An intelligent recording platform for the operation and maintenance work log of distribution automation based on the CS architecture

Abstract

The present invention relates to the technical field of information management, and includes an intelligent recording platform for the operation and maintenance work log of distribution automation based on the CS architecture. The platform includes a log field parsing module, a task chain construction module, a task logic verification module, and a log storage and traceability module. In the present invention, by carefully parsing and comparing the uploaded log records, missing and abnormal fields are effectively marked and corrected, improving the accuracy and integrity of the data. In the sorting and grouping of task sequences, through the association of device numbers and task types and the adjustment of execution times, task scheduling is optimized, the operation and maintenance management efficiency is improved, the task chain is reorganized to resolve conflicts and adjust priorities, ensuring the smooth execution of operation and maintenance tasks, reducing delays, and the logical verification enhances task reliability through rule verification and error marking, infers and analyzes and corrects unassociated tasks, enhancing the ability to handle exceptions, improving data traceability and storage efficiency, and enhancing the traceability ability of data management.

Description

Technical Field

[0001] The present invention relates to the technical field of information management, and in particular to a distribution automation operation and maintenance work log intelligent recording platform based on a CS architecture. Background Art

[0002] The field of information management technology includes methods and systems related to the acquisition, processing, storage, retrieval and management of information. The core content of this technical field is to structure or unstructure information in different fields and scenarios through digital means to meet specific application requirements. Information management technology is widely used in enterprise resource management, archive management, data sharing and analysis and other fields. Its overall technical system covers the design of information collection interfaces, the formulation of data processing rules, the construction of storage architecture, the optimization of retrieval models and the development of multi-user collaborative management mechanisms.

[0003] Among them, the distribution automation operation and maintenance work log intelligent recording platform based on CS architecture refers to a technical platform developed based on client-server architecture, which is used to record distribution automation operation and maintenance work logs. The platform mainly focuses on the operation record management involved in distribution automation operation and maintenance, collects operation and maintenance content through human-computer interaction, and uses information processing modules for classification and storage. A shared management mechanism for logs is built based on the database. Specific technical means include standardized template design for distribution equipment operation and maintenance records, structured storage of log data, development of client recording interfaces, and implementation of data query and authority allocation functions on the server side.

[0004] Existing technologies are widely used in data processing and storage. However, when processing complex operation and maintenance log data, the lack of efficient error identification and correction mechanisms leads to inaccurate or incomplete data, inflexible scheduling of task execution, and a lack of effective conflict resolution and priority adjustment mechanisms. This leads to inefficient task execution and increased operation and maintenance costs. In terms of logic verification, existing technologies rarely perform in-depth verification of the execution logic of operation and maintenance tasks, and are prone to ignoring potential logical errors, affecting the reliability of tasks. In terms of data tracing and storage, traditional technologies fail to achieve efficient historical data comparison and dynamic updating, which limits the flexibility and application scope of data management, making operation and maintenance management seem powerless in the face of complex and changing environments. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a distribution automation operation and maintenance work log intelligent recording platform based on CS architecture.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: A distribution automation operation and maintenance work log intelligent recording platform based on CS architecture includes:

[0007] The log field parsing module parses the task type, equipment number, and timestamp field contents in the uploaded log records, compares them with the distribution operation and maintenance work log data, generates field comparison results, marks missing fields and abnormal fields and corrects them, renumbers them according to task type and time sequence, and generates a parsed field set;

[0008] The task chain construction module sorts the task sequence in the log by device number, groups the task types and adjusts the execution time based on the parsed field set, performs logic verification of the time sequence and device association, obtains task sequence association data, performs chain reconstruction and priority adjustment processing on conflicting tasks, and obtains reorganized task chain data;

[0009] The task logic verification module performs rule verification on the context association of the field content in the task chain based on the reorganized task chain data, marks and records task conflicts and context logic errors, generates logic verification results, performs inference analysis on unassociated tasks and performs context logic correction, and generates a logic correction task set;

[0010] The log storage tracing module corrects the task set based on the logic, groups and stores the unmatched task chains according to the task type and time series, marks the dynamic status of the abnormal task chains, updates and hierarchically manages them according to the partition tracing mechanism, and generates a tracing distribution operation and maintenance log storage data table.

[0011] As a further solution of the present invention, the step of obtaining the field comparison result includes:

[0012] Based on the uploaded log records, parse the task type, device number and timestamp fields, extract the field content of each record in the log, verify and check the task type, device number and timestamp in the extracted field set respectively, filter the log records with complete field content and no duplication, and generate the parsed log record set;

[0013] Perform a field comparison between the parsed log record set and the task type field in the power distribution operation and maintenance work log, and according to the field matching rule, record the entries with successful field matching and the corresponding equipment number and timestamp to obtain task type comparison summary data;

[0014] The task type is used to compare the device number field and the timestamp field in the summary data, and a multi-field joint match is performed using the formula:

[0015] ;

[0016] Calculate the difference between fields and generate field comparison results;

[0017] in, Represents the difference between fields. Represents the device number or timestamp field value in the log record field collection. Represents the corresponding field value of the power distribution operation and maintenance work log. is the weight parameter of the field matching rule. is the total number of matching fields.

[0018] As a further solution of the present invention, the step of obtaining the parsed field set includes:

[0019] Based on the field comparison results, check the integrity of the field content, mark the field position for missing fields and add placeholder values, extract abnormal data in the field range, identify the deviation items of the abnormal fields and adjust them to alternative values ​​that meet the range, and generate a marked and corrected field set;

[0020] Based on the marked and corrected field set, the classification attribute is read according to the task type associated with the field, the field data is grouped and processed, the field grouping results are arranged with reference to the task type priority parameter, and a classified and sorted field set is generated;

[0021] Based on the classified and sorted field set, the parsed field contents in the group are extracted item by item, the parsed fields are combined according to the numbering sequence, the field data is formatted into a standard data form, the field type data consistency is verified and the fields are integrated to generate a parsed field set.

