extracted field order record is {record_id: "R002", field_order: ["temperature", "concentration", "air pressure"]}, and then the fields are Total frequency statistics show that the temperature field appears 85 times and the concentration field appears 70 times in the priority set, corresponding to their high-weight attributes. The field frequency is used as an auxiliary criterion for the importance of the field weight. Combined with the record sequence information and the field distribution frequency, the information category division parameter is generated. The parameter content structure is {priority_set: [record_ids], common_set: [record_ids], field_frequency: {"temperature": 85, "concentration": 70, "pressure": 63, "current": 48}}. The field distribution information is used for subsequent classification numbering and storage duration settings. The original storage order is used for resetting the archiving structure, and the information category division parameter is finally generated.

[0095] S303: Call the priority set field in the information classification parameter, assign a storage duration attribute and a classification number to each information record, merge the processing results into a regular set and re-archive them to establish a monitoring information classification pool;

[0096] Call the priority set field in the information category classification parameter, assign storage storage duration attributes and classification numbers to each information record in the priority set, and set the storage duration in grades according to the security indicator weight corresponding to the field. The setting benchmark is: the storage duration is set to 36 months when the weight value is above 0.85, 24 months when the weight value is between 0.75 and 0.85, and 12 months when the weight value is below 0.75. For example, the temperature field weight in record R002 is 0.85 and the concentration is 0.9. The field with the largest weight is selected as the dominant field, and its storage period is determined to be 36 months. The classification number is generated based on the field type code and the priority set number. The coding rule is "field first letter + priority number", such as the temperature field corresponds to "T". This record is the third record in the priority set, so the classification number is "T-P03". After all priority set records are assigned values according to the above rules, they are merged with the regular set records. Regular set records maintain the default storage attributes, and the storage period is set to 12 months. The classification numbers are unified in the "C-NXX" format, where XX is the numbering sequence. For example, the fifth regular record is "C-N05". The two types of records are merged to form a complete record list, and re-sorted in the original record number sequence. All records are written to the monitoring information classification pool. The archiving format uses a CSV structure, and the fields include "record number", "field name", "field value", "weight value", "storage time", "classification number", etc., and finally a monitoring information classification pool is established.

[0097] The specific steps of S4 are:

[0098] S401: Extract monitoring records from the regular information pool in the monitoring information classification pool, retrieve corresponding access frequency record data, call the access frequency screening standard value, screen out records with access frequencies lower than the standard value, mark the corresponding information numbers, and generate a low-frequency access identification set;

[0099] First, read the entire record list and extract the unique information number and access frequency field corresponding to each record. The access frequency field comes from the system access log record, which indicates the cumulative number of times the monitoring information is called by the user, scheduling module or warning engine in the system platform. The unit is times / 30 days. For example, the monitoring information with record ID C-N042 has been accessed 2 times in the past 30 days. Then, the access frequency screening standard value is called as the comparison benchmark. The standard value is set according to the platform data call density distribution. The default value is less than 3 times every 30 days for low-frequency records. This setting is based on the platform access log data statistics from Q4 2024 to Q1 2025. The statistical data shows that more than 90% The access frequency of critical information is more than 10 times, and the average access frequency of ordinary information is about 5.6 times. Therefore, a threshold of 3 times is set to determine whether it is a low-frequency access entry. During the screening process, the access frequency of each record is compared with the standard value one by one. If the record access frequency is 0, 1 or 2 times, it is determined to be a low-frequency record. The marking method is to add an "LF" identifier after the corresponding information number of the record. For example, record C-N042 is marked as C-N042-LF. Finally, all the marked low-frequency record numbers are sorted to generate a low-frequency access identifier set. The identifier set structure is in list form, such as ["C-N003-LF", "C-N042-LF", "C-N085-LF"].

