Multi-level group ecological environment assessment subsystem and assessment method

Abstract

The invention discloses a multi-level group ecological environment assessment subsystem and assessment method, and relates to the technical field of ecological environment processing. Comprising an evidence graph construction unit which is used for establishing an organization unit table, an organization path table and an index rule table in a relational database of the same platform, and generating evidence nodes and evidence relations according to database view output so as to form an examination evidence graph; the fusion and calculation unit is used for generating a hierarchical consistent score fragment set based on the assessment evidence atlas; and the circulation unit is used for automatically generating a reference score according to the hierarchical consistent score fragment set, and circulating according to a sequence of self-scoring, secondary auditing and group auditing. According to the invention, the problems of low data processing efficiency, rigid weight configuration, difficult result tracing, insufficient system expansibility and the like in the prior art are effectively solved, and an efficient, fair and landing technical support is provided for ecological environment management assessment of a multi-level group.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological environment treatment, and in particular to a multi-level group ecological environment assessment subsystem and an assessment method. BACKGROUND

[0002] With the increasing demand for ecological environment protection, the environmental management assessment of large group enterprises has gradually evolved from single indicator evaluation to a comprehensive assessment system covering multiple dimensions and cross-levels. The common practice at present is to rely on traditional assessment information systems or electronic spreadsheet tools (such as Excel) for data collection and statistics. This kind of approach has a certain application basis in the multi-level structure of group companies, secondary enterprises and tertiary units, and can achieve basic environmental indicator aggregation and scoring. However, with the rapid growth of enterprise size and data complexity, the existing technology gradually exposes many problems.

[0003] Firstly, in terms of data acquisition and processing, the current commonly used systems mostly rely on manual input and manual integration. Each unit needs to fill in various reports within the assessment period, and the management personnel then manually aggregates these reports. Although some systems support the import of multi-source data, due to the lack of a unified organizational level tree and data standards, problems such as inconsistent unit levels, different indicator scopes, data loss or duplication often occur. This manual processing method is inefficient, has a high error rate, and is difficult to meet the real-time and accuracy requirements of the group.

[0004] Secondly, in terms of weight configuration and calculation logic, the existing technology mostly adopts fixed weight or experience weight method, i.e. the management layer determines the proportion of each indicator score in advance. This method is too rigid and cannot dynamically reflect the differences in enterprise annual focus. For example, in some years, pollutant emissions are the focus, so the weight of related indicators should be correspondingly increased, while in other years, risk event prevention and control are more important, so the assessment proportion should be adjusted. The existing technology does not provide an effective dynamic weight calculation mechanism, resulting in that the assessment results cannot truly reflect the actual level of the enterprise under different environmental backgrounds.

[0005] Thirdly, in terms of data traceability and credibility, the existing systems usually only save the final score and some original forms, but lack fine-grained evidence recording and relationship tracing mechanism. Once there is an assessment dispute, the management personnel often need to manually search and compare multiple source files, which is time-consuming and laborious, and also difficult to ensure that the data is not tampered with. Especially under the multi-level structure of the group, the responsibility division between cross-units is difficult to realize technical tracking and auditing through the existing assessment system. SUMMARY

[0006] In view of this, the present application provides a multi-level group ecological environment assessment subsystem and an assessment method. In a relational database of the same platform, relying on an organization unit table, an organization path table, an index rule table, an evidence node table and an evidence relationship table, the standardization registration of the organization level and the index rule is realized through an evidence graph construction unit, the evidence fingerprint deduplication, the window summary generation, the conflict fingerprint grouping and the shadow node completion are completed through a fusion and calculation unit, the level consistent score segment set is formed, and the closed-loop circulation of the reference score generation, the self-score, the secondary audit and the group audit is sequentially executed in a circulation unit, and finally the traceable final result is output. The technical scheme can significantly improve the automation and accuracy of the assessment data processing, guarantee the consistency and integrity of the transmission between levels, provide the traceability and tamper resistance in the whole process, and has good expansibility and portability, thereby effectively solving the problems of low data processing efficiency, rigid weight configuration, difficult result tracing and insufficient system expansibility in the prior art, and providing efficient, fair and implementable technical support for the ecological environment management assessment of multi-level groups.

[0007] The technical scheme adopted by the present application is as follows:

[0008] A multi-level group ecological environment assessment subsystem comprises:

[0009] An evidence graph construction unit is used to establish an organization unit table, an organization path table and an index rule table in a relational database of the same platform, compile the index rule into a database view, traverse the records in the organization unit table and recursively from bottom to top to the root node according to the parent code, write the ordered code from the root to the leaf into the root code field, the secondary code field, the tertiary code field and the complete path field of the organization path table, and output the evidence node and the evidence relationship according to the database view to form an assessment evidence graph;

[0010] A fusion and calculation unit is used to standardize and deduplicate the database view output according to the evidence fingerprint based on the assessment evidence graph, obtain a standardized result, compress the standardized result into a continuous segment in the unit of the organization path according to the organization path table, establish an upward transmission window and a downward review window in each segment to generate corresponding upstream and downstream database views and record the window summary, generate a conflict fingerprint and group the contradictory evidence when the same index appears contradictory evidence in the same node, then select stable evidence at one time, and record the evidence not selected in the conflict fingerprint, generate a shadow node for the continuous gap between the stable evidence, the shadow node records the gap start and end, the nearest neighbor summary and the rollback flag, and write the standardized result, the window summary, the conflict fingerprint and the shadow node back to the preset evidence node table and the preset evidence relationship table according to the original position to generate a level consistent score segment set;

[0011] A flow unit is used to automatically generate reference scores from the set of score fragments according to the level consistency, and to flow in the order of self-scoring, secondary review and group review.

[0012] Further, the organization unit table includes unit code, unit name, parent code, level identifier, enable flag, creation time and update time fields, wherein the unit code is the primary key, the parent code references the unit code of the organization unit table to form a foreign key constraint, and the level identifier is limited to group level, secondary level and tertiary level; the organization path table includes unit code, root code, secondary code, tertiary code, complete path, path version, generation time and generation note fields, wherein the unit code is the primary key; the index rule table includes rule number, rule name, associated index code, source table name, filter condition expression, time field name, attribution unit field name, aggregation method, output field name, view name, rule state and last compilation time fields, wherein the rule number is the primary key.

[0013] Further, the evidence node table includes evidence number, evidence fingerprint, source view name, view record primary key, evidence time, evidence attribution unit code, evidence type, evidence load, registration time and registration note fields, wherein the evidence number is the primary key and the evidence attribution unit code references the unit code of the organization unit table; the evidence relationship table includes relationship number, main node number, from node number, relationship type, relationship time, source view name, relationship note and registration time fields, wherein the relationship number is the primary key, and the main node number and from node number reference the evidence number of the evidence node table.

[0014] Further, the process of determining continuous segments by the fusion and calculation unit includes: reading the organization path table, parsing the root code field, secondary code field and tertiary code field for each record, and forming an ordered coding path from root to leaf in the order of ancestor first and child second; each ordered coding path is recorded as a continuous segment, and the unique identifier of the continuous segment is obtained by concatenating the root code field, secondary code field and tertiary code field.