[0022] As a further solution of the present invention, the step of acquiring task sequence associated data includes:

[0023] Based on the parsing field set, the task sequence in the log is parsed, the device identification of each task is extracted, logical judgment is performed on the device identification field, and the numbering is sorted to generate the task sequence device number association result;

[0024] According to the task sequence equipment number association result, the task type field is extracted, the task type and equipment number are grouped, and the grouping logic is optimized according to the weight parameter, using the formula:

[0025] ;

[0026] Generate task sequence type association grouping results;

[0027] in, The associated value representing the associated grouping of task types, Represents a set of task types. Represents the device number weight, represents the task execution time weight, Represents the sum of the absolute values ​​of the corresponding sets;

[0028] Based on the task sequence type association grouping result, extract the group execution time field, determine whether the group time parameter conforms to the time sequence rule, verify the logical association between the device numbers in the group, optimize the group time field parameters according to the verification result, and obtain the task sequence association data.

[0029] As a further solution of the present invention, the step of acquiring the reorganized task chain data includes:

[0030] Based on the task sequence association data, extract task time parameters and association identifiers, compare task time overlap ranges, screen time conflicting tasks and analyze associations to generate a conflicting task set;

[0031] Based on the conflicting task set, read the task dependency, adjust the task priority order, update the time parameters and verify the dependency continuity and conflicting remaining tasks, and generate a reorganized task sequence table;

[0032] Based on the reorganized task sequence table, the task time periods and associated information are extracted, the tasks are linked to form a chain structure, the continuity of the time periods and the consistency of the rules are verified, and the reorganized task chain data is obtained.

[0033] As a further solution of the present invention, the step of obtaining the logic check result includes:

[0034] Perform field parsing on the reorganized task chain data, extract the context field content in the task chain item by item, analyze the context association weight of each field, analyze the mutual relationship and action path between the fields through field characteristics, filter out the fields that do not meet the requirements through weight verification and logical analysis, and generate a context field weight set;

[0035] According to the context field weight set, the logical consistency of the field content and the associated context is verified through the corresponding position, numerical value and content difference of the field value, using the formula:

[0036] ;

[0037] Calculate the logic verification score, filter the fields with scores below the threshold, mark the conflicting fields, and generate the contextual logic consistency score;

[0038] in, Represents the logic check score, Represents the contextual position value of the field value in the associated logic. represents the associated positional parameter value of the field, Represents the standard deviation of the field content. Represents the logical weight value of the field content. Dynamic weight coefficient representing the contextual logic of field content;

[0039] According to the contextual logic consistency score, the task chain fields in the task chain with logic scores lower than a threshold are marked as logic conflict fields, and the logic verification results are generated by comparing the contextual rules of the conflicting fields.

[0040] As a further solution of the present invention, the step of acquiring the logic correction task set includes:

[0041] Based on the logic verification result, extract the logic identification and time parameters of the unrelated tasks, compare the task logic structure item by item, analyze the context logic conflict points, classify and sort the incomplete tasks and semantic features, and generate an unrelated task set;

[0042] Based on the unrelated task set, extract contextual semantic features, analyze the derivation paths of the unrelated tasks, supplement the inference relationships according to logical rules, adjust the task association paths and regroup the tasks to generate a logical inference task set;

[0043] Based on the logic inference task set, the context logic supplement structure is verified, the consistency of the logic rules is checked item by item, the conflicting data not covered by the logic rules is corrected, the correction tasks are sorted and classified, and a logic correction task set is generated.

[0044] As a further solution of the present invention, the step of obtaining the data table storing the traceability distribution operation and maintenance log includes:

[0045] Based on the logic, the task set is corrected, unmatched task chains are extracted according to the task type field and the time series field, and classified, the classified task chain data is combined with the time series field value to be sorted item by item, the grouping order of the task chains is adjusted according to the time field value, and the time series grouping structure of the task chains is generated;

[0046] According to the time series grouping structure of the task chain, the task chain status field value is analyzed, the task chain start time and end time fields are disassembled, and the time interval is combined with the status deviation to adjust, using the formula:

[0047] ;

[0048] Calculate the task chain status adjustment value, mark the abnormal task chain status, and generate the abnormal task chain status marking result;

[0049] in, Indicates the task chain status adjustment value. Indicates the start time field value of the task chain. Indicates the end time field value of the task chain. represents the deviation weight coefficient of the task chain, Indicates the deviation value of the task chain status field;

[0050] Based on the abnormal task chain status marking result, the task chain records of each partition are dynamically classified and managed according to the partition tracing mechanism, the task chain status information in the partition marking is extracted and prioritized, and the task chain tracing data is integrated through the call of dynamic partition records to establish a tracing distribution operation and maintenance log storage data table.