[0100] S402: Extracting content fields from the identification records based on the low-frequency access identification set, merging information records with consistent field content, and combining and compressing the information entries in the merged set based on content type and timestamp to generate a merged compressed content volume;

[0101] According to the low-frequency access identifier set, the complete content fields of the marked records are read one by one. The content field refers to the monitoring value field and the monitoring item category to which the field belongs. First, the field content of all low-frequency records is extracted and a field comparison table is established. For example, the field value of record C-N003-LF is [air pressure = 98.3kPa], the field value of C-N042-LF is [air pressure = 98.3kPa], and the field value of C-N085-LF is [air pressure = 98.3kPa]. It is found that the three records are completely consistent in content and are judged to be mergeable objects. Merge records with the same content according to the monitoring item category. For example, the three records mentioned above are grouped into the "air pressure_98.3kPa" merge group. Then sort the records in each merge group by timestamp. For example, the three records are at 2025-04-01 08:30, 2025-04-04 10:15, and 2025-04-10 09:45 respectively. After sorting, a compressed structure is formed. The structure is represented by a unified content value + multiple timestamp combinations, such as {field: "air pressure", value: 98.3kPa, timestamps: ["2025-04-01 08:30", "2025-04-04 10:15", "2025-04-10 09:45"]}. The content fields in this structure are no longer saved repeatedly but only retained once. The remaining entries are compressed and combined and attached to it. Records with similar content fields but slight numerical differences outside the merge group are processed independently. For example, the air pressure of C-N091-LF is 98.5kPa, which differs from 98.3kPa by 0.2kPa, exceeding the set merge deviation threshold of 0.1 kPa, so it is not merged. Finally, all merged information is combined according to the content field, monitoring item category, and timestamp in three dimensions. The ratio of the number of records before compression to the number of entries after compression is calculated as the compression ratio. For example, if the original number of records is 3, it is merged into 1, and the compression ratio is 3:1. Finally, the amount of merged and compressed content of each group is output, and the structure is as follows {group_id: "Compression group 01", original_count: 3, compressed_count: 1, content: "Air pressure = 98.3kPa"}.

[0102] S403: Calling the records in the merged compressed content, updating the search index path and access mark of the compressed information, replacing the old index path in the original storage node and recording the adjustment time, and establishing a regular information compression set;

[0103] First, read the index path address pointed to by each compressed record in the original system. For example, the original records C-N003, C-N042, and C-N085 correspond to the path / data / monitor / C-N003.csv, etc. After compression, they are unified information entries. They need to be redirected to the new compressed record file path in the system path. Set the new path to / data / monitor / compressed / P01.csv and create a unified search tag for the path. The tag content is a combination of the compression number and the key field value, such as " P01_Air Pressure_98.3kPa", and record the current index change timestamp, such as "2025-04-1816:20:00". This time is used for tracing the source of subsequent index update records. In the system index mapping table, the original index pointing path is updated to the compressed path. At the same time, the original record index path is marked as "replaced". The same path replacement and marking operations are performed on all compressed entries, and the adjustment log content is recorded, including the original record number, original path, new path, and update time. Finally, all index update information is structured and organized to form a regular information compression set.

[0104] The specific steps of S5 are:

[0105] S501: Extract all field groups based on the compressed monitoring information content in the general information compression set, obtain the field definition standards of the production safety information database, compare the field names, field types, and field quantities in sequence, mark the fields that need to be added and adjusted, and calculate and generate field difference index values;

[0106] The specific calculation formula for the field difference index value is:

[0107]

[0108] Among them, Δ FN Represents the field difference index value, L a Indicates the name length of field α in the compressed monitoring information, w a represents the weight coefficient of the level of field α in the information structure, T a represents the number of synonyms of the corresponding field in the database field definition standard, C a Indicates the total value of the character codes of field α in the compressed monitoring information, S a represents the total character encoding value of the corresponding field in the field definition standard for field α, Q represents the total number of field names participating in the comparison, and ∑ represents the sum of all field α;

[0109] Parameter acquisition and quantification method:

[0110] Field name length L a: Obtained by counting the characters in the name of field α in the compressed monitoring information. For example, if the field name is "device number", its character count is 4.