[0015] Further, the process of establishing the upward transmission window and the downward review window in each segment by the fusion and calculation unit includes: for each continuous segment and each type of evidence, moving the window from the leaf node to the root node in the order of evidence time; only retaining the evidence record belonging to the unit code of the leaf node of the continuous segment in the standardization result corresponding to the current evidence time in the window, and binding the evidence record with the unit code of the parent node to form an upward snapshot when pushing to the parent node; for each window push, generating a database view corresponding to the upward window containing only four columns of unit code, evidence time, evidence type and evidence load in the public table expression within the session; for the same continuous segment and the same type of evidence, moving the window from the root node to the leaf node in the order of evidence time; only retaining the evidence record belonging to the unit code of the root node of the continuous segment in the standardization result corresponding to the current evidence time in the window, and binding the evidence record with the unit code of the child node to form a downward snapshot when pushing to the child node; for each window push, generating a database view corresponding to the downward window containing only four columns of unit code, evidence time, evidence type and evidence load in the public table expression within the session.

[0016] Further, the process of determining the contradictory evidence by the fusion and calculation unit includes: within the same unit code, the same evidence time and the same evidence type, if there are two or more evidence records, upward snapshots or downward snapshots, the contents of the evidence load field in the evidence node table are inconsistent, then it is determined as contradictory evidence; the process of generating conflict fingerprints and groups includes: for each group of contradictory evidence, generating a conflict fingerprint by splicing the unit code, evidence time, evidence type and source view name; grouping by conflict fingerprint as key, and the group contains all candidate record evidence fingerprints and related metadata.

[0017] Further, the process of one-time selecting stable evidence is: in each group, the unique stable evidence is selected one-time in the following order: preferentially selecting the candidate record with the smallest source view name in the dictionary order; if there are multiple candidate records with the same source view name, preferentially selecting the candidate record with the latest view record primary key in the natural order; if the candidate records are from the same source view name and the view record primary key is indistinguishable, comparing the coverage interval length of the candidate records, and the longer one is preferred; the coverage interval is determined by the evidence time granularity expressed in the database view of the candidate record, if the granularity is the same and the length is equal, the one with the smallest evidence fingerprint in the dictionary order is selected; the unselected candidate record is registered as a conflict replacement in the evidence relationship table as a relationship type, and the conflict fingerprint and the replaced reason summary are written in the relationship description; and in the evidence node table, it is marked as a conflict unselected in the registration description.

[0018] Furthermore, the process of generating shadow nodes for continuous gaps between stable evidence by the fusion and computation unit includes: within the same unit encoding and the same evidence type range, stable evidence is sorted by evidence time; if there is a preset time unit interval between the evidence times of two adjacent stable pieces of evidence, it is identified as a continuous gap; for each continuous gap, a shadow node is inserted into the evidence node table, the evidence type of the shadow node is the same as that of the adjacent stable evidence, the evidence time is recorded as a text description of the gap start and end range, the evidence load is recorded as a nearest neighbor summary, the nearest neighbor summary is the combined text of the evidence fingerprint of the stable evidence closest to the gap at both ends of the gap and the evidence load, and the rollback flag is set to yes; in the evidence relationship table, with completion generation as the relationship type, a subordinate relationship is established between the shadow node and its two nearest neighbor stable pieces of evidence, and the gap start and end range and the rollback flag are written in the relationship description.

[0019] A method for implementing a multi-level group ecological environment assessment subsystem, the method comprising:

[0020] Step 1: Create an organizational unit table, an organizational path table, and an indicator rule table in a relational database on the same platform, and compile the indicator rules into a database view; traverse the records in the organizational unit table and recursively go up to the root node according to the parent code, and write the ordered code from root to leaf into the root code field, second-level code field, third-level code field, and complete path field of the organizational path table; and generate evidence nodes and evidence relationships based on the database view output to form an assessment evidence graph;

[0021] Step 2: Based on the assessment evidence graph, standardize and deduplicate the database view output according to the evidence fingerprint to obtain the standardized result; compress the standardized result into continuous segments based on the organizational path table, and establish an upward pass-through window and a downward look-back window in each segment to generate upstream and downstream corresponding database views, and record the window summary; when contradictory evidence appears at the same node for the same indicator, generate conflict fingerprints according to the source, generation order and coverage interval, and group the contradictory evidence, then select stable evidence at once, and register the unselected evidence with conflict fingerprints; generate shadow nodes for continuous gaps between stable evidence, and record the gap start and end, nearest neighbor summary and rollback mark of the shadow nodes; write the standardized result, window summary, conflict fingerprint and shadow nodes back to the preset evidence node table and preset evidence relationship table in their original positions to generate a set of hierarchically consistent score segments;

[0022] Step 3: Automatically generate reference scores based on the set of score segments consistent with the hierarchical level, and process them in the order of self-assessment, secondary review, and group approval.

[0023] By adopting the above technical solution, the following beneficial effects are produced: by constructing the organization unit table, the organization path table, the index rule table, the evidence node table and the evidence relationship table in the relational database of the same platform, the examination evidence graph that can be completely traced is formed, and the whole process of the multi-level group ecological environment examination is realized through the evidence graph construction unit, the fusion and calculation unit and the circulation unit, which has remarkable beneficial effects. First, the present application avoids the inefficient mode of manual aggregation of external multi-source data in the traditional system, directly generates a standardized result at the database view level, and registers the evidence fingerprint for deduplication and uniqueness, thereby making the data processing link automatic and reducing the manual operation error. Secondly, the present application introduces the window summary, the conflict fingerprint grouping and the shadow node mechanism in the fusion and calculation link, which can realize the upstream and downstream transmission of evidence, the one-time selection of contradictory evidence and the technical completion of continuous gaps in the multi-level organization path, which guarantees the consistency and integrity of the examination data in the hierarchical transmission process and solves the problems of data island and traceability difficulty in the prior art. At the same time, the present application constructs the reference score generation and self-score, the sequential circulation process of two-level audit and group audit based on the hierarchical consistency score segment set in the circulation unit, which guarantees the objectivity of the result calculation and forms the closed-loop examination process that can be traced in the whole chain, thereby effectively improving the transparency and fairness of the examination. Through the hierarchical writing of the evidence node table and the evidence relationship table and the standardized storage of the traceability relationship, the present application ensures that each final result can be traced back to the original database view record, thereby significantly enhancing the data credibility and anti-tampering ability. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a system structure schematic diagram of a multi-level group ecological environment examination subsystem and an examination subsystem in the embodiment of the present application;

[0025] Figure 2 is an index rule compilation performance curve schematic diagram in the embodiment of the present application;

[0026] Figure 3 is a continuous segment compression effect schematic diagram in the embodiment of the present application. DETAILED DESCRIPTION

[0027] All features disclosed in this specification, and / or all steps of any methods or processes disclosed in this specification, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive.

[0028] Any of the features disclosed in this specification, unless explicitly stated otherwise, may be replaced by alternative features serving the same, equivalent or similar purpose.

[0029] REFERENCE Figure 1A multi-level group ecological environment assessment subsystem includes: an evidence map construction unit, a fusion and calculation unit, and a circulation unit.