[0051] Compared with the prior art, the advantages and positive effects of the present invention are:

[0052] In the present invention, by carefully parsing and comparing the uploaded log records, missing and abnormal fields are effectively marked and corrected, and the accuracy and completeness of the data are enhanced. In the process of sorting and grouping the task sequence, more accurate task scheduling is achieved through the association of equipment numbers and task types, and the adjustment of execution time, and the efficiency of operation and maintenance management is improved. The task chain is reorganized through conflict resolution and priority adjustment to ensure the smooth execution of operation and maintenance tasks and reduce delays and errors caused by task conflicts. In the logic verification stage, the reliability of task execution is further improved through rule verification of the task chain and marking of logical errors. The inference analysis and logic correction of unrelated tasks enhance the ability to handle abnormal situations and ensure the continuity of operation and maintenance work. By comparing with the historical database and marking the dynamic status of abnormal task chains, more efficient data tracing and storage are achieved, and the traceability capability of data management is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the platform of the present invention;

[0054] Figure 2 It is a flow chart of the field comparison results in the present invention;

[0055] Figure 3 This is a flowchart of parsing a field set in the present invention;

[0056] Figure 4 It is a flowchart of the task sequence association data in the present invention;

[0057] Figure 5 A flowchart of reorganizing task chain data in the present invention;

[0058] Figure 6 It is a flow chart of the logic verification result in the present invention;

[0059] Figure 7 A flowchart of a set of logic correction tasks in the present invention;

[0060] Figure 8 This is a flow chart of tracing the distribution operation and maintenance log storage data table in the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0063] See also Figure 1 , a distribution automation operation and maintenance work log intelligent recording platform based on CS architecture includes:

[0064] The log field parsing module parses the task type, equipment number, and timestamp field contents in the uploaded log records, compares them with the distribution operation and maintenance work log data, generates field comparison results, marks missing fields and abnormal fields and corrects them, renumbers them according to task type and time sequence, and generates a parsed field set;

[0065] The task chain construction module sorts the task sequence in the log by device number, groups the task type associations, and adjusts the execution time based on the parsed field set, and performs logical verification of the time sequence and device association, obtains the task sequence association data, performs chain reconstruction and priority adjustment processing on conflicting tasks, and obtains the reorganized task chain data;

[0066] The task logic verification module performs rule verification on the context association of the field content in the task chain based on the reorganized task chain data, marks and records task conflicts and context logic errors, generates logic verification results, performs inference analysis on unrelated tasks and performs context logic correction, and generates a logic correction task set;

[0067] The log storage and tracing module corrects the task set based on logic, groups and stores the unmatched task chains according to the task type and time series, marks the dynamic status of the abnormal task chains, updates and hierarchically manages them according to the partition tracing mechanism, and generates a tracing distribution operation and maintenance log storage data table.

[0068] The field comparison results include missing fields, abnormal fields, and correction details. The parsed field set includes task type and time series. The task sequence related data includes equipment number, task type, and execution time. The reorganized task chain data includes chain reconstruction and priority adjustment. The logic verification results include conflict markers and error records. The logic correction task set includes task inference and logic correction. The traceability distribution operation and maintenance log storage data table includes task chain grouping, abnormal markers, and traceability updates.

[0069] See also Figure 2 , the steps for obtaining the field comparison results include:

[0070] Based on the uploaded log records, parse the task type, device number and timestamp fields, extract the field content of each record in the log, verify and check the task type, device number and timestamp in the extracted field set respectively, filter the log records with complete field content and no duplication, and generate the parsed log record set;

[0071] Each log record is parsed in turn. The task type field is classified by reading the predefined task description field in the log and classifying it by category label. The classification rules for each task type are based on the standards specified in the operation manual. For example, if the task description field contains keywords such as "inspection" or "maintenance", it is classified as the corresponding task type. The equipment number field is parsed by parsing the equipment identification field in the log file and verifying its uniqueness and format integrity according to the numbering rules in the equipment database. The verification rules include whether the character length and character combination of the equipment number meet the predefined standards. The timestamp field extracts the field recording the time in the log record and converts it to the ISO8601 standard time format. At the same time, for all extracted records, the integrity of the fields is checked one by one by calling predefined functions, including whether the field value is empty and whether the field format is correct. The records that do not meet the standards are further excluded. Then, the retained records are deduplicated according to the combined value of the task type, equipment number and timestamp. The hash table is used to quickly identify and remove duplicate records, and finally a parsed log record set is generated.

[0072] Compare the parsed log record set with the task type field in the power distribution operation and maintenance work log. According to the field matching rules, record the entries with successful field matching and the corresponding equipment number and timestamp to obtain the task type comparison summary data.

[0073] The task type field value of each record is extracted from the collection, and a string match is performed item by item with the known task type field values ​​in the distribution operation and maintenance work log. The matching rule is judged based on the complete consistency of the field value. For example, the task type field value "inspection" is extracted and compared with the task type list in the distribution operation and maintenance work log one by one. If there is a complete match, the successfully matched entry and its corresponding equipment number and timestamp are recorded. At the same time, invalid matching fields are eliminated during the matching process. For example, the task type field value is empty or the log record field value is not in the task type list of the distribution operation and maintenance work log. After the comparison is completed using this rule, all successfully matched records are retained and organized as task type comparison summary data. During the operation, the matching frequency of the task type is also counted for the successfully matched entries. The frequency statistics are implemented through a counter to verify the integrity of the matching results of each task type.

[0074] Use the task type to compare the device number field and the timestamp field in the summary data, and perform multi-field joint matching using the formula:

[0075] ;

[0076] Calculate the difference between fields and generate field comparison results;

[0077] in, Represents the difference between fields. Represents the device number or timestamp field value in the log record field collection. Represents the corresponding field value of the power distribution operation and maintenance work log. is the weight parameter of the field matching rule. is the total number of matching fields;

[0078] The benefit of the formula is that, by weighted calculation of the difference values ​​between fields and combining the weight parameters of multi-field matching, the overall difference of multi-field matching can be accurately measured, thus optimizing the matching accuracy;

[0079] Summation symbol Indicates that weighted calculation is performed on all matching fields. The denominator is the normalized weight parameter. , , , , , , , , , ;

[0080] The values ​​are obtained by: and The device number and timestamp value obtained by field parsing are directly collected and obtained. The setting is based on the importance of the field and the historical data of the field matching. The weight value is dynamically adjusted by analyzing the influence of the field in multiple matching results, and then substituted into the formula for calculation:

[0081] ;

[0082] The result shows that the difference between fields is 1.9. According to the actual difference threshold, we can further judge whether the field meets the matching conditions. The field matching result directly reflects the difference degree of multi-field matching.

