Land resource survey result storage method and system based on artificial intelligence

Abstract

The invention relates to a territorial resource survey result storage method and system based on artificial intelligence, and aims to solve the problems of low efficiency and insufficient intelligence in the storage verification process of massive survey result data. The system preprocesses uploaded data through a survey result reading and analyzing unit, extracts core content and generates a unique identifier; the MD5 storage verification unit is used for realizing rapid comparison and result reuse; and when direct matching cannot be performed, the survey result storage and verification unit performs deep verification in combination with the encrypted information table, and manual verification can be triggered. The system is further provided with a processing unit, through maintaining a log table and a mapping relation pair, historical verification experience is dynamically accumulated, and features which are stable in conclusion and appear at high frequency are converted into a rapid judgment basis, so that an intelligent verification assembly line with self-learning and self-adaptive capabilities is formed. According to the method, the automation level and the processing efficiency of storage verification are remarkably improved while the data security and the verification preciseness are guaranteed.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a method and system for storing land and resources survey results based on artificial intelligence. Background Technology

[0002] With the widespread application of information technology in the field of land and resources management, the massive amounts of survey data generated by various survey entities are showing a continuous growth trend. This data is not only an important basis for land spatial planning, resource monitoring and supervision, and scientific decision-making, but also often involves sensitive geospatial information and resource attribute data, placing high demands on the security, integrity, and verifiability of data storage. Therefore, establishing an efficient and reliable mechanism for storing and verifying survey results to ensure the authenticity, accuracy, and compliance of the data entered into the database has become a fundamental and crucial task in the current development of land and resources informatization.

[0003] In current technical practices, the storage and verification of survey results typically relies on direct content comparison or filtering mechanisms based on fixed rules. These methods often suffer from low verification efficiency and insufficient adaptability when dealing with large-scale, multi-source data. They struggle to quickly identify duplicate submissions or highly similar results, and cannot dynamically and intelligently manage data involving specific confidentiality requirements. Furthermore, traditional verification processes lack effective learning and evolution capabilities, failing to accumulate experience from historical verification records to optimize subsequent verification efficiency, potentially leading to a decline in system processing performance as the data volume increases.

[0004] Therefore, the industry urgently needs a more intelligent storage and verification solution. This solution should be able to quickly identify and deduplicate survey results, and accurately screen and prioritize content that may involve sensitive information. By introducing an adaptive learning mechanism, the system can gradually transform frequently occurring data features with stable verification conclusions into rapid judgment criteria. This ensures the rigor of verification while significantly improving overall processing efficiency and the system's automation level, thus meeting the practical needs of modern land and resources survey results management. Summary of the Invention

[0005] To achieve the above objectives, this application provides the following technical solution: According to a first aspect of the present invention, the present invention claims protection for an artificial intelligence-based land and resources survey results storage system, the system comprising the following components: The survey results reading and analysis unit is used to read the survey results uploaded by the land and resources survey entity, perform information-cutting analysis on the survey results to remove survey time periods and result information that have no impact on the classification of confidentiality, select the core legitimate survey results, and use the MD5 hash algorithm to calculate the MD5 hash value of the legitimate survey results. The MD5 storage verification unit is used to query the MD5 storage verification mapping relationship pair to confirm whether there is a key-value pair therein, and the MD5 value contained in the key-value pair is equal to the MD5 hash value of the legitimate investigation result. If it is confirmed to exist, the storage verification index in the key-value pair will be directly used as the final storage verification result of the survey results uploaded by the land and resources survey subject. The survey results storage verification unit is used to initiate a verification process based on the encrypted information survey results table when the MD5 storage verification unit confirms that there is no matching key-value pair in the MD5 storage verification mapping relationship, and to match the legitimate survey results with the encrypted information in the encrypted information survey results table; If no encrypted information is found in the legitimate survey results, the survey results uploaded by the land and resources survey entity are deemed qualified, and the storage verification result is passed. If the legitimate survey results match encrypted information, a storage verification request from the administrator is received, and a manual or semi-automatic deep storage verification is performed on the legitimate survey results that match the encrypted information based on the storage verification request, generating a storage verification result of the survey results uploaded by the land and resources survey subject. The MD5 storage verification mapping relationship processing unit is used to perform a synchronization operation on the logs in the log table based on the generated storage verification result after the investigation result storage verification unit completes the storage verification of the legitimate investigation results that match the encrypted information. Each log entry in the log table contains three fields: MD5 value, storage verification index, and storage verification frequency. The MD5 storage verification mapping relationship processing unit is further used to determine whether the storage verification frequency of the synchronized log exceeds the preset maximum threshold. If it does, the log entry is deleted from the log table, and its MD5 value and storage verification index are inserted as a new key-value pair into the MD5 storage verification mapping relationship pair to realize the accumulation and synchronization of verification knowledge.