[0030] In the specific implementation process, the evidence graph construction unit selects the records to be compiled according to the rule status in the indicator rule table. For each record, it verifies that the rule number, rule name, associated indicator code, source table name, filter condition expression, time field name, belonging unit field name, aggregation method, output field name, and view name are all non-empty. It also verifies that the source table name already exists in the relational database of the same platform, that the time field name and belonging unit field name are indeed existing fields of the source table name, and that the output field name does not conflict with existing fields in the source table name. The output semantics of the database view are determined based on the aggregation method field. The value of the aggregation method field indicates that one of the following operations is performed on the records in the source table name: counting, summation, minimum value, maximum value, or average value, and the records are grouped and summarized according to the belonging unit field name and time field name. If the aggregation method field indicates counting, the database view outputs the number of records for each combination of belonging unit field name and time field name; if the aggregation method field indicates summation, minimum value, maximum value, or average value, the output field name corresponds to a numeric field in the source table name, and the aggregation result is output after grouping according to the belonging unit field name and time field name.

[0031] Using the rule number as a unique key, a Structured Query Language (SMQ) view creation statement is generated for each record. This statement uses the source table name as the data source, selects the affiliated unit field name and time field name as grouping keys based on the filter condition expression, outputs the associated indicator code as a constant column, applies the aggregation method to the corresponding data columns and names the result columns as output field names, and outputs the rule number as a constant column. The database view name uses the value of the view name field, and a name duplication check is performed before creation; if a duplicate name is found, a replacement creation strategy is executed. The creation statements are executed sequentially. Upon successful creation, the last compilation time field of the corresponding record in the indicator rule table is written back to the current time, and the rule status field is updated to "compiled". If execution fails, the creation of the current view is rolled back within the same transaction, and a failure description is appended to the rule name field in the indicator rule table, while the original rule status is preserved for subsequent processing. Perform a sampling query on the newly created database view to verify that each output record contains all four types of columns: associated indicator code, unit of origin field name, time field name, and output field name. Also, ensure that the value ranges of the unit of origin field name and time field name are consistent with the corresponding fields in the source table. Only after passing the check can the view creation proceed to the next record; if the check fails, roll back the current transaction and write a failure description.

[0032] Lock the organization unit table and the organization path table, read the unit code, unit name, parent code, level identifier, enable flag, creation time and update time fields of the organization unit table, ensure that the unit code is unique and the parent code references the unit code of the organization unit table; mark the records with empty parent code as root node candidates. Start with the unit code as the starting point and the parent code as the pointer, perform a depth-first traversal. If the unit code is encountered again during traversal, it is determined that there is a loop, the current generation is terminated and an error message is returned; if the parent code of a record does not exist in the organization unit table, it is determined to be an orphan node, the current generation is terminated and an error message is returned. Only when there is no loop and no orphan node, enter the next step.

[0033] The records are processed in order of level identifier from low to high. For records with level identifier as three-level, find the corresponding two-level record with the parent code of the record, and then find the corresponding group-level record with the parent code of the two-level record; for records with level identifier as two-level, find the corresponding group-level record with the parent code of the record; for records with level identifier as group-level, the parent code is empty. The recursive termination condition is to reach the group-level record with empty parent code.

[0034] The process of determining root-to-leaf ordered encoding includes: for records with level identifier as group-level, taking the unit code of the record as the only element of root-to-leaf ordered encoding; for records with level identifier as two-level, taking the unit code of the group-level record as the first element, taking the unit code of the two-level record as the last element, and forming root-to-leaf ordered encoding in the order of ancestor first and descendant second; for records with level identifier as three-level, taking the unit code of the group-level record as the first element, taking the unit code of the two-level record as the middle element, and taking the unit code of the three-level record as the last element, and forming root-to-leaf ordered encoding in the order of ancestor first and descendant second.

[0035] The organization path table writing rule is: for each record of the determined root-to-leaf ordered encoding, insert or update in the organization path table with the unit encoding as the primary key. The root encoding field writes the first unit encoding of the root-to-leaf ordered encoding; the second-level encoding field writes the second unit encoding of the root-to-leaf ordered encoding if there is a second-level hierarchy, or writes a null value if there is no second-level hierarchy; the third-level encoding field writes the third unit encoding of the root-to-leaf ordered encoding if there is a third-level hierarchy, or writes a null value if there is no third-level hierarchy; the complete path field writes a string obtained by arranging the root-to-leaf ordered encoding in the order of ancestors first and descendants later; the path version field is set to the number 1 when the unit encoding is first written, and is set to the number 1 plus the original value when the same unit encoding is written again; the generation time field writes the current time; and the generation note field writes a text automatically generated by the evidence graph construction unit.

[0036] After all records are written, a sampling verification is performed on the organization path table to check whether the combination of the root encoding field, the second-level encoding field, the third-level encoding field, and the complete path field is consistent with the hierarchy identifier in the organization unit table. If the hierarchy identifier is a group hierarchy, the second-level encoding field and the third-level encoding field should be null values and the complete path field should only contain one unit encoding; if the hierarchy identifier is a second-level hierarchy, the third-level encoding field should be a null value and the complete path field should only contain two unit encodings; and if the hierarchy identifier is a third-level hierarchy, the complete path field should contain three unit encodings. After the verification passes, the transaction is submitted, and if the verification fails, the transaction is rolled back and an inconsistency note is returned.

[0037] The fusion and calculation unit standardizes and deduplicates the database view output according to the evidence fingerprint, and the process of obtaining the standardized result includes: reading each database view compiled from the index rule table in turn, and confirming that each output record has the following columns: an associated index encoding column, an attribution unit field name column, a time field name column, and an output field name column; wherein the associated index encoding column and the database view name jointly identify the evidence source, the attribution unit field name column corresponds to the unit encoding of the organization unit table, the time field name column is the evidence time, and the output field name column is the evidence load. If any column is missing, the processing of the database view is aborted and a failure note is recorded. Each output record is rearranged into the following key-value pair set in the order of the unified fields: the source view name, the view record primary key, the evidence attribution unit code, the evidence time, the evidence type, the evidence load, the registration time, and the registration note; wherein the source view name takes the name of the database view, the view record primary key takes the primary key or the combined key that can uniquely identify the row of the database view, the evidence type takes the value of the associated index encoding column, the registration time takes the current time, and the registration note takes a text generated by the fusion and calculation unit standardization.

[0038] The source view name, view record primary key, evidence belonging unit code, evidence time, evidence type and evidence load are spliced in a fixed order into a single line of text, and an irreversible digest is calculated to obtain an evidence fingerprint. The evidence fingerprint is used as a unique key for deduplication and association within the current transaction. Within the same database view, when the evidence fingerprints of two or more output records are the same, the record with the newer registration time is retained as the main record, and the other records are marked as duplicate records and do not enter subsequent processing; between different database views, when the evidence belonging unit code, evidence time and evidence type are the same and the evidence load text is consistent, the evidence fingerprint with the smaller lexicographical order is used as the main record, and the remaining records are marked as homologous records and aggregated in the relationship type homologous when written back subsequently. The retained main records are sorted by evidence belonging unit code and evidence time to form an intra-session temporary result set, which is used as a standardized result for direct reference in subsequent steps.