[0083] See also Figure 3 , the steps to obtain the parsed field collection include:

[0084] Based on the field comparison results, check the integrity of the field content, mark the field position for missing fields and add placeholder values, extract abnormal data in the field range, identify the deviation items of the abnormal fields and adjust them to alternative values ​​that meet the range, and generate a set of marked and corrected fields;

[0085] Compare each field item by item and set integrity standards. Scan each field to check whether it contains missing values. Mark the specific location of the missing field in the mapping index table. The field number of the index table corresponds to its location information in the task record log. For the missing field content, a logical fill-in placeholder value is generated through the preset default value. The logic is based on the priority rule of the task type to which the field belongs, and calls the default parameter set related to the task attribute to complete the filling of each missing field. For example, when the missing field recorded in the inspection log involves the voltage data range, according to the upper and lower limit rules of distribution automation, the missing field is replaced by the average value parameter to occupy the place and mark it as corrected. The abnormal data of the field range is extracted, and each entry of the field set is scanned item by item. The maximum difference deviation method is used to calculate the deviation between the field value and the standard value. The data beyond the specified range is defined as abnormal data, and reassigned to a substitute value that meets the range according to the deviation item. The setting rule of the substitute value is calculated according to the median value between the upper and lower limits of the field to ensure that the continuity of the data is not destroyed. Generate a marked and corrected field set for further data processing and optimization.

[0086] Based on the marked and corrected field set, the classification attribute is read according to the task type associated with the field, the field data is grouped and processed, and the field grouping results are arranged according to the task type priority parameter to generate a classified and sorted field set;

[0087] Each field in the field set is compared one-to-one with the task type mapping table, and the grouping information of the field is extracted according to the priority rule of the task type. The correspondence between the field and the corresponding task type is dynamically recorded through the group record index table. When the field data is grouped, the weight value of the field is calculated for each group, and the grouping priority of the field is arranged according to the task type parameter. For example, in the distribution automation operation and maintenance work log, the task type such as "fault recovery" has a higher priority than "inspection record". The field grouping arrangement arranges the fields of the fault recovery task in the priority processing range. The calculation of the task type priority refers to the product value of the task historical operation frequency and the importance parameter, and the field priority is arranged from high to low. The classified and sorted field set is generated according to the field classification priority rule for subsequent parsing and combination steps.

[0088] Based on the classified and sorted field set, extract the parsed field content in the group one by one, combine the parsed fields according to the numbering sequence, format the field data into a standard data form, verify the consistency of the field type data and perform field integration to generate a parsed field set;

[0089] Read each entry in the field grouping, group the grouped fields one by one into a standardized data set according to the order of the field index number. In the field parsing content, parse the number and data type of each field one by one to ensure the legitimacy of each field content. Connect the field parsing content with the standard field format through the logical mapping table. Standardize the fields into a unified format according to the recording requirements of the task log. For example, for the distribution fault task, parse the fault time field into the 24-hour standard format, and convert the fault type field into a unique identification code. After formatting the field data, verify the consistency of the field type data item by item. During the verification, calculate the integrity and consistency of the numerical field and the text field respectively. The final optimization of the integrated field set is achieved by adjusting the abnormal field content, and the parsed field set is generated to meet the intelligent and standardized requirements of the distribution automation operation and maintenance work log recording.

[0090] See also Figure 4 ,The steps for obtaining task sequence associated data include:

[0091] Based on the parsing field set, the task sequence in the log is parsed, the device ID of each task is extracted, logical judgment is performed on the device ID field, and the numbering is sorted to generate the task sequence device number association result;

[0092] The equipment identification field in the task is broken down into two parts: equipment number and equipment type. Logical judgment is performed on the equipment number based on its uniqueness. First, a uniqueness check table is built for all equipment number fields in the log. The uniqueness of the number is verified item by item through the check table, and equipment records with duplicate numbers are screened out. At the same time, the equipment type field is used to determine whether it meets the requirements of the target equipment type set. Records of non-target equipment types are screened out, and the set of equipment records that meet the requirements is retained. The retained records are sorted according to the equipment number, and the sorting results are used to generate the task sequence equipment number association results.

[0093] According to the result of the task sequence equipment number association, extract the task type field, group the task type and equipment number, optimize the grouping logic according to the weight parameter, and use the formula:

[0094] ;

[0095] Generate task sequence type association grouping results;

[0096] in, The associated value representing the associated grouping of task types, Represents a set of task types. Represents the device number weight, represents the task execution time weight, Represents the sum of the absolute values ​​of the corresponding sets;

[0097] The formula is beneficial in that it optimizes the weight and improves the relevance of task grouping logic through multiple comprehensive operations of equipment number weight, task type set and task execution time weight;

[0098] Build a collection by extracting the task type field from the task sequence device number association result. Specifically, extract the field of each task type into an independent array. The length of the array is the number of elements in the collection.

[0099] The weight value is generated by assigning each equipment number to the number of task records associated with it. The more equipment number records, the higher the weight;

[0100] By extracting the execution time field of each task, the absolute value of the task execution time difference is calculated as the time weight;

[0101] Assumptions , then we calculate:

[0102] ;

[0103] The result shows that the correlation value of task type association grouping is 4.36, which can be used as a measure of task grouping logic.