[0006] Furthermore, it also includes: The information analysis performed by the survey results reading and analysis unit specifically includes: The uploaded survey results are structured and parsed to identify and extract the metadata, which includes the survey task initiation time, data processing log timestamps, and auxiliary text information for format description. The stripping operation is based on a preset metadata tag library and natural language processing model to ensure that only core geographic information entities, attribute data, and spatial relationship data that directly affect the determination of data confidentiality level are retained, thus constituting the legitimate survey results.

[0007] Furthermore, it also includes: Each key-value pair in the MD5 storage verification mapping relationship also includes a valid period index; the valid period index is an integer value that decreases over time or system operation cycle, and the initial value is set by the system. While confirming the existence of a matching key-value pair and directly returning the storage verification index, the MD5 storage verification unit performs the operation of resetting the legal period index in the key-value pair to the preset maximum legal period T_max. The MD5 storage verification mapping relationship processing unit also periodically traverses all key-value pairs and decrements their valid period index by 1. When it detects that the valid period index of a certain key-value pair has decreased to 0, it automatically deletes the key-value pair from the MD5 storage verification mapping relationship.

[0008] Furthermore, it also includes: The MD5 storage verification mapping relationship enables the processing unit to perform the following logic for synchronizing the log table: Check if a log exists in the log table, and if its MD5 value field is equal to the MD5 hash value of the legitimate investigation result that matches the encrypted information. If it does not exist, a new log will be created, with the MD5 value field set to the MD5 hash value, the storage verification index field set to the storage verification result generated this time, and the storage verification frequency field initialized to 1; if it exists, the adjustment logic will be entered.

[0009] Furthermore, it also includes: Compare the generated storage verification result with the storage verification indicator field value of the existing log entry to see if they are consistent. If they match, increment the value of the storage verification frequency field of that log entry by 1; if they do not match, decrement the value of the storage verification frequency field of that log entry by 1. Determine whether the reduced storage verification frequency field value is less than the preset minimum threshold F_min; if it is less than F_min, delete the log from the log table; the storage verification frequency field reflects the consistency and stability of historical storage verification conclusions based on the same MD5 value.

[0010] According to a second aspect of the present invention, the present invention claims protection for a method for storing land and resources survey results based on artificial intelligence, the specific steps of which are as follows: Step S110: Read the survey results uploaded by the land and resources survey entity, remove auxiliary information that has no impact on the classification of confidentiality through information cutting analysis, extract the legitimate survey results, and calculate the MD5 hash value of the legitimate survey results. Step S120: Query the MD5 storage verification mapping relationship pair and determine whether there is a key-value pair whose MD5 value is equal to the MD5 hash value calculated in step S110. If yes, proceed to step S130; otherwise, proceed to step S140. Step S130: The storage verification index in the matching key-value pair is directly used as the final storage verification result of the survey results uploaded by the land and resources survey subject, and the legal period index in the key-value pair is reset to the maximum legal period T_max; Step S140: Match the legitimate survey results with the preset encrypted information survey results table; if no encrypted information is matched, the survey results are deemed qualified and the storage verification result is passed; if encrypted information is matched, the administrator's storage verification request is received, and a deep storage verification is performed on the legitimate survey results based on the request to generate a storage verification result. Step S150: Based on the generated storage verification result, update the log table and query whether there is a log in the log table whose MD5 value is equal to the current MD5 hash value. If it does not exist, add it. If it exists, increase or decrease its storage verification frequency according to whether the storage verification result is consistent, and delete the log when the frequency is lower than the minimum value F_min. Step S160: Check the updated logs. If the storage verification frequency of a certain log exceeds the preset maximum threshold F_max, remove the log from the log table and add its MD5 value and storage verification index as a new key-value pair to the MD5 storage verification mapping relationship pair. Step S170: Periodically decrement the valid periodicity index of all key-value pairs in the MD5 storage verification mapping relationship by 1, and delete the key-value pairs whose valid periodicity index has been decremented to 0 from the mapping relationship.

[0011] Furthermore, the information analysis in step S110 specifically includes: The uploaded survey results are structured and parsed to identify and extract the metadata, which includes the survey task initiation time, data processing log timestamps, and auxiliary text information for format description. The stripping operation is based on a preset metadata tag library and natural language processing model to ensure that only core geographic information entities, attribute data, and spatial relationship data that directly affect the determination of data confidentiality level are retained, thus constituting the legitimate survey results.