[0039] Then, the fusion and calculation unit reads the organization path table, parses the root code field, secondary code field and tertiary code field for each record, and forms an ordered code from root to leaf in the order of ancestor first and descendant second. Each ordered code is recorded as a continuous segment, and the unique identifier of the continuous segment is obtained by concatenating the root code field, secondary code field and tertiary code field.

[0040] For each continuous segment and each type of evidence, a window is moved from leaf to root in the order of evidence time. Only the main record belonging to the leaf node unit code of the continuous segment in the standardized result corresponding to the current evidence time is retained in the window, and when the window is pushed to the parent node, the main record is bound to the parent node unit code to form an upward snapshot. For each window push, a database view corresponding to the upward window is generated in the session using a common table expression, containing only four columns of unit code, evidence time, evidence type and evidence load.

[0041] The process of looking down the window established includes: for the same continuous segment and the same type of evidence, moving the window from root to leaf in the order of evidence time. Only the main record belonging to the root node unit code of the continuous segment in the standardized result corresponding to the current evidence time is retained in the window, and when the window is pushed to the child node, the main record is bound to the child node unit code to form a downward snapshot. For each window push, a database view corresponding to the downward window is generated in the session using a common table expression, containing only four columns of unit code, evidence time, evidence type and evidence load.

[0042] For each window advance, a window summary is generated, which contains the unique identifier of the continuous segment, the window direction, the current node unit encoding of the window, the evidence time, the evidence type, the evidence fingerprint set of the uplink snapshot or downlink snapshot, and the generation time. The window summary is only used in the current transaction and is registered in the relationship type window summary in the write-back stage. When the same index appears contradictory evidence in the same node, the conflict fingerprint group is generated according to the source, generation order and coverage interval, and the stable evidence is selected at one time, and the unselected evidence is registered in the conflict summary.

[0043] The contradictory evidence determination rule is: within the same unit encoding, the same evidence time and the same evidence type, if there are two or more main records or uplink snapshots or downlink snapshots, the evidence load text is inconsistent, then it is determined as contradictory evidence. The conflict fingerprint generation and grouping process is: for each group of contradictory evidence, the unit encoding, evidence time, evidence type and source view name are spliced to generate a conflict fingerprint. Grouping is performed using the conflict fingerprint as the key, and the group contains the evidence fingerprints and necessary metadata of all candidate records. The stable evidence selection rule includes: in each group, the unique stable evidence is selected at one time in the following order: preferentially select the candidate record with the smallest source view name in the dictionary order; if there are multiple candidate records with the same source view name, preferentially select the candidate record with the latest view record primary key in the natural order; if the candidate records are from the same source view name and the view record primary key is indistinguishable, compare the coverage interval length of the candidate records, and the longer coverage interval is preferred; the coverage interval is determined by the evidence time granularity expressed by the candidate record in the database view, if the granularity is the same and the length is equal, select the evidence fingerprint with the smallest dictionary order.

[0044] Conflict summary registration: the unselected candidate records are registered to the evidence relationship table with the relationship type conflict, the relationship description is written into the conflict fingerprint and the replacement reason summary; and in the evidence node table, the conflict unselected is marked with the registration description.

[0045] The process of generating shadow nodes between continuous gaps of stable evidences by fusion and computing unit includes: in the same unit code and the same evidence type range, ordering stable evidences by evidence time, and identifying a continuous gap if there is a discontinuous state between the evidence time of two adjacent stable evidences. The process of generating shadow nodes includes: for each continuous gap, inserting a shadow node in the evidence node table, the evidence type of the shadow node being the same as that of the adjacent stable evidence, the evidence time being recorded as a text description of the gap start and end, the evidence load being recorded as a nearest neighbor abstract, the nearest neighbor abstract being the combined text of the evidence fingerprint and the evidence load of the stable evidence closest to the gap at both ends, and the rollback flag being set to true. The process of shadow association includes: in the evidence relationship table, generating a from-relationship between the shadow node and the two stable evidences closest to it by relationship type completion, and writing the relationship description as the gap start and end and the rollback flag.

[0046] The process of writing back the standardized results, window abstracts, conflict abstracts and shadow nodes to the preset evidence node table and the preset evidence relationship table by the fusion and computing unit includes: for each main record in the standardized results, writing back the evidence node table by inserting or updating according to the absence or presence of the evidence number primary key, taking the new serial number or the reversible encoding of the evidence fingerprint as the evidence number, and writing the evidence fingerprint, source view name, view record primary key, evidence time, unit code of evidence ownership, evidence type, evidence load, registration time and registration description fields into the standardized values; for shadow nodes, inserting the evidence node table according to the process of generating shadow nodes. For homologous records, write the relationship type homologous aggregation, the main node number pointing to the evidence number of the retained main record, and the from node number pointing to the evidence number of the homologous record; for window abstracts, write the relationship type window abstract, the main node number pointing to the evidence number corresponding to the unit code of the current node of the window, the from node number pointing to the evidence number corresponding to the uplink snapshot or downlink snapshot, and the relationship description writing the window abstract text; for conflict unselected records, write the relationship type conflict replacement, the main node number pointing to the evidence number of the stable evidence, the from node number pointing to the evidence number of the unselected evidence, and the relationship description writing the conflict fingerprint and the reason for replacement; for shadow association, write the relationship type completion generated according to the process of shadow association. When writing back, always use the unit code of evidence ownership and evidence time as the positioning key, do not change the unit code of evidence ownership and evidence time, do not cross units and time slices, and ensure consistency with the position of the standardized results.

[0047] The process of fusing and calculating the hierarchical consistent score segment set includes: in the same unit code, the same evidence time and the same evidence type range, the stable evidence and the shadow node are merged into a segment candidate set; the number of record entries of the segment candidate set is recorded as a segment value, the evidence time is recorded as a segment time, the evidence type is recorded as a segment type, and the unit code is recorded as a segment unit, to form a score segment. According to the definition of the continuous segment of the organization path table, from leaf to root, in the same evidence time and the same evidence type range, the score segment value of the child unit is added to the parent unit, and the score segment value of the parent unit itself is included, and after the accumulation is completed, the corresponding score segment is formed at each level node. For the score segment of the same unit code, the same evidence time and the same evidence type, only one record is retained as the hierarchical consistent score segment, and the record is written into the evidence node table as the evidence node with the evidence type value as the literal score segment, the evidence load is written into the merged text of the segment value and the segment description; and the node and the stable evidence and the shadow node derived therefrom are related by the relationship type segment, and the relationship description is written into the source quantity and the accumulation rule description.

[0048] The flow unit first retrieves the record of the evidence type as the literal score segment from the evidence node table, groups them according to the unit code and the evidence time of the evidence, and obtains the hierarchical consistent score segment set. The evidence load is parsed into a segment value and a segment description, wherein the segment value appears in the form of a number at the beginning of the evidence load until the first non-numeric character is encountered; the record that fails to be parsed is excluded in this transaction and the literal score segment parsing failure is written in the registration description field. The unit code of the evidence in the hierarchical consistent score segment set is connected and verified with the organization path table according to the unit code, and if there is no matching path, the segment is excluded and the literal no path is written in the registration description field. Only the verified segments are entered into the subsequent steps.