[0104] Based on the task sequence type association grouping results, extract the group execution time field, determine whether the group time parameters comply with the time sequence rules, verify the logical association between the device numbers in the group, optimize the group time field parameters based on the verification results, and obtain the task sequence association data;

[0105] The execution time field of each group is decomposed into two parameters: start time and end time. A time series integrity check table is constructed according to the time sequence rules. The check table is used to verify whether the tasks in the group meet the time sequence requirements. If there is an error in the task time sequence, the exception is marked and the task record is removed. At the same time, the continuity of the device logic is verified through the device number association field in the group. Groups with discontinuous device numbers are marked as abnormal groups and removed. The group records retained by the above operations constitute task group records with optimized time parameters, and finally the task sequence association data is obtained.

[0106] See also Figure 5 ,The steps for obtaining the reorganized task chain data include:

[0107] Based on the task sequence association data, extract the task time parameters and association identifiers, compare the task time overlap range, filter the time conflicting tasks and analyze the associations to generate a conflicting task set;

[0108] By scanning the task log data records, the time information and corresponding association identifiers of each task in the task sequence are read one by one. When extracting time parameters, a segmented interval-by-interval extraction method is adopted to ensure that the time start and end points of each task are mapped one-to-one with the association identifier, and recorded in the time parameter index table. When comparing the task time overlapping range, the time intervals of each task are compared pairwise, and the range value of the interval intersection is calculated. Task pairs with an intersection greater than zero are recorded as time overlapping tasks. The association relationship between tasks is matched through the association identifier table. When filtering time conflicting tasks, all overlapping tasks are sorted according to the time overlap ratio, and task pairs with larger conflict ratios are filtered out first. The task identifier, overlapping range, and association identifier are recorded in the conflicting task set for further processing and analysis.

[0109] Based on the conflicting task set, read the task dependencies, adjust the task priority order, update the time parameters and verify the dependency continuity and conflicting remaining tasks, and generate a reorganized task sequence table;

[0110] Match the dependency identifiers of tasks one by one from the task log records, and establish an association matrix for the dependency information of each conflicting task. The rows and columns of the matrix represent the task identifiers, and the matrix elements represent the strength of the dependency relationship. When adjusting the task priority order, the dependency depth of the task is recursively calculated based on the dependency matrix of the conflicting tasks. The priority of the task with a larger dependency depth is adjusted to a higher position and recorded in the priority adjustment index table. When updating the time parameters, the start and end time intervals of the tasks are rearranged according to the priority, and the high-priority tasks are arranged in the early time period. The start and end times are adjusted continuously according to the dependency relationship between the tasks. For example, if task C depends on the completion of task A, the end time of task A must be less than the start time of task C. When verifying the dependency continuity, by traversing the adjusted task schedule, calculate each pair of dependent tasks One by one to see if the time interval meets the time continuity requirements, and record the remaining unresolved conflicting tasks to generate an optimized reorganized task sequence table for further task chain construction.

[0111] Based on the reorganized task sequence table, extract the task time period and related information, link the tasks to form a chain structure, verify the continuity of the time period and the consistency of the rules, and obtain the reorganized task chain data;

[0112] The start and end time and associated information of each task are read from the task sequence table one by one, and the time periods and associated information are mapped to the nodes and link relationships of the task chain. When linking tasks to form a chain structure, the priority in the task sequence table is used as the link direction basis, and the tasks with adjacent start and end time periods are linked into continuous chain nodes. The starting and end points of the nodes are the start and end times of the tasks, respectively. When verifying the continuity of the time period and the consistency of the rules, the time period gaps and rule parameters in the chain are checked one by one. For example, the gaps must not be greater than the preset time threshold. At the same time, the rule attributes in the chain are compared to see if they meet the logical order of the tasks. The verified reorganized task chain data is obtained for intelligent recording of distribution automation operation and maintenance work logs and task flow optimization.

[0113] See also Figure 6 , the steps for obtaining the logic verification results include:

[0114] Perform field analysis on the reorganized task chain data, extract the context field content in the task chain item by item, analyze the context association weight of each field, analyze the relationship and action path between fields through field characteristics, filter out fields that do not meet the requirements through weight verification and logical analysis, and generate a context field weight set;

[0115] First, extract the fields involved in contextual logic from the task chain data one by one, use data parsing tools to decode the content of the field value and mark the starting and ending positions of each field, identify the classification information of the field based on the content characteristics of the field, such as the numeric type, text type or Boolean type of the field, and calculate the basic statistics of the field by analyzing its numeric range and distribution, including the minimum value, maximum value, mean and standard deviation of the field. For text type fields, establish a statistical table containing word frequency and keyword frequency to quantify the field content characteristics. For Boolean type fields, calculate the ratio of "true" values ​​to "false" values ​​in the field. By analyzing the type of field content and extracting features, calculate the logical importance weight of the field in the task chain. The weight value is determined comprehensively by the frequency of occurrence of the field, the field dependency in the logic chain and the degree of contextual logic association of the field. Sort all fields and their corresponding logical weight values, and eliminate fields with lower weight values. Keep fields with higher weight values ​​as important fields for subsequent logic verification to generate a context field weight set.