[0012] Furthermore, it also includes: In step S130, each key-value pair in the MD5 storage verification mapping relationship also includes a valid periodicity index; The legal cycle indicator is an integer value that decreases over time or system operating cycle, and its initial value is set by the system.

[0013] Furthermore, the specific logic for updating the log table in step S150 is as follows: Check if there is a log in the log table whose MD5 value is equal to the current MD5 hash value. If not, add a new log, set its MD5 value field to the current MD5 hash value, set the storage verification index field to the storage verification result generated this time, and initialize the storage verification frequency field to 1. If it exists, compare the storage verification result generated this time with the storage verification indicator field value of the existing log. If they match, increment the storage verification frequency field value of the log entry by 1; if they do not match, decrement the storage verification frequency field value of the log entry by 1. Determine whether the decremented storage verification frequency field value is less than the preset minimum threshold F_min; if it is less than F_min, delete the log entry from the log table.

[0014] Furthermore, it also includes: In step S170, the valid periodicity index of all key-value pairs in the MD5 storage verification mapping relationship is periodically decremented by 1, and key-value pairs whose valid periodicity index has been decremented to 0 are deleted from the mapping relationship.

[0015] This application relates to an artificial intelligence-based method and system for storing land and resources survey results, aiming to solve the problems of low efficiency and insufficient intelligence in the storage and verification process of massive survey data. The system preprocesses uploaded data through a survey result reading and analysis unit, extracting core content and generating unique identifiers; it utilizes an MD5 storage verification unit to achieve rapid comparison and result reuse; when direct matching is not possible, the survey result storage verification unit performs deep verification in conjunction with an encrypted information table, which can trigger manual review. The system also includes a processing unit that dynamically accumulates historical verification experience by maintaining a log table and mapping relationships, transforming stable and frequently occurring characteristics of conclusions into rapid judgment criteria, thereby forming an intelligent verification pipeline with self-learning and adaptive capabilities. This invention significantly improves the automation level and processing efficiency of storage verification while ensuring data security and verification rigor. Attached Figure Description

[0016] Figure 1 A structural module diagram of a land and resources survey results storage system based on artificial intelligence, as claimed in an embodiment of the present invention; Figure 2 The flowchart illustrates a method for storing land and resources survey results based on artificial intelligence, as claimed in this embodiment of the invention. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0018] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of those features. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications in the embodiments of this application, such as up, down, left, right, front, back, etc., are only used to explain the relative positional relationships and movements between components in a specific orientation as shown in the accompanying drawings. If the specific orientation changes, the directional indications will change accordingly. Furthermore, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0019] References to embodiments herein mean that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0020] In the daily data collection and storage scenarios of the provincial land and resources survey data center, massive amounts of survey result data packets are submitted daily from dozens of municipal-level survey units. These data packets are diverse in format, complex in content, and contain a large amount of auxiliary information unrelated to core geographic entities. Traditional manual or rule-based automated security level screening and storage verification processes suffer from bottlenecks such as high response latency, repetitive work, and low intelligence when faced with such high concurrency and large-volume data flows. This embodiment will elaborate on the specific operating mechanism of the system and method of the present invention in this scenario.

[0021] According to a first embodiment of the present invention, the present invention claims protection for an artificial intelligence-based land and resources survey results storage system, see [link to relevant documentation]. Figure 1This system includes a survey results reading and analysis unit, an MD5 storage verification unit, a survey results storage verification unit, and an MD5 storage verification mapping relationship processing unit. These units work collaboratively to form an intelligent storage verification pipeline with self-learning and adaptive capabilities.

[0022] The system includes the following components: The survey results reading and analysis unit is used to read the survey results uploaded by the land and resources survey entity, perform information-cutting analysis on the survey results to remove survey time periods and result information that have no impact on the classification of confidentiality, select the core legitimate survey results, and use the MD5 hash algorithm to calculate the MD5 hash value of the legitimate survey results. The MD5 storage verification unit is used to query the MD5 storage verification mapping relationship pair to confirm whether there is a key-value pair therein, and the MD5 value contained in the key-value pair is equal to the MD5 hash value of the legitimate investigation result. If it is confirmed to exist, the storage verification index in the key-value pair will be directly used as the final storage verification result of the survey results uploaded by the land and resources survey subject. The survey results storage verification unit is used to initiate a verification process based on the encrypted information survey results table when the MD5 storage verification unit confirms that there is no matching key-value pair in the MD5 storage verification mapping relationship, and to match the legitimate survey results with the encrypted information in the encrypted information survey results table; If no encrypted information is found in the legitimate survey results, the survey results uploaded by the land and resources survey entity are deemed qualified, and the storage verification result is passed. If the legitimate survey results match encrypted information, a storage verification request from the administrator is received, and a manual or semi-automatic deep storage verification is performed on the legitimate survey results that match the encrypted information based on the storage verification request, generating a storage verification result of the survey results uploaded by the land and resources survey subject. The MD5 storage verification mapping relationship processing unit is used to perform a synchronization operation on the logs in the log table based on the generated storage verification result after the investigation result storage verification unit completes the storage verification of the legitimate investigation results that match the encrypted information. Each log entry in the log table contains three fields: MD5 value, storage verification index, and storage verification frequency. The MD5 storage verification mapping relationship processing unit is further used to determine whether the storage verification frequency of the synchronized log exceeds the preset maximum threshold. If it does, the log entry is deleted from the log table, and its MD5 value and storage verification index are inserted as a new key-value pair into the MD5 storage verification mapping relationship pair to realize the accumulation and synchronization of verification knowledge.