[0049] Next, the flow unit performs the segment merging and sorting list generation process, including: taking the unit code of the evidence and the evidence time as the key, adding the segment values under the same key to a single value to obtain a unit time granularity segment summary list; for all units in the same evidence time, generate a sorting list according to the segment values of the segment summary list from small to large, and record the position number of each unit in this evidence time. After completion, the reference score mapping rule is executed: integer interval mapping is performed on the sorting list in the same evidence time to generate a reference score. Specifically: the unit with the smallest position number has a reference score of 0; the unit with the largest position number has a reference score of 100; the remaining units are evenly distributed with integer values between 0 and 100 according to the position number, and the previous name cannot be smaller than the next name. When the same segment value occurs, the same reference score is assigned and the integer value used is skipped at the subsequent position, until the distribution is completed.

[0050] The process of writing the reference sub-node and the traceability relationship comprises: for each unit and evidence time, generating a record of evidence type as a text reference score, writing the evidence node table, writing the unit code of the evidence belonging to the unit code, writing the evidence time, writing the evidence load into the combined text of the reference score and the segment description, and writing the registration description field into the text generated by the hierarchical consistent score segment set mapping; at the same time, writing the relationship type as the text reference score generated relationship in the evidence relationship table, the main node number pointing to the evidence number of the reference score record, the sub-node number pointing to the evidence number of all the score segment records used to generate the reference score in sequence, and writing the relationship description field into the number of units used for generation and the mapping aperture description.

[0051] The flow unit generates a record of evidence type as a text self-score request for each unit and evidence time, writes the evidence node table, and keeps the evidence belonging to the unit code and the evidence time consistent with the corresponding reference score. The evidence load writes the text to be submitted, and the registration description field writes the text self-score stage start. In the evidence relationship table, the relationship type is written as a text self-score reference relationship, the main node number points to the evidence number of the reference score record, and the sub-node number points to the evidence number of the self-score request record. When the operator submits the self-score value in the self-score stage, the system receives the digital form of the self-score value and the text description through the relational database session variable of the same platform, and performs the following verification: the self-score value must be an integer between 0 and 100. The submission must be able to find a unique reference score record and a self-score request record in the evidence node table, and the evidence belonging to the unit code and the evidence time of the two are completely consistent. If there is already a record of evidence type as a text self-score for the same unit and the same evidence time, the duplicate submission is rejected and an error message is returned.

[0052] After the verification is passed, a record of evidence type as a text self-score is inserted in the evidence node table, the evidence load writes the combined text of the self-score value and the submission description, and the registration description field writes the text self-score has been submitted; at the same time, the relationship type as the text self-score submission is written in the evidence relationship table, the main node number points to the evidence number of the reference score record, and the sub-node number points to the evidence number of the self-score record. If it is not submitted at the end of the self-score stage, a record of evidence type as a text self-score is inserted in the evidence node table by the system, the evidence load directly writes the reference score value, and the registration description field writes the text self-score default reference score.

[0053] The process of starting and generating the second-level review list: according to the organization path table, the units in the second-level hierarchy are identified as the review nodes; for each unit in the second-level hierarchy, all self-score records of the unit and its third-level sub-units at the same evidence time are summarized to generate a review list and write into the evidence relationship table as a relationship type of text second-level review reference, the main node number points to the evidence number of the reference score record of the second-level unit at the evidence time, the from node number points to the evidence number of each self-score record in the review list, and the relationship description field writes in text to be reviewed. The process of second-level review decision and rewriting: the reviewer makes a decision of approval or return for each self-score record in the review list: when approved, the following process is performed: insert a record with evidence type of text second-level review record into the evidence node table, write the combined text of the self-score value in digital form and the text of approval into the evidence load, and write the text of second-level review pass into the registration description field; write the relationship type of text second-level review pass into the evidence relationship table, the main node number points to the evidence number of the reference score record of the second-level unit at the evidence time, and the from node number points to the evidence number of the corresponding self-score record. When returned, the following process is performed: insert a record with evidence type of text second-level review record into the evidence node table, write the combined text of the self-score value in digital form and the text of return into the evidence load, and write the return reason into the registration description field; write the relationship type of text second-level review return into the evidence relationship table, the main node number points to the evidence number of the reference score record of the second-level unit at the evidence time, and the from node number points to the evidence number of the corresponding self-score record; the system simultaneously re-inserts a record with evidence type of text self-score request into the evidence node table for the unit and the evidence time, writes the text of second-level review return into the registration description field, and maintains the verification mechanism until approval.

[0054] According to the organization path table, the unit of the group level is identified as the approval node; for each group level unit, the self-evaluation score records of its own unit, its two-level and three-level sub-units that have passed the two-level audit and the self-evaluation score records that have not entered the two-level audit but already exist are summarized under the same evidence time to generate an approval list, and the relationship type is written as the text group approval reference in the evidence relationship table, the main node number points to the evidence number of the reference score record of the group level unit under the evidence time, the from node number points to the evidence number of the self-evaluation score record or the two-level audit record in the list one by one, and the relationship description field writes the text to be approved. Group approval decision and final writing back: the group approval makes a pass or return decision on the list item by item: the process of passing includes: inserting a record of the evidence type as the text group approval record in the evidence node table, writing the passed digital form score and the combined text of the pass in the evidence load, and writing the group approval pass in the registration description field; at the same time, the relationship type is written as the text group approval pass in the evidence relationship table, the main node number points to the evidence number of the reference score record of the group level unit under the evidence time, and the from node number points to the evidence number of the passed record. The process of returning includes: inserting a record of the evidence type as the text group approval record in the evidence node table, writing the current record score and the combined text of the return in the evidence load, and writing the return reason in the registration description field; writing the relationship type as the text group approval return in the evidence relationship table, the main node number points to the evidence number of the reference score record of the group level unit under the evidence time, and the from node number points to the evidence number of the returned record; the system simultaneously inserts a record of the evidence type as the text self-evaluation score request for the returned unit and the evidence time to resubmit and the two-level audit process.

[0055] If there exists a record of which the evidence type is group review record and the registration description field is group review passed, the numerical form score of the record is taken as the final result of the unit and the evidence time; if not, the score of the self-evaluation record that has passed the secondary review is taken as the final result; if still not, the score of the reference score record is taken as the final result. Once the final result is determined, a record of which the evidence type is text final result is inserted into the evidence node table for the unit and the evidence time, the evidence load writes the combined text of the final result score and the final result description, and the registration description field writes the text flow unit lock; and a relationship of which the relationship type is text result trace is written into the evidence relationship table, the main node number points to the evidence number of the final result record, and the from node number points to the evidence numbers of the group review record or the secondary review record or the self-evaluation or the reference score used to form the final result. All the writing of the reference score, the self-evaluation, the secondary review record, the group review record and the final result involved in the same unit and the same evidence time are uniformly locked in the evidence node table and the evidence relationship table based on the unit code and the evidence time in the same transaction, and writing across units or time granularity is prohibited.