[0116] According to the context field weight set, the logical consistency of the field content and the associated context is verified through the corresponding position, numerical value and content difference of the field value, using the formula:

[0117] ;

[0118] Calculate the logic verification score, filter the fields with scores below the threshold, mark the conflicting fields, and generate the contextual logic consistency score;

[0119] in, Represents the logic check score, Represents the contextual position value of the field value in the associated logic. represents the associated positional parameter value of the field, Represents the standard deviation of the field content. Represents the logical weight value of the field content. Dynamic weight coefficient representing the contextual logic of field content;

[0120] The formula is useful in that it provides a quantitative standard for multi-dimensional logic verification through the comprehensive calculation of field value differences, content deviation values, and logic weights;

[0121] By parsing the contextual content extracted from the task chain field, the field value and associated positional parameters It is obtained by calculating the relative position of the associated fields in the task chain and the corresponding content range of the field value;

[0122] For example, , ,The data is extracted by analyzing the contextual association logic of log files;

[0123] The standard deviation value of the field content is the deviation between the field content and its contextual logical expected value, which is obtained by subtracting the actual value from the reference value and taking the absolute value. For example ;

[0124] Indicates the logical weight value of the field content, which is determined by the importance of the field in the logic chain, for example ;

[0125] The dynamic weight coefficient representing the context logic is obtained by dynamically adjusting the association of field logic, for example ;

[0126] Substitute into the formula to calculate:

[0127] ;

[0128] The result shows that the context logic check score of the field is 0.427. After comparison with the preset threshold, the field is marked as a logical conflict field.

[0129] According to the contextual logic consistency score, the task chain fields with logic scores lower than the threshold in the task chain are marked as logic conflict fields, and the logic verification results are generated by comparing the context rules of the conflicting fields;

[0130] Mark the fields with scores lower than the set threshold, filter out all conflicting fields, and verify whether there is a conflict between the conflicting fields and the contextual relationship of the fields through the association analysis of logical rules. The analysis of conflicting relationships establishes a field dependency tree through the logical paths between fields. The conflicting fields and the fields in their associated paths are marked in the field dependency tree. The fields in the path are sorted according to the scoring priority, and the secondary conflicting fields in the dependency path are eliminated. Finally, the field with the highest conflict score in the dependency path is retained as the primary conflicting field. A conflict record table is generated for all conflicting fields, recording the logical verification score of the field, the contextual logical rules of the field, and the dependency path information of the field conflict. All conflict records are classified and stored in the conflict record table. On the basis of the conflict record table, by comparing the verification scores of the fields in the task chain, the scores of the conflicting fields are further corrected and the contextual logical rules of the fields are updated. Finally, the logical verification results are generated in combination with the logical scores of all fields.

[0131] See also Figure 7 ,The steps for obtaining the logic correction task set include:

[0132] Based on the logic verification results, the logic identifiers and time parameters of the unrelated tasks are extracted, the logical structures of the tasks are compared item by item, the contextual logic conflicts are analyzed, the incomplete tasks and semantic features are classified and sorted, and a set of unrelated tasks is generated;

[0133] By scanning the distribution automation operation and maintenance work log records, the relevance of the logical identifiers of all tasks is verified item by item. For tasks that fail to establish logical associations, the logical identifiers and time parameters of the tasks are recorded through the index table, and the logical structures of the tasks are compared item by item. By analyzing the logical conditions in the context of each task, the missing contents of the preceding logic and subsequent logic of the unassociated tasks are extracted, and the associated task structures in the log are compared to identify the interrupted logical paths of the unassociated tasks. When analyzing the contextual logic conflict points, the task logical identifiers and contextual relationships are compared item by item, and the logical starting or ending points of the unassociated tasks are overlapped and compared with the adjacent tasks on the timeline. When classifying and sorting out incomplete tasks and semantic features, the unassociated tasks are divided into the preceding logic missing class and the subsequent logic missing class according to the location of the logical path interruption and the semantic missing content, and their time parameters and logical identifiers are recorded to generate an unassociated task set to provide data support for subsequent inference and correction tasks.

[0134] Based on the unrelated task set, contextual semantic features are extracted, the derivation paths of unrelated tasks are analyzed, the inference relationships are supplemented according to logical rules, the task association paths are adjusted and the tasks are recombined to generate a logical inference task set;

[0135] By parsing the logical identifier and time parameters of each unrelated task item by item, extracting the relevant semantic fragments in the task context, and recording the semantic features as the logical attributes of the task, when analyzing the derivation path of the unrelated tasks, the interruption positions of the preceding and subsequent logics are read task by task, the matching degree between the preceding and following logical conditions is calculated, and the logical interruption points are supplemented and inferred with reference to the context rules. When supplementing the inference relationship according to the logical rules, an inference rule is set for the logical interruption point of each unrelated task. The rule supplements the inference path of the tasks whose preceding logic is not closed in chronological order according to the logical attributes extracted from the context semantic features, and ensures that the supplemented logical path meets the restrictions of the context time parameters and logical conditions. When adjusting the task association path and recombining the tasks, the logical identifier of the inference path is recorded, and the tasks after the supplementary inference are recombined into a new logical task set to generate a logical inference task set for further logic verification and correction.

[0136] Based on the logical inference task set, verify the contextual logic supplement structure, check the consistency of logical rules item by item, correct the conflicting data not covered by the logical rules, organize and classify the correction tasks, and generate a logical correction task set;

[0137] By checking the logical paths of the inferred tasks one by one, ensure that the logical identifiers and time parameters of the supplementary paths meet the consistency constraints. When checking the consistency of the logical rules one by one, perform logical condition matching on the starting and ending points of each logical identifier supplementary path, and mark the paths that do not meet the logical rule consistency as conflicting paths. When correcting the conflicting data not covered by the logical rules, correct the marked conflicting paths one by one, and adjust the path logical conditions to a state consistent with the context rules. For example, adjust the starting time of the logical identifier of task H to the minimum time value of the path continuity. When sorting and classifying the corrected tasks, reclassify the corrected tasks according to the logical identifiers and time parameters to generate a set of logical correction tasks for the final task logic optimization and intelligent logging.