[0023] Furthermore, it also includes: The information analysis performed by the survey results reading and analysis unit specifically includes: The uploaded survey results are structured and parsed to identify and extract the metadata, which includes the survey task initiation time, data processing log timestamps, and auxiliary text information for format description. The stripping operation is based on a preset metadata tag library and natural language processing model to ensure that only core geographic information entities, attribute data, and spatial relationship data that directly affect the determination of data confidentiality level are retained, thus constituting the legitimate survey results.

[0024] Furthermore, it also includes: Each key-value pair in the MD5 storage verification mapping relationship also includes a valid period index; the valid period index is an integer value that decreases over time or system operation cycle, and the initial value is set by the system. While confirming the existence of a matching key-value pair and directly returning the storage verification index, the MD5 storage verification unit performs the operation of resetting the legal period index in the key-value pair to the preset maximum legal period T_max. The MD5 storage verification mapping relationship processing unit also periodically traverses all key-value pairs and decrements their valid period index by 1. When it detects that the valid period index of a certain key-value pair has decreased to 0, it automatically deletes the key-value pair from the MD5 storage verification mapping relationship.

[0025] Furthermore, it also includes: The MD5 storage verification mapping relationship enables the processing unit to perform the following logic for synchronizing the log table: Check if a log exists in the log table, and if its MD5 value field is equal to the MD5 hash value of the legitimate investigation result that matches the encrypted information. If it does not exist, a new log will be created, with the MD5 value field set to the MD5 hash value, the storage verification index field set to the storage verification result generated this time, and the storage verification frequency field initialized to 1; if it exists, the adjustment logic will be entered.

[0026] Furthermore, it also includes: Compare the generated storage verification result with the storage verification indicator field value of the existing log entry to see if they are consistent. If they match, increment the value of the storage verification frequency field of that log entry by 1; if they do not match, decrement the value of the storage verification frequency field of that log entry by 1. Determine whether the reduced storage verification frequency field value is less than the preset minimum threshold F_min; if it is less than F_min, delete the log from the log table; the storage verification frequency field reflects the consistency and stability of historical storage verification conclusions based on the same MD5 value.

[0027] In this embodiment, the survey results reading and analysis unit serves as the data entry point, responsible for receiving and parsing the survey results data package uploaded by the land and resources survey entity. This data package is typically a compressed file containing spatial vector data files, attribute database tables, metadata XML documents, survey process record documents, and a results report PDF. The survey results reading and analysis unit first decompresses the data package and then launches a structured parsing engine. The parsing engine uses a pre-defined metadata tag library, which defines dozens of tags not directly related to data security classification, such as survey task initiation time, data processing log timestamps, coordinate system descriptions, data format versions, cartographer names, and reviewing units. Simultaneously, the unit integrates a lightweight natural language processing model to identify descriptive and procedural auxiliary text paragraphs in the results report PDF, such as "This report was compiled by XX unit" and "Field survey completed in XXXX year XX month." The stripping operation is performed non-destructively; the system extracts all identified metadata and auxiliary text information and stores it in a temporary log area, retaining only the core geographic information entities, attribute data, and spatial relationship data that directly affect security classification in the memory workspace, thus constituting the so-called legitimate survey results. Here, "legitimate" refers to a set of core data that has undergone information purification and is suitable for subsequent security level verification. For example, in a Shapefile containing land use status patches for a certain area, the spatial coordinates, land use codes, and area fields are retained, while the file creation time and projection parameter description text are removed. After completing the information analysis, the survey results reading and analysis unit uses the MD5 hash algorithm to perform a one-way hash calculation on the legitimate survey results in the memory working area. The calculation process first involves standardizing all spatial coordinate sequences and attribute key-value pairs according to predefined rules, such as unifying coordinate precision to six decimal places. The attribute fields are then serialized into a byte stream in alphabetical order. An MD5 operation is then performed on this byte stream to generate a 128-bit, 32-hexadecimal-character MD5 hash value. This hash value serves as the unique digital fingerprint of this core survey result data.