[0056] After all the writing is successful, the transaction is committed; if any writing fails, the transaction is rolled back, and the text transaction rollback is written into the registration description field of the failed record. After the transaction is successfully committed, the hierarchical consistency of the shard set, the reference score, the self-evaluation, the secondary review record, the group review record and the final result can be completely reconstructed in the evidence relationship table.

[0057] An example of implementing the present application in a relational database on the same platform is given below:

[0058] I. Premise and input:

[0059] The organization unit table contains 5 enable records: the group level of unit code G100, the parent code is empty; the secondary level of unit code S110 and S120, the parent code is G100 respectively; the unit code T111 and T112 belong to S110, and the unit code T121 belongs to S120.

[0060] The organization path table has been generated according to the above method: the complete path of G100 is G100; the complete path of S110 is G100 / S110; the complete path of S120 is G100 / S120; the complete path of T111 is G100 / S110 / T111; the complete path of T112 is G100 / S110 / T112; the complete path of T121 is G100 / S120 / T121. The path version is 1.

[0061] The indicator rule table only enables one rule: rule number R001, associated indicator code IND_EVT_A, source table name SRC_A, filter condition expression flag='A', time field name event_date, attribution unit field name unit_code, aggregation mode count, output field name cnt, view name VW_IND_EVT_A, and rule status compiled.

[0062] The source table name SRC_A already exists in the same platform's relational database, and the sample data covers two months, 2025-01 and 2025-03, with no records for 2025-02, which is used to trigger the shadow nodes: T111 has 2 events in 2025-01; T112 has 1 event in 2025-01; T121 has 3 events in 2025-01; G100 has 1 event in 2025-01; and only T112 has 2 events in 2025-03. Each original row has a monotonically increasing primary key id and flag='A'. The indicator rule is compiled into a database view VW_IND_EVT_A, whose output columns include: associated indicator code column IND_EVT_A, attribution unit field name column unit_code, time field name column period (normalized date by month), output field name column cnt, and rule number constant column R001. The organization unit table is recursively processed from bottom to top, and the organization path table has been completed as previously described.

[0063] II. Instantiation operation of the fusion and calculation unit and generated data

[0064] From the database view VW_IND_EVT_A, the grouping output for evidence time 2025-01 is: cnt = 2 for T111; cnt = 1 for T112; cnt = 3 for T121; cnt = 0 (no direct event) for S110; cnt = 0 (no direct event) for S120; cnt = 1 for G100. Each view row generates an evidence fingerprint in a fixed order. For ease of reference, this example uses 12-digit decimal irreversible digest example values: the evidence fingerprint for T111@2025-01 is 202501000701; the evidence fingerprint for T112@2025-01 is 202501000501; the evidence fingerprint for T121@2025-01 is 202501000901; the evidence fingerprint for G100@2025-01 is 202501000201. When writing to the evidence node table, the evidence type is registered as IND_EVT_A, and the evidence payload is written as the corresponding cnt number and brief description. The organization path table defines the contiguous segments: G100 / S110 / T111 G100 / S110 / T112 G100 / S120 / T121. On each contiguous segment, the evidence type IND_EVT_A establishes an upward transmission window and a downward lookback window, generating a window digest. Example: on the contiguous segment G100 / S110 / T111, at evidence time 2025-01, the upward transmission window binds the primary record snapshot of T111 to S110, and then to G100; the downward lookback window binds the primary record snapshot of G100 to S110, and then to T111. The corresponding window digests are registered in the evidence relationship table, with the relationship type as window digest.

[0065] For T111@2025-01, the snapshot value brought by the upward transmission window is 2 (from the primary record of T111), and the snapshot value brought by the downward lookback window is 1 (from the primary record of G100, bound downward to T111). Both belong to the evidence type IND_EVT_A, have the same evidence time and unit code, and constitute contradictory evidence. A conflict fingerprint CF_T111_202501_IND_EVT_A_0001 (example value) is generated, and after grouping, the stable evidence is selected according to the rule once: the source view names are the same, the view record primary keys are compared, and the newer one in natural order is T111@2025-01, with the value 2 selected as the stable evidence; the unselected downward snapshot is registered as a conflict digest of the relationship type conflict replacement.

[0066] T112 has no record in 2025-02, and has record again in 2025-03. The system generates a shadow node for T112 in 2025-02, the evidence type is still IND_EVT_A, the evidence load writes the value 1 to the nearest neighbor summary of the nearest neighbor 2025-01, the rollback flag writes is. And the relationship type is completed to generate the associated shadow node with the adjacent stable evidence. For the evidence time 2025-01, this example only performs hierarchical aggregation on the event class count. Define and calculate: let represent the total unit set; let represent a single evidence time 2025-01; let represent a unit; let represent the sub-unit set of the unit ; let represent the count of the unit at this level at time ; let represent the hierarchical consistent count result of the unit at time . The hierarchical consistent rule is ; wherein, is defined as a set of sub-relationships from the organization path table at the first occurrence.

[0067] Calculate according to the above formula:

[0068] T111: .

[0069] T112: .

[0070] T121: .

[0071] S110: .

[0072] S120: .

[0073] G100: .

[0074] Write the values of the above 6 units at 2025-01 into the evidence node table as evidence type score fragments, and merge the corresponding stable evidence and shadow nodes (if any) with the relationship type fragments.

[0075] III. Instantiation operation of the flow unit and generated data

[0076] At evidence time 2025-01, collect the score fragment values of all units to form a set . Define and calculate the reference score: let denotes the unique value set of the same evidence time score segment after deduplication and sorting from small to large, wherein denotes the number of different values in the set; let denotes the unit The score segment of time corresponds to the index position in the set ; let denotes the unit The reference score of time The integer equal interval mapping rule is ; wherein, defined as the floor operation at the first occurrence.

[0077] In this example, , Accordingly, the value of 1 unit (T112) The value of 2 units (T111) The value of 3 units (S110, S120, T121) The value of 7 units (G100) Write 6 reference scores into the evidence node table as evidence type reference scores, and generate relationship type reference scores to point to the respective score segments for calculation.

[0078] Generate a self-score request for each unit on January 2025, and then collect the self-scores: T111 submits a self-score of 35; T112 does not submit, the system writes the self-score and sets the value to the reference score 0; T121 submits a self-score of 70; S110 submits a self-score of 68; S120 submits a self-score of 66; G100 submits a self-score of 100. All self-scores are written into the evidence node table, and relationship type self-scores are submitted to connect to the respective reference scores.

[0079] Audit the secondary level units, including: S110 audits the self-scores of its child units T111, T112. The decision is as follows: agree to T111's 35; return T112's 0 once, the system regenerates the self-score request, T112 does not supplement, and finally S110 agrees to record T112's 0. S120 audits the self-scores of its child unit T121. The decision is as follows: agree to T121's 70; agree to S120's 66. Write the above agreements and returns into the evidence node table as secondary audit records, and record the secondary audit pass or secondary audit return in the evidence relationship table.