[0138] See also Figure 8 ,The steps for obtaining the data table for tracing the distribution operation and maintenance log storage include:

[0139] Based on the logic correction task set, the unmatched task chains are extracted according to the task type field and time series field, and classified. The classified task chain data is combined with the time series field value to sort item by item. The grouping order of the task chain is adjusted according to the time field value to generate the time series grouping structure of the task chain.

[0140] First, parse the type field of each task in the task chain data, convert the type field value into a unique type identifier, ensure that the task chain can be clearly classified according to the type, sort the data of each type group according to the time series field value, and compare the numerical value of the time field one by one to ensure that the task chains of the same type can be arranged in chronological order. During the sorting process, for the time field of each task chain, calculate the time interval between adjacent task chains, and compare the time interval value with the preset time span range. If the time interval exceeds the preset normal range, mark the current task chain as a time-abnormal task chain. By analyzing the continuity of the task chain time field, determine whether there is an interrupted time period. For discontinuous time periods, extract the relevant task chains and store them as separate abnormal groups. Save the normal task chain groups and abnormal task chain groups that have been screened and sorted separately to generate a time series grouping structure for the task chain.

[0141] According to the time series grouping structure of the task chain, analyze the task chain status field value, disassemble the task chain start time and end time fields, and adjust them by combining the time interval with the status deviation. The formula is:

[0142] ;

[0143] Calculate the task chain status adjustment value, mark the abnormal task chain status, and generate the abnormal task chain status marking result;

[0144] in, Indicates the task chain status adjustment value. Indicates the start time field value of the task chain. Indicates the end time field value of the task chain. represents the deviation weight coefficient of the task chain, Indicates the deviation value of the task chain status field;

[0145] The benefit of the formula is that by calculating the time field difference and combining the dynamic weight and the adjustment of the state deviation value, the task chain state deviation can be evaluated more accurately, providing a multi-dimensional basis for the determination of abnormal states, making the determination of abnormal states more flexible and accurate;

[0146] The start time field value of the task chain and the end time field value They are obtained directly by parsing the time fields of the task chain. For example, the start time field value extracted from the task chain is 3.2, and the end time field value is 6.8;

[0147] Calculate the dynamic weight coefficient by analyzing the status field value of the task chain ,This coefficient is determined by the comprehensive evaluation of the fluctuation range of the task chain status value and the logical priority. For example, the dynamic weight coefficient value is 1.2;

[0148] Compare the actual value of the task chain status value with the reference value and calculate the status deviation value , such as the deviation value is 2.5;

[0149] Substitute the above parameters into the formula for calculation:

[0150] ;

[0151] Calculation results It indicates that the state adjustment value of the task chain is 6.6. According to the comparison of the threshold range of the dynamic state, the task chain is determined to be in an abnormal state and marked, and finally an abnormal task chain state marking result is generated.

[0152] Based on the abnormal task chain status marking results, the task chain records of each partition are dynamically classified and managed according to the partition traceability mechanism, the task chain status information in the partition mark is extracted and prioritized, and the task chain traceability data is integrated through the call of dynamic partition records to establish a traceability distribution operation and maintenance log storage data table;

[0153] The task chains are classified according to the partition numbers. During the classification, the task chain records are extracted according to the dynamic status mark value of each partition and the preliminary grouping of the partition task chains is generated. On the basis of the grouping, the time field value and the state deviation value of the task chains in the partition are secondary classified, and the task chains are prioritized using the partition dynamic state deviation range. For the task chains in each partition, a partition traceability structure is constructed. The traceability structure contains information such as the priority, state deviation, and time field range of the task chain. When integrating the partition traceability structure, the task chain records of different partitions are compared one by one, and the classification information is dynamically adjusted according to the partition priority and the task chain status mark value. The traceability data of all partitions are integrated into a complete partition traceability record table. The final record integration of the partition task chain is completed in combination with the hierarchical classification management method. After updating the traceability record table, a traceability distribution operation and maintenance log storage data table is generated.

[0154] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A distribution automation operation and maintenance work log intelligent recording platform based on CS architecture, characterized in that: The platform includes: The log field parsing module parses the task type, equipment number, and timestamp field contents in the uploaded log records, compares them with the distribution operation and maintenance work log data, generates field comparison results, marks missing fields and abnormal fields and corrects them, renumbers them according to task type and time sequence, and generates a parsed field set; The task chain construction module sorts the task sequence in the log by device number, groups the task types and adjusts the execution time based on the parsed field set, performs logic verification of the time sequence and device association, obtains task sequence association data, performs chain reconstruction and priority adjustment processing on conflicting tasks, and obtains reorganized task chain data; The step of acquiring the task sequence associated data comprises: Based on the parsing field set, the task sequence in the log is parsed, the device identification of each task is extracted, logical judgment is performed on the device identification field, and the numbering is sorted to generate the task sequence device number association result; According to the task sequence equipment number association result, the task type field is extracted, the task type and equipment number are grouped, and the grouping logic is optimized according to the weight parameter, using the formula: ； Generate task sequence type association grouping results; in, The associated value representing the associated grouping of task types, Represents a set of task types. Represents the device number weight, represents the task execution time weight, Represents the sum of the absolute values ​​of the corresponding sets; Based on the task sequence type association grouping result, extract the group execution time field, determine whether the group time parameter conforms to the time sequence rule, verify the logical association between the device numbers in the group, optimize the group time field parameter according to the verification result, and obtain the task sequence association data; The step of acquiring the reorganization task chain data comprises: Based on the task sequence association data, extract task time parameters and association identifiers, compare task time overlap ranges, screen time conflicting tasks and analyze associations to generate a conflicting task set; Based on the conflicting task set, read the task dependency, adjust the task priority order, update the time parameters and verify the dependency continuity and conflicting remaining tasks, and generate a reorganized task sequence table; Based on the reorganized task sequence table, extract the task time period and associated information, link the tasks to form a chain structure, verify the continuity of the time period and the consistency of the rules, and obtain the reorganized task chain data; The task logic verification module performs rule verification on the context association of the field content in the task chain based on the reorganized task chain data, marks and records task conflicts and context logic errors, generates logic verification results, performs inference analysis on unassociated tasks and performs context logic correction, and generates a logic correction task set; The log storage tracing module corrects the task set based on the logic, groups and stores the unmatched task chains according to the task type and time series, marks the dynamic status of the abnormal task chains, updates and hierarchically manages them according to the partition tracing mechanism, and generates a tracing distribution operation and maintenance log storage data table.