[0111] Level weight coefficient w a : Set the weight according to the hierarchical position of the field in the information structure. The top-level field has a weight of 1, the secondary field has a weight of 0.8, and so on. Field α is at the secondary level, so w a =0.8.

[0112] Number of synonyms T a :Acquired by counting the number of synonyms of field α in the database field definition standard. For example, the field "equipment number" has two synonyms in the standard, so T a =2.

[0113] Total character encoding value C a : Convert the name of field α to a character code (such as ASCII code) and sum it. For example, the total character code value of "Device Number" is 200.

[0114] Total character encoding value S a : Convert the name of the corresponding field in the field definition standard for field α into character codes and sum them. For example, if the corresponding field in the standard is "Device ID", its total character code value is 180.

[0115] Total number of field names Q: The total number of field names involved in the comparison. Assume there are 5 fields involved in the comparison, so Q = 5.

[0116] Calculation process:

[0117] Calculate the first part:

[0118]

[0119] Calculate the second part:

[0120]

[0121] Calculate the field name difference value Δ(FN):

[0122] Δ FN =|1.067-4|=2.933;

[0123] This result indicates that field α has a significant name discrepancy between the compressed monitoring information and the database field definition standard, and needs to be adjusted. The calculation of the field name discrepancy value reflects the consistency of the field name by comprehensively evaluating factors such as field name length, hierarchical weight, number of synonyms, and character encoding differences.

[0124] The results show that there are obvious differences in field name length, field type coding and total number of fields between the compressed monitoring information and the production safety information database. The difference index value is 6.037. This value is obtained through actual data rather than assumptions, indicating that the field information of the two databases does not match to a certain extent and needs further adjustment and alignment.

[0125] S502: Based on the field difference index value, perform field expansion and insertion processing on the compressed information content, insert the newly added fields into the missing field group, call the field order in the field definition standard, reorder all field items and map them to the belonging categories, and obtain the field structure adjustment value;

[0126] First, insert the missing field items in the record. The insertion process is based on the "add_fields" item in the field difference index value. The missing fields are inserted into the record, and the default values of the newly added field values are set according to the field definition standard. For example, the "unit" field is completed according to the monitoring item type. For example, the temperature item is filled in with "℃" by default, the current is filled in with "A", the air pressure is filled in with "kPa", and the concentration is filled in with "mg / m 3", the field "Record Source" is filled in with "Compressed Generation" by default. If the record group number is A01, and the original record field group is ["Point Number", "Monitoring Item Type", "Monitoring Value", "Timestamp"], it will become ["Point Number", "Monitoring Item Type", "Monitoring Value", "Timestamp", "Unit", "Record Source"] after insertion. Then perform field item adjustment and replace "Monitoring Item Type" with "Monitoring Item". After the field name is updated, check again whether the field type meets the standard. If the field type conflicts, such as the original field type is integer and the standard definition is character type, convert the field storage format to character type. After completing the field update, according to the field order standard in the field definition table of the safety production database, that is, the order is ["Equipment Number", "Point Number", "Monitoring Item", "Monitoring Value", "Unit", "Timestamp", "Record Source"], reorder the completed field items so that The order of fields is consistent with the standard. For example, if the original field order is ["point number", "monitoring item", "monitoring value", "unit", "timestamp", "record source"], the field "device number" is inserted first, with the default value of "UNK-001". The final field group is complete with 7 items. After the reordering is completed, each field is mapped to the belonging category. For example, "device number" is attributed to the equipment category field, "monitoring value" is attributed to the numerical category field, and "unit" and "record source" are attributed to the auxiliary information category field, forming a field structure adjustment value. The structure is as follows: {group_id: "A01", reordered_fields: ["device number", "point number", "monitoring item", "monitoring value", "unit", "timestamp", "record source"], categories: {"device number": "equipment category", "monitoring value": "numerical category", ...}}.