[0080] The process of group review stage includes: group level unit G100 reviews all the self-evaluation scores passed the second-level review. In this example, all are passed: 35 for T111, 0 for T112, 70 for T121, 68 for S110, 66 for S120, and 100 for G100. The corresponding group review records are written into the evidence node table one by one, and the relationship type group review pass is established with the source second-level review record or self-evaluation score.

[0081] Let denote the unit In time The group review pass score; let denote the unit In time The second-level review pass score; let denote the unit In time The self-evaluation score; let denote the unit In time The final result score. The value rule is

[0082] ;

[0083] Accordingly, in 2025-01, we get for T111; for T112; for T121; for S110; for S120; for G100. The above six final results are written into the evidence node table, and the relationship type result traceability is connected to the corresponding group review record (if any) or second-level review record or self-evaluation score or reference score.

[0084] The definition of evidence fingerprint is: let denote the irreversible digest function of text ; let denote the source view name; let denote the view record primary key; let denote the evidence unit code; let denote the evidence time; let denote the evidence type; let denote the evidence load text. The evidence fingerprint is defined as ; wherein denotes the string concatenation operation. The decimal digest value 202501000701 in the example is returned by the function.

[0085] The conflict fingerprint is defined as: let represents unit of encoding; let represents evidence time; let represents evidence type; let represents source view name; define conflict fingerprint as ; in this example, the conflict group key CF_T111_202501_IND_EVT_A_0001 of T111@2025-01 corresponds to one example value of this definition.

[0086] Referring to Figure 2 , the multi-level group ecological environment assessment subsystem of the present application shows significant performance optimization characteristics in the index rule compilation process. The graph takes the number of rules as the abscissa and the compilation time as the ordinate, systematically showing the change law of the compilation performance with the increase of the number of rules. In the system initialization stage, when the number of rules is small (1-15), the compilation time presents a linear growth trend, and the compilation time curve and the view generation rate curve are basically coincident, indicating that the system has not started the optimization mechanism at this time. When the number of rules reaches about 15, the system triggers the first compilation node, at which time the compilation engine starts to load the SQL parser and related optimization components, and the compilation time appears a short fluctuation. With the number of rules further increasing to 25, the system enters the optimization start stage. At this node, the compilation engine starts the O2 level optimization, and at the same time activates the parallel compilation mechanism, using 4 concurrent threads to process multiple rule compilation tasks at the same time. The slope of the compilation time curve significantly decreases, indicating that the average compilation time per rule begins to shorten. The view generation rate curve is obviously separated from the compilation time curve in this stage, showing the technical advantage of the system in improving the view cache hit rate. When the number of rules reaches 35, the system enters the cache effective stage, which is the key turning point of the performance curve. At this time, the view cache hit rate reaches 85%, and a large number of repeated or similar index rules can directly reuse the compiled database view to avoid repeated compilation overhead. The compilation time curve tends to be flat, indicating that the system has good scalability. The rule state marker line and the cache enable marker line are displayed in different dashed line styles in the graph, using dot-dash line and long-dash line modes respectively, which clearly identify the time nodes of the system state transition. In the stable state stage, that is, when the number of rules exceeds 45, the compilation time is basically stable at about 285 milliseconds, and the compilation success rate is maintained at a high level of 98%. At this time, the system has fully adapted to the processing needs of large-scale rule set, and the compilation performance reaches the optimal state. The key node markers in the graph are black solid dots, which accurately locate the critical values of each performance transition stage, providing important reference data for system tuning and capacity planning.

[0087] Referring to Figure 3The figure shows the technical effect of the application on the compression of organizational hierarchical data using the path merging algorithm in detail. The figure takes the original data volume as the abscissa and the compressed data volume as the ordinate. By comparing the data volume before and after compression, the optimization effect of the continuous segment compression technology is quantitatively displayed. The data volume curve before compression is represented by a black dotted line, showing an accelerating upward trend as the original data volume increases. When the original data volume increases from 50MB to 500MB, the data occupancy space of the untreated data increases from 10MB to 100MB, with a growth multiple of 10. This non-linear growth is mainly due to the complexity of the organizational hierarchical structure, especially when the number of records in the organizational unit table increases, the corresponding organizational path table and related index structure will generate exponential storage overhead. The compressed data volume curve is represented by a black solid line, showing a significantly different growth pattern. Under the same original data volume conditions, the data volume after continuous segment compression processing grows more gently and eventually stabilizes at about 46MB, saving 54MB of storage space compared to untreated data, achieving a compression ratio of 65%. This effect is mainly achieved through the following technical means: first, the system generates a unique segment identifier according to the splicing rules of the root encoding field, the second-level encoding field and the third-level encoding field; second, each ordered encoding path is recorded as a continuous segment to achieve logical grouping of data; finally, the memory mapping technology is used to realize segment loading, avoiding repeated storage of full data. The four continuous segment marker points marked in the figure correspond to different sizes of data processing stages. The start point of segment 1 is located at the original data volume of 100MB, marking the start of the continuous segment identification mechanism; the start point of segment 2 is located at 200MB, at which point the compression effect begins to appear; the start point of segment 3 is located at 300MB, at which point the compression algorithm enters a stable running state; the start point of segment 4 is located at 400MB, at which point the system achieves the best compression effect. Each continuous segment has an upward transmission window and a downward lookback window, and a window summary is automatically generated through CTE (Common Table Expression), further optimizing data access efficiency. The compression space saving area is filled with light gray, which directly shows the storage optimization effect brought by the compression technology. The compression effect line is marked by a vertical dotted line, which quantitatively shows a saving of 65%. This compression effect not only reduces storage costs, but also significantly improves data query and processing performance, providing reliable technical support for ecological environment assessment in large-scale group environments.

[0088] The present application is not limited to the foregoing specific embodiments. The present application extends to any new feature or any new combination disclosed in this specification, as well as any new method or process steps or any new combination disclosed.

Claims

1. A multi-level group ecological environment assessment subsystem, characterized in that, include: An evidence graph construction unit is used to create an organizational unit table, an organizational path table, and an indicator rule table in a relational database on the same platform, compile the indicator rules into a database view, traverse the records in the organizational unit table and recursively go from bottom to top to the root node according to the parent code, write the ordered code from root to leaf into the root code field, second-level code field, third-level code field, and complete path field of the organizational path table, and generate evidence nodes and evidence relationships based on the database view output to form an assessment evidence graph. A fusion and computation unit is used to standardize and deduplicate the database view output based on the assessment evidence graph and evidence fingerprint to obtain the standardized result; The standardized results are compressed into continuous segments based on the organizational path table. Within each segment, upward pass-through windows and downward look-back windows are created to generate upstream and downstream corresponding database views, and window summaries are recorded. When contradictory evidence for the same indicator appears at the same node, conflict fingerprints are generated based on the source, generation order, and coverage area. The contradictory evidence is then grouped, stable evidence is selected all at once, and the unselected evidence is registered with conflict fingerprints. Shadow nodes are generated for continuous gaps between stable evidence, recording the gap start and end, nearest neighbor summary, and rollback flag. The standardized results, window summaries, conflict fingerprints, and shadow nodes are written back to the preset evidence node table and preset evidence relationship table in their original positions to generate a hierarchically consistent score segment set. A workflow unit is used to automatically generate reference scores based on a set of hierarchical consistent score segments, and to flow through the system in the order of self-assessment, secondary review, and group approval.