2. According to the CS architecture-based distribution automation operation and maintenance work log intelligent recording platform of claim 1, it is characterized in that: The step of obtaining the field comparison result includes: Based on the uploaded log records, parse the task type, device number and timestamp fields, extract the field content of each record in the log, verify and check the task type, device number and timestamp in the extracted field set respectively, filter the log records with complete field content and no duplication, and generate the parsed log record set; Perform a field comparison between the parsed log record set and the task type field in the power distribution operation and maintenance work log, and according to the field matching rule, record the entries with successful field matching and the corresponding equipment number and timestamp to obtain task type comparison summary data; The task type is used to compare the device number field and the timestamp field in the summary data, and a multi-field joint match is performed using the formula: ； Calculate the difference between fields and generate field comparison results; in, Represents the difference between fields. Represents the device number or timestamp field value in the log record field collection. Represents the corresponding field value of the power distribution operation and maintenance work log. is the weight parameter of the field matching rule. is the total number of matching fields.

3. The CS architecture-based distribution automation operation and maintenance work log intelligent recording platform according to claim 2 is characterized in that: The step of obtaining the parsed field set includes: Based on the field comparison results, check the integrity of the field content, mark the field position for missing fields and add placeholder values, extract abnormal data in the field range, identify the deviation items of the abnormal fields and adjust them to alternative values ​​that meet the range, and generate a marked and corrected field set; Based on the marked and corrected field set, the classification attribute is read according to the task type associated with the field, the field data is grouped and processed, the field grouping results are arranged with reference to the task type priority parameter, and a classified and sorted field set is generated; Based on the classified and sorted field set, the parsed field contents in the group are extracted item by item, the parsed fields are combined according to the numbering sequence, the field data is formatted into a standard data form, the field type data consistency is verified and the fields are integrated to generate a parsed field set.

4. The CS architecture-based distribution automation operation and maintenance work log intelligent recording platform according to claim 1 is characterized in that: The step of obtaining the logic verification result includes: Perform field parsing on the reorganized task chain data, extract the context field content in the task chain item by item, analyze the context association weight of each field, analyze the mutual relationship and action path between the fields through field characteristics, filter out the fields that do not meet the requirements through weight verification and logical analysis, and generate a context field weight set; According to the context field weight set, the logical consistency of the field content and the associated context is verified through the corresponding position, numerical value and content difference of the field value, using the formula: ； Calculate the logic verification score, filter the fields with scores below the threshold, mark the conflicting fields, and generate the contextual logic consistency score; in, Represents the logic check score, Represents the contextual position value of the field value in the associated logic. represents the associated positional parameter value of the field, Represents the standard deviation of the field content. Represents the logical weight value of the field content. Dynamic weight coefficient representing the contextual logic of field content; According to the contextual logic consistency score, the task chain fields in the task chain with logic scores lower than a threshold are marked as logic conflict fields, and the logic verification results are generated by comparing the contextual rules of the conflicting fields.

5. The CS architecture-based distribution automation operation and maintenance work log intelligent recording platform according to claim 4 is characterized in that: The step of obtaining the logic correction task set includes: Based on the logic verification result, extract the logic identification and time parameters of the unrelated tasks, compare the task logic structure item by item, analyze the context logic conflict points, classify and sort the incomplete tasks and semantic features, and generate an unrelated task set; Based on the unrelated task set, extract contextual semantic features, analyze the derivation paths of the unrelated tasks, supplement the inference relationships according to logical rules, adjust the task association paths and regroup the tasks to generate a logical inference task set; Based on the logic inference task set, the context logic supplement structure is verified, the consistency of the logic rules is checked item by item, the conflicting data not covered by the logic rules is corrected, the correction tasks are sorted and classified, and a logic correction task set is generated.

6. The CS architecture-based distribution automation operation and maintenance work log intelligent recording platform according to claim 5 is characterized in that: The step of obtaining the data table storing the traceability distribution operation and maintenance log includes: Based on the logic, the task set is corrected, unmatched task chains are extracted according to the task type field and the time series field, and classified, the classified task chain data is combined with the time series field value to be sorted item by item, the grouping order of the task chains is adjusted according to the time field value, and the time series grouping structure of the task chains is generated; According to the time series grouping structure of the task chain, the task chain status field value is analyzed, the task chain start time and end time fields are disassembled, and the time interval is combined with the status deviation to adjust, using the formula: ； Calculate the task chain status adjustment value, mark the abnormal task chain status, and generate the abnormal task chain status marking result; in, Indicates the task chain status adjustment value. Indicates the start time field value of the task chain. Indicates the end time field value of the task chain. represents the deviation weight coefficient of the task chain, Indicates the deviation value of the task chain status field; Based on the abnormal task chain status marking result, the task chain records of each partition are dynamically classified and managed according to the partition tracing mechanism, the task chain status information in the partition marking is extracted and prioritized, and the task chain tracing data is integrated through the call of dynamic partition records to establish a tracing distribution operation and maintenance log storage data table.

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