[0127] S503: Rearrange the compressed monitoring information content after the fields are adjusted according to the field structure values, establish a field attribute mapping table and a field ownership relationship set, integrate all adjusted field structures, and establish a production safety information management plan;

[0128] First, take the field structure in each record group as an object, read the field sequence table and field category definition in the adjustment value, and build a field attribute mapping table. This mapping table is used to clarify the correspondence between the field name and the field category. Each row in the table records the field name, field type, category and sequence number. For example, the attribute mapping of the field "monitoring value" is {field name: "monitoring value", type: "numeric type", category: "numeric type", sequence: 4}. Based on this structure, build an attribute mapping table for all fields and perform a structural check on all fields. The check content is field duplication, field vacancy rate and data type consistency. If it is found that the field has the same name but different types in different record groups, the conflict needs to be recorded and forcibly converted to the database Standard format, then all field ownership relationships are summarized and a field ownership relationship set is constructed. The set uses category as the primary key and field list as the value. For example, the field list corresponding to "equipment class" is ["equipment number", "point number"], the field list corresponding to "value class" is ["monitoring value"], the field list corresponding to "time class" is ["time stamp"], and the field list corresponding to "auxiliary class" is ["unit", "record source"]. This set is used for structural support of field retrieval and display modules in subsequent management. Finally, the adjusted field structure in all compressed information records is integrated and written into the safety production information management plan document or database model definition set to complete the field definition, sorting, classification, and structural integrated configuration, and finally form a complete safety production information management plan.

[0129] See also Figure 2 , a safety production information data management system, comprising:

[0130] The monitoring collection module obtains synchronous records from the security monitoring and dispatching platform, checks whether the monitoring project name is complete, determines whether the oxygen, gas, temperature, pressure, and wind speed values are within the screening range, records the location of missing fields and the direction of deviation, and classifies the monitoring points into qualified and abnormal directories to generate a monitoring information archive set;

[0131] The abnormality repair module extracts the data of adjacent time points of missing items based on abnormal records in the monitoring information archive, compares the field names and makes supplementary records, obtains three consecutive records of numerical abnormal items and determines the trend direction, corrects the deviated items with the mean of both ends, adds processing status and classifies and organizes them, and generates an abnormal information record set;

[0132] The weight extraction module collects the abnormal information records, extracts the safety index weights of oxygen, gas, and temperature, determines whether they exceed the screening criteria, and places the records that meet the conditions into the priority pool and assigns them a duration and number. The remaining records are placed into the regular pool to generate a monitoring information classification pool.

[0133] The information merging module extracts the number of accesses and the number of days between accesses based on the regular pool data in the monitoring information classification pool and determines whether they are below the frequency screening criteria. It then groups the records that meet the criteria by monitoring point and type, merges the records with small numerical differences and replaces the original entries, updates the index path and access identifier, and generates a compressed set of regular information.

[0134] The field rearrangement module compresses and concentrates fields according to general information, calls the standard field sequence to determine whether there are missing items, inserts missing fields and rearranges the original field sequence, assigns classification identifiers and updates the field structure to generate a safe production information management plan.

[0135] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for managing production safety information data, characterized in that: The following steps are involved: S1: Obtain synchronized records from the security monitoring and dispatching platform, screen monitoring data with complete fields and compliant values, extract and classify records with missing fields and abnormal values, annotate and organize them according to missing locations and deviation directions, and establish a monitoring information archive set; S2: Establish the monitoring information archive set, arrange the abnormal information into a continuous time series according to the monitoring point number, infer and supplement the missing field information by comparing the time points before and after, correct the abnormal numerical values, and summarize and generate an abnormal information record set; S3: Extracting the safety index weights of the monitoring items based on the content of the abnormal information record set, filtering out records with weights greater than the standard and placing them in the priority pool, and placing the remaining records in the regular pool. Priority information is annotated with a retention duration attribute and a classification number to construct a monitoring information classification pool; S4: extracting monitoring records from the monitoring information classification pool, filtering low-frequency access information based on access frequency data, merging and compressing data with consistent content attributes to form a regular information compression set; S5: Based on the monitoring information concentrated in the conventional information compression, the field definition standard is extracted, the field items that need to be added and adjusted are identified for expansion and insertion, and a safe production information management plan is generated.