[0028] The core task of the MD5 storage verification unit is to implement high-speed cache lookup. This unit maintains an in-memory hash table data structure called the MD5 storage verification mapping pair. Each entry in this hash table is a key-value pair, where the key is an MD5 hash value and the value is a composite data structure containing at least two fields: a storage verification index and a valid period index. The storage verification index is an enumerated value, such as passed, requiring manual review, or prohibited from storage due to confidentiality, representing the final verification conclusion of the data content corresponding to the MD5 value in history. The valid period index is an integer value, with its initial value set by the system, for example, 100, representing that the maximum valid lifespan of the cache entry is 100 system operation cycles. After receiving the MD5 hash value from the investigation results reading and analysis unit, the MD5 storage verification unit immediately performs an exact match query in the MD5 storage verification mapping pair hash table. The time complexity of the query algorithm is O(1). If the query is successful, meaning a key-value pair is found whose key is exactly equal to the input MD5 hash value, the MD5 storage verification unit will perform two atomic operations: First, the storage verification index in the key-value pair is directly used as the final storage verification result of the entire uploaded survey results data packet, and returned to the upstream caller or written to the result queue. This process is usually completed within milliseconds. Second, the valid period index in the key-value pair is reset to the system's preset maximum valid period T_max, for example, reset to 100, to refresh its cache validity. This mechanism ensures that the effective cache of frequently accessed data can reside for a long time, accelerating the processing of hot data.

[0029] If the MD5 storage verification unit does not find a matching key-value pair in the MD5 storage verification mapping relationship, the process transfers to the survey results storage verification unit. This unit is responsible for executing more complex and time-consuming verification logic. First, this unit matches the legitimate survey results with a pre-set encrypted information survey results table. This table is a relational table stored in a secure database, where each record represents a known sensitive geographic entity or pattern that requires close attention, such as a specific proportion of military restricted area boundaries, undisclosed important infrastructure coordinates, or the outline of a specific type of mineral resource distribution area. The matching process combines spatial overlay analysis with attribute joint query. The system overlays the spatial layers in the legitimate survey results with the spatial layers in the encrypted information survey results table, calculating spatial relationships such as intersection and containment; simultaneously, it performs keyword matching and semantic similarity calculation on attribute fields. The matching engine outputs a list of matching results. If the list is empty, it indicates that the current legitimate survey results do not violate any known encrypted information red lines, and the system automatically determines that the survey results uploaded by the land and resources survey entity are qualified, generates a passed storage verification result, and allows it to enter the next stage of standardization processing and permanent storage process. If the matching result list is not empty, meaning one or more elements in the legitimate investigation results are identified as potentially related to records in the encrypted information investigation results table, the investigation results storage verification unit will pause the automated process and instead generate a storage verification request task, pushing it to the dedicated administrator's workbench interface. This request includes details of the matched encrypted information entries, the corresponding legitimate investigation result fragments displayed in an anonymized or access-controlled manner, and contextual information. The administrator, typically an expert with the appropriate security clearance, reviews the request on the workbench, can access more detailed background information, and ultimately makes a manual judgment to approve, require modification and re-review, or prohibit storage, or initiates a semi-automated auxiliary verification tool for in-depth analysis. The investigation results storage verification unit receives the administrator's final judgment and uses it as the storage verification result for this uploaded investigation result.

[0030] Regardless of whether the storage verification result generated by the investigation result storage verification unit is automatically passed or based on the administrator's judgment, as long as the result originates from the matching verification of legitimate investigation results and the encrypted information table (i.e., it does not use the MD5 cache shortcut), the system will initiate the MD5 storage verification mapping relationship processing unit to record and potentially accumulate knowledge. This unit first operates on the log table. The log table is a persistent relational table, and each log entry contains three core fields: MD5 value (string type), index, storage verification index enumeration type, and storage verification frequency (integer type). The processing logic is as follows: The unit uses the MD5 hash value of the current legitimate investigation result as the query condition to search the log table for a record with an MD5 value field equal to it. If it does not exist, an insertion operation is performed to add a new log record, setting its MD5 value field to the MD5 hash value, its storage verification index field to the newly generated storage verification result (e.g., passed), and its storage verification frequency field to 1. If a log record with the same MD5 value already exists, the adjustment logic is entered. The core of the adjustment logic is to compare whether the storage verification result generated this time is consistent with the storage verification index field value of the historical log entry. If the two values ​​match, it indicates that the verification conclusion based on the same data content is stable, and the system increments the storage verification frequency field value of that log entry by 1. If they do not match, it indicates that the verification conclusion has changed or diverged, which may be due to changes in the data context, verification rules, or administrator judgment criteria. The system decrements the storage verification frequency field value of that log entry by 1. Subsequently, the system immediately determines the reduced storage verification frequency value. If this value is less than the system's preset minimum threshold F_min, for example, F_min=1, it is considered that the verification history corresponding to this MD5 value is too unstable or has expired, and has no value for preservation. Therefore, the log entry is completely deleted from the log table. The storage verification frequency field is essentially a confidence counter, reflecting the degree of consistency of historical verification conclusions for specific data content.