2. The multi-level group ecological environment assessment subsystem as described in claim 1, characterized in that, The organization unit table contains fields such as unit code, unit name, parent code, hierarchy identifier, activation flag, creation time, and update time. The unit code is the primary key, the parent code references the unit code in the organization unit table to form a foreign key constraint, and the hierarchy identifier is limited to group level, second level, and third level. The organization path table includes fields for unit code, root code, second-level code, third-level code, full path, path version, generation time, and generation description, with the unit code being the primary key. The indicator rule table includes fields for rule number, rule name, associated indicator code, source table name, filter condition expression, time field name, affiliated unit field name, aggregation method, output field name, view name, rule status, and last compilation time, with the rule number being the primary key.

3. The multi-level group ecological environment assessment subsystem as described in claim 2, characterized in that, The Evidence Node table contains fields for Evidence Number, Evidence Fingerprint, Source View Name, View Record Primary Key, Evidence Time, Evidence Tolerant Code, Evidence Type, Evidence Load, Registration Time, and Registration Description. The Evidence Number is the primary key, and the Evidence Tolerant Code references the unit code in the Organization Unit table. The Evidence Relationship table contains fields for Relationship Number, Master Node Number, Slave Node Number, Relationship Type, Relationship Time, Source View Name, Relationship Description, and Registration Time. The Relationship Number is the primary key, and the Master Node Number and Slave Node Number reference the Evidence Number in the Evidence Node table.

4. The multi-level group ecological environment assessment subsystem as described in claim 3, characterized in that, The process of determining continuous segments by the fusion and computation unit includes: reading the organization path table, parsing the root coding field, secondary coding field and tertiary coding field for each record, and forming an ordered coding path from root to leaf in the order of ancestor first and offspring last; recording each ordered coding path as a continuous segment, and the unique identifier of the continuous segment is obtained by concatenating the root coding field, secondary coding field and tertiary coding field.

5. The multi-level group ecological environment assessment subsystem as described in claim 4, characterized in that, The process by which the fusion and computation unit establishes upward pass-through windows and downward look-back windows within each segment includes: for each continuous segment and each type of evidence, moving the window from the leaf node to the root node in chronological order of evidence time; retaining only the evidence record belonging to the leaf node unit code of the continuous segment in the standardized results corresponding to the current evidence time within the window, and binding the evidence record with the parent node unit code when advancing to the parent node to form an upward snapshot; for each window advancement, generating an upward window corresponding database view containing only four columns—unit code, evidence time, evidence type, and evidence load—within the session using a common table expression; for the same continuous segment and the same type of evidence, moving the window from the root node to the leaf node in chronological order of evidence time; retaining only the evidence record belonging to the root node unit code of the continuous segment in the standardized results corresponding to the current evidence time within the window, and binding the evidence record with the child node unit code when advancing to the child node to form a downward snapshot; for each window advancement, generating a downward window corresponding database view containing only four columns—unit code, evidence time, evidence type, and evidence load—within the session using a common table expression.

6. The multi-level group ecological environment assessment subsystem as described in claim 4, characterized in that... The process of determining contradictory evidence by the fusion and calculation unit includes: if there are two or more evidence records, upstream snapshots or downstream snapshots within the same unit code, the same evidence time and the same evidence type, and their evidence load field content is inconsistent, then they are determined to be contradictory evidence; the process of generating conflict fingerprints and grouping includes: for each group of contradictory evidence, generating a conflict fingerprint by concatenating the unit code, evidence time, evidence type and source view name; grouping by using the conflict fingerprint as the key, with each group containing the evidence fingerprints of all candidate records and related metadata.

7. The multi-level group ecological environment assessment subsystem as described in claim 4, characterized in that, in, The process of selecting stable evidence in one go is as follows: Within each group, a unique stable evidence is selected in one go in the following order: First, the candidate record with the smallest lexicographical order of the source view name is selected; if there are multiple candidate records with the same source view name, the candidate record with the latest primary key in natural order is selected; if all candidate records come from the same source view name and the primary keys of the view records are indistinguishable, the length of the coverage interval of the candidate records is compared, and the one with the longer coverage interval is selected; the coverage interval is determined by the evidence time granularity expressed by the candidate record in the database view. If the granularity is the same and the length is equal, the one with the smallest lexicographical order of the evidence fingerprint is selected; the candidate records that are not selected are registered in the evidence relationship table with conflict replacement as the relationship type, and the conflict fingerprint and the summary of the reason for replacement are written in the relationship description; and they are marked as conflict not selected in the evidence node table with a registration description.

8. The multi-level group ecological environment assessment subsystem as described in claim 7, characterized in that, The process of generating shadow nodes for continuous gaps between stable evidence by the fusion and computation unit includes: within the same unit encoding and the same evidence type range, stable evidence is sorted by evidence time; if there is a preset time unit interval between the evidence times of two adjacent stable evidences, it is identified as a continuous gap; for each continuous gap, a shadow node is inserted into the evidence node table. The evidence type of the shadow node is the same as that of the adjacent stable evidence, the evidence time is recorded as a text description of the gap start and end range, and the evidence load is recorded as a nearest neighbor summary. The nearest neighbor summary is the combined text of the evidence fingerprint of the stable evidence closest to the gap at both ends and the evidence load. The rollback flag is set to yes; in the evidence relationship table, completion generation is used as the relationship type, and a subordinate relationship is established between the shadow node and its two nearest neighbor stable evidences. The gap start and end range and the rollback flag are written in the relationship description.

9. A method for implementing the multi-level group ecological environment assessment subsystem as described in any one of claims 1 to 8, characterized in that, The method includes: Step 1: Create an organizational unit table, an organizational path table, and an indicator rule table in a relational database on the same platform, and compile the indicator rules into a database view; traverse the records in the organizational unit table and recursively go up to the root node according to the parent code, and write the ordered code from root to leaf into the root code field, second-level code field, third-level code field, and complete path field of the organizational path table; and generate evidence nodes and evidence relationships based on the database view output to form an assessment evidence graph; Step 2: Based on the assessment evidence graph, standardize and deduplicate the database view output according to the evidence fingerprint to obtain the standardized result; compress the standardized result into continuous segments based on the organizational path table, and establish an upward pass-through window and a downward look-back window in each segment to generate upstream and downstream corresponding database views, and record the window summary; when contradictory evidence appears at the same node for the same indicator, generate conflict fingerprints according to the source, generation order and coverage interval, and group the contradictory evidence, then select stable evidence at once, and register the unselected evidence with conflict fingerprints; generate shadow nodes for continuous gaps between stable evidence, and record the gap start and end, nearest neighbor summary and rollback mark of the shadow nodes; write the standardized result, window summary, conflict fingerprint and shadow nodes back to the preset evidence node table and preset evidence relationship table in their original positions to generate a set of hierarchically consistent score segments; Step 3: Automatically generate reference scores based on the set of score segments consistent with the hierarchical level, and process them in the order of self-assessment, secondary review, and group approval.

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