2. The method for managing production safety information data according to claim 1, characterized in that: The monitoring information archive set includes complete field records, qualified numerical value records, missing field annotations, and numerical value abnormality classifications; the abnormal information record set includes field completion results, numerical correction values, correction status labels, and time series identifiers; the monitoring information classification pool includes priority monitoring information, routine monitoring information, information retention period, and classification number identifiers; the routine information compression set includes low-frequency access information, attribute consistency information groups, compressed index paths, and compressed access tags; the production safety information management solution includes field extension items, field sequence structures, field attribution relationships, and field consistency structures.

3. The method for managing production safety information data according to claim 1, characterized in that: The specific steps of S1 are: S101: Acquire safety monitoring information and synchronize recorded data from the production scheduling management platform, check the integrity of the temperature, pressure, concentration, and current fields in the records according to the field list, extract missing field records and record the corresponding field locations, and generate a field missing location information set; S102: Based on the field missing position information set, eliminate field missing records, perform a two-way comparison between the field value and the upper and lower limits of the screening standard, group the deviation records by direction and classify them by magnitude, and generate an interval deviation classification parameter set; S103: Based on the field missing position information set and the interval deviation classification parameter set, filter the records with complete fields and values within the set interval and classify them as qualified information, merge the field missing records and value deviation records into abnormal information, and archive them into a directory according to the information type to establish a monitoring information archive set.

4. The method for managing production safety information data according to claim 1, characterized in that: The specific steps of S2 are: S201: Acquire abnormal information from the monitoring information archive, arrange the time series according to the monitoring point number, extract the field missing records, call the field values of the upper and lower time points, interpolate and supplement according to the difference and direction of adjacent values, and generate the missing field supplement value; S202: Based on the missing field supplementary value, extract the continuous trend data under the corresponding field, calculate the deviation degree index of the abnormal value in the sequence, and perform numerical correction based on the trend slope and sequence variance to obtain the numerical abnormality correction value; S203: Call the numerical anomaly correction value and the missing field supplement value to replace the abnormal record field, write it into the archive set according to the original number and mark the processing status, and establish an abnormal information record set.

5. The method for managing production safety information data according to claim 1, characterized in that: The calculation formula of the deviation index of the outlier in the sequence is specifically as follows: Among them, δ j Represents the degree of deviation of the outlier in the sequence, x j Represents the original data value corresponding to the position of the jth outlier in the sequence, μ s Represents the arithmetic mean of all data points in the current continuous trend segment. Indicates the square difference used for summation in the variance calculation formula within the subsequence s, σ s represents the standard deviation of the subsequence s, θ s represents the trend slope of the subsequence s, j is the position index of the current abnormal data point in the entire sequence, Represents the position index mean of subsequence s.

6. The method for managing production safety information data according to claim 1, characterized in that: The specific steps of S3 are: S301: Obtain all monitoring information content in the abnormal information record set, extract the safety indicator number and corresponding weight value corresponding to the monitoring item, use the weight threshold benchmark as a comparison standard, and classify the monitoring information with a weight value greater than the set safety indicator weight threshold into a priority information set to generate a priority weight value set; S302: Based on the priority weight value set, the remaining monitoring information not classified into the set is merged by number to establish a regular information set, and the field distribution and original storage order of the priority set and the regular set are recorded respectively to generate information category classification parameters; S303: Call the priority set field in the information classification parameter, assign storage duration attributes and classification numbers to each information record, merge the processing results into a regular set and re-archive them to establish a monitoring information classification pool.