[0031] After synchronizing the log table, the MD5 storage verification mapping pair processing unit further examines the recently updated log entry, including new entries and entries with varying frequencies. The criterion for this check is whether the storage verification frequency field value exceeds the system's preset maximum threshold F_max (e.g., F_max=10). If it does, it indicates that the investigation result corresponding to that MD5 value has been verified more than F_max times, and the high consistency frequency of historical conclusions means that the number of consistent occurrences far exceeds the number of inconsistent occurrences. This indicates that the correspondence between the MD5 value and the storage verification metric is sufficiently stable and reliable, meeting the conditions for conversion from temporary logs to permanent accelerated cache. At this point, the processing unit performs a knowledge deposition operation: first, it removes the log record from the log table; then, it constructs a new key-value pair using the MD5 value field and the storage verification metric field, initializes the valid periodicity metric to T_max, and finally inserts this new key-value pair into the MD5 storage verification mapping pair hash table. Thus, the system completes a process of learning from specific verification instances and optimizing its performance.

[0032] In addition, the MD5 storage verification mapping processing unit also runs a background scheduled task that periodically iterates through all key-value pairs in the MD5 storage verification mapping hash table, for example, every morning. During the iteration, the valid periodicity index of each key-value pair is decremented by 1. After decrementing, the index value is immediately checked. If the valid periodicity index of a key-value pair has been decremented to 0, the key-value pair is automatically deleted from the hash table. This mechanism introduces the concept of time-to-live (TTL) for cached entries, ensuring the timeliness of cached data. Cache entries that have not been hit for a long time cannot have their valid periodicity index reset and will be automatically cleaned up over time. This prevents outdated and erroneous cache conclusions from being affected by business rule updates or changes in geographical information, thus ensuring the accuracy and reliability of the system's long-term operation.

[0033] According to a second embodiment of the present invention, referring to Figure 2 This invention seeks protection for a method for storing land and resources survey results based on artificial intelligence, the specific steps of which are as follows: Step S110: Read the survey results uploaded by the land and resources survey entity, remove auxiliary information that has no impact on the classification of confidentiality through information cutting analysis, extract the legitimate survey results, and calculate the MD5 hash value of the legitimate survey results. Step S120: Query the MD5 storage verification mapping relationship pair and determine whether there is a key-value pair whose MD5 value is equal to the MD5 hash value calculated in step S110. If yes, proceed to step S130; otherwise, proceed to step S140. Step S130: The storage verification index in the matching key-value pair is directly used as the final storage verification result of the survey results uploaded by the land and resources survey subject, and the legal period index in the key-value pair is reset to the maximum legal period T_max; Step S140: Match the legitimate survey results with the preset encrypted information survey results table; if no encrypted information is matched, the survey results are deemed qualified and the storage verification result is passed; if encrypted information is matched, the administrator's storage verification request is received, and a deep storage verification is performed on the legitimate survey results based on the request to generate a storage verification result. Step S150: Based on the generated storage verification result, update the log table and query whether there is a log in the log table whose MD5 value is equal to the current MD5 hash value. If it does not exist, add it. If it exists, increase or decrease its storage verification frequency according to whether the storage verification result is consistent, and delete the log when the frequency is lower than the minimum value F_min. Step S160: Check the updated logs. If the storage verification frequency of a certain log exceeds the preset maximum threshold F_max, remove the log from the log table and add its MD5 value and storage verification index as a new key-value pair to the MD5 storage verification mapping relationship pair. Step S170: Periodically decrement the valid periodicity index of all key-value pairs in the MD5 storage verification mapping relationship by 1, and delete the key-value pairs whose valid periodicity index has been decremented to 0 from the mapping relationship.

[0034] Furthermore, the information analysis in step S110 specifically includes: The uploaded survey results are structured and parsed to identify and extract the metadata, which includes the survey task initiation time, data processing log timestamps, and auxiliary text information for format description. The stripping operation is based on a preset metadata tag library and natural language processing model to ensure that only core geographic information entities, attribute data, and spatial relationship data that directly affect the determination of data confidentiality level are retained, thus constituting the legitimate survey results.