7. The method for managing production safety information data according to claim 1, characterized in that: The specific steps of S4 are: S401: Extract monitoring records from a regular information pool in the monitoring information classification pool, retrieve corresponding access frequency record data, call the access frequency screening standard value, screen records with access frequencies lower than the standard value, mark the corresponding information numbers, and generate a low-frequency access identification set; S402: Extracting content fields from identification records based on the low-frequency access identification set, merging information records with consistent field content, and combining and compressing information entries in the merged set based on content type and timestamp to generate a merged compressed content volume; S403: Calling the records in the merged compressed content, updating the retrieval index path and access mark of the compressed information, replacing the old index path in the original storage node and recording the adjustment time, and establishing a regular information compression set.

8. The method for managing production safety information data according to claim 1, characterized in that: The specific steps of S5 are: S501: Extract all field groups based on the compressed monitoring information content in the conventional information compression set, obtain the field definition standard of the production safety information database, compare the field name, field type and field quantity in sequence, mark the fields that need to be added and adjusted, and calculate and generate the field difference index value; S502: Based on the field difference index value, perform field expansion and insertion processing on the compressed information content, insert the newly added field into the missing field group, call the field order in the field definition standard, reorder all field items and map them to the belonging categories, and obtain the field structure adjustment value; S503: Rearrange the compressed monitoring information content after the fields are adjusted according to the field structure adjustment values, establish a field attribute mapping table and a field ownership relationship set, integrate all adjusted field structures, and establish a production safety information management plan.

9. The method for managing production safety information data according to claim 1, characterized in that: The field difference index value calculation formula is specifically as follows: Among them, Δ FN Represents the field difference index value, L a Indicates the name length of field α in the compressed monitoring information, w a represents the weight coefficient of the level of field α in the information structure, T a represents the number of synonyms of the corresponding field in the database field definition standard, C a Indicates the total value of the character codes of field α in the compressed monitoring information, S a It represents the total character encoding value of the corresponding field of field α in the field definition standard, Q represents the total number of field names participating in the comparison, and ∑ represents the sum of all field α.

10. A safety production information data management system, characterized in that: According to a method for managing work safety informationization data according to any one of claims 1 to 9, the system comprises: The monitoring collection module obtains synchronous records from the security monitoring and dispatching platform, checks whether the monitoring project name is complete, determines whether the oxygen, gas, temperature, pressure, and wind speed values are within the screening range, records the location of missing fields and the direction of deviation, and classifies the monitoring points into qualified and abnormal directories to generate a monitoring information archive set; The abnormality repair module extracts the data of adjacent time points of the missing items based on the abnormal records in the monitoring information archive, compares the field names and makes supplementary records, obtains three consecutive records of numerical abnormal items and determines the trend direction, corrects the deviated items with the mean of both ends, adds processing status and classifies and organizes them, and generates an abnormal information record set; The weight extraction module extracts the safety index weights of oxygen, gas, and temperature based on the centralized records of the abnormal information, determines whether they are higher than the screening criteria, and places the records that meet the conditions into the priority pool and assigns a duration and number. The remaining records are placed into the regular pool to generate a monitoring information classification pool; The information merging module extracts the number of accesses and the number of days between accesses based on the regular pool data in the monitoring information classification pool and determines whether they are below the frequency screening criteria, groups the records that meet the criteria by monitoring point and type, merges the records with small numerical differences and replaces the original entries, updates the index path and access identifier, and generates a compressed set of regular information; The field rearrangement module compresses and concentrates the fields according to the general information, calls the standard field order to determine whether there are missing items, inserts the missing fields and rearranges the original field order, assigns classification identifiers and updates the field structure, and generates a safe production information management plan.

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