[0035] Furthermore, it also includes: In step S130, each key-value pair in the MD5 storage verification mapping relationship also includes a valid periodicity index; The legal cycle indicator is an integer value that decreases over time or system operating cycle, and its initial value is set by the system.

[0036] Furthermore, the specific logic for updating the log table in step S150 is as follows: Check if there is a log in the log table whose MD5 value is equal to the current MD5 hash value. If not, add a new log, set its MD5 value field to the current MD5 hash value, set the storage verification index field to the storage verification result generated this time, and initialize the storage verification frequency field to 1. If it exists, compare the storage verification result generated this time with the storage verification indicator field value of the existing log. If they match, increment the storage verification frequency field value of the log entry by 1; if they do not match, decrement the storage verification frequency field value of the log entry by 1. Determine whether the decremented storage verification frequency field value is less than the preset minimum threshold F_min; if it is less than F_min, delete the log entry from the log table.

[0037] Furthermore, it also includes: In step S170, the valid periodicity index of all key-value pairs in the MD5 storage verification mapping relationship is periodically decremented by 1, and key-value pairs whose valid periodicity index has been decremented to 0 are deleted from the mapping relationship.

[0038] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

[0039] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0040] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0041] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A land and resources survey results storage system based on artificial intelligence, characterized in that, The system includes the following components: The survey results reading and analysis unit is used to read the survey results uploaded by the land and resources survey entity, perform information-cutting analysis on the survey results to remove survey time periods and result information that have no impact on the classification of confidentiality, select the core legitimate survey results, and use the MD5 hash algorithm to calculate the MD5 hash value of the legitimate survey results. The MD5 storage verification unit is used to query the MD5 storage verification mapping relationship pair to confirm whether there is a key-value pair therein, and the MD5 value contained in the key-value pair is equal to the MD5 hash value of the legitimate investigation result. If it is confirmed to exist, the storage verification index in the key-value pair will be directly used as the final storage verification result of the survey results uploaded by the land and resources survey subject. The survey results storage verification unit is used to initiate a verification process based on the encrypted information survey results table when the MD5 storage verification unit confirms that there is no matching key-value pair in the MD5 storage verification mapping relationship, and to match the legitimate survey results with the encrypted information in the encrypted information survey results table; If no encrypted information is found in the legitimate survey results, the survey results uploaded by the land and resources survey entity are deemed qualified, and the storage verification result is passed. If the legitimate survey results match encrypted information, a storage verification request from the administrator is received, and a manual or semi-automatic deep storage verification is performed on the legitimate survey results that match the encrypted information based on the storage verification request, generating a storage verification result of the survey results uploaded by the land and resources survey subject. The MD5 storage verification mapping relationship processing unit is used to perform a synchronization operation on the logs in the log table based on the generated storage verification result after the investigation result storage verification unit completes the storage verification of the legitimate investigation results that match the encrypted information. Each log entry in the log table contains three fields: MD5 value, storage verification index, and storage verification frequency. The MD5 storage verification mapping relationship processing unit is further used to determine whether the storage verification frequency of the synchronized log exceeds the preset maximum threshold. If it does, the log entry is deleted from the log table, and its MD5 value and storage verification index are inserted as a new key-value pair into the MD5 storage verification mapping relationship pair to realize the accumulation and synchronization of verification knowledge.

2. The artificial intelligence-based land and resources survey results storage system according to claim 1, characterized in that, Also includes: The information analysis performed by the survey results reading and analysis unit specifically includes: The uploaded survey results are structured and parsed to identify and extract the metadata, which includes the survey task initiation time, data processing log timestamps, and auxiliary text information for format description. The stripping operation is based on a preset metadata tag library and natural language processing model to ensure that only core geographic information entities, attribute data, and spatial relationship data that directly affect the determination of data confidentiality level are retained, thus constituting the legitimate survey results.

3. The artificial intelligence-based land and resources survey results storage system according to claim 1, characterized in that, Also includes: Each key-value pair in the MD5 storage verification mapping relationship also includes a valid period index; the valid period index is an integer value that decreases over time or system operation cycle, and the initial value is set by the system. While confirming the existence of a matching key-value pair and directly returning the storage verification index, the MD5 storage verification unit performs the operation of resetting the legal period index in the key-value pair to the preset maximum legal period T_max. The MD5 storage verification mapping relationship processing unit also periodically traverses all key-value pairs and decrements their valid period index by 1. When it detects that the valid period index of a certain key-value pair has decreased to 0, it automatically deletes the key-value pair from the MD5 storage verification mapping relationship.

4. The artificial intelligence-based land and resources survey results storage system according to claim 1, characterized in that, Also includes: The MD5 storage verification mapping relationship enables the processing unit to perform the following logic for synchronizing the log table: Check if a log exists in the log table, and if its MD5 value field is equal to the MD5 hash value of the legitimate investigation result that matches the encrypted information. If it does not exist, a new log will be created, with the MD5 value field set to the MD5 hash value, the storage verification index field set to the storage verification result generated this time, and the storage verification frequency field initialized to 1; if it exists, the adjustment logic will be entered.

5. The artificial intelligence-based land and resources survey results storage system according to claim 4, characterized in that, Also includes: Compare the generated storage verification result with the storage verification indicator field value of the existing log entry to see if they are consistent. If they match, increment the value of the storage verification frequency field of that log entry by 1; if they do not match, decrement the value of the storage verification frequency field of that log entry by 1. Determine whether the reduced storage verification frequency field value is less than the preset minimum threshold F_min; if it is less than F_min, delete the log from the log table; the storage verification frequency field reflects the consistency and stability of historical storage verification conclusions based on the same MD5 value.

6. A method for storing land and resources survey results based on artificial intelligence, characterized in that, The specific steps of this method are as follows: Step S110: Read the survey results uploaded by the land and resources survey entity, remove auxiliary information that has no impact on the classification of confidentiality through information cutting analysis, extract the legitimate survey results, and calculate the MD5 hash value of the legitimate survey results. Step S120: Query the MD5 storage verification mapping relationship pair and determine whether there is a key-value pair whose MD5 value is equal to the MD5 hash value calculated in step S110. If yes, proceed to step S130; otherwise, proceed to step S140. Step S130: The storage verification index in the matching key-value pair is directly used as the final storage verification result of the survey results uploaded by the land and resources survey subject, and the legal period index in the key-value pair is reset to the maximum legal period T_max; Step S140: Match the legitimate survey results with the preset encrypted information survey results table; if no encrypted information is matched, the survey results are deemed qualified and the storage verification result is passed; if encrypted information is matched, the administrator's storage verification request is received, and a deep storage verification is performed on the legitimate survey results based on the request to generate a storage verification result. Step S150: Based on the generated storage verification result, update the log table and query whether there is a log in the log table whose MD5 value is equal to the current MD5 hash value. If it does not exist, add it. If it exists, increase or decrease its storage verification frequency according to whether the storage verification result is consistent, and delete the log when the frequency is lower than the minimum value F_min. Step S160: Check the updated logs. If the storage verification frequency of a certain log exceeds the preset maximum threshold F_max, remove the log from the log table and add its MD5 value and storage verification index as a new key-value pair to the MD5 storage verification mapping relationship pair. Step S170: Periodically decrement the valid periodicity index of all key-value pairs in the MD5 storage verification mapping relationship by 1, and delete the key-value pairs whose valid periodicity index has been decremented to 0 from the mapping relationship.

7. The method for storing land and resources survey results based on artificial intelligence according to claim 6, characterized in that, The information analysis in step S110 specifically includes: The uploaded survey results are structured and parsed to identify and extract the metadata, which includes the survey task initiation time, data processing log timestamps, and auxiliary text information for format description. The stripping operation is based on a preset metadata tag library and natural language processing model to ensure that only core geographic information entities, attribute data, and spatial relationship data that directly affect the determination of data confidentiality level are retained, thus constituting the legitimate survey results.

8. The method for storing land and resources survey results based on artificial intelligence according to claim 6, characterized in that, Also includes: In step S130, each key-value pair in the MD5 storage verification mapping relationship also includes a valid periodicity index; The legal cycle indicator is an integer value that decreases over time or system operating cycle, and its initial value is set by the system.

9. The method for storing land and resources survey results based on artificial intelligence according to claim 6, characterized in that, The specific logic for updating the log table in step S150 is as follows: Check if there is a log in the log table whose MD5 value is equal to the current MD5 hash value. If not, add a new log, set its MD5 value field to the current MD5 hash value, set the storage verification index field to the storage verification result generated this time, and initialize the storage verification frequency field to 1. If it exists, compare the storage verification result generated this time with the storage verification indicator field value of the existing log. If they match, increment the storage verification frequency field value of the log entry by 1; if they do not match, decrement the storage verification frequency field value of the log entry by 1. Determine whether the decremented storage verification frequency field value is less than the preset minimum threshold F_min; if it is less than F_min, delete the log entry from the log table.

10. The method for storing land and resources survey results based on artificial intelligence according to claim 6, characterized in that, Also includes: In step S170, the valid periodicity index of all key-value pairs in the MD5 storage verification mapping relationship is periodically decremented by 1, and key-value pairs whose valid periodicity index has been decremented to 0 are deleted from the mapping relationship.

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