File digital quality detection method based on real estate

Abstract

The invention discloses an archive digital quality detection method based on real estate, and particularly relates to the field of digital quality detection.The dust accumulation degree of a real estate archive storage environment is judged by analyzing electrostatic accumulation of the real estate archive storage environment; according to the dust accumulation degree, judging whether the archive meets a digital condition or not; the completeness of the structured information is evaluated by analyzing the complex structure of the real estate file; the loss risk of the fine-grained information is evaluated by analyzing context information and implicit semantics of the archive; by comprehensively analyzing the dust accumulation degree of the real estate file storage environment, the integrity of the structured information and the loss risk of the fine-grained information, the quality of the real estate digital file is detected, and the integrity and reliability of the real estate digital file are improved.

Description

Technical Field

[0001] The present invention relates to the field of digital quality detection, and more specifically, to a digital quality detection method for archives based on real estate. Background Art

[0002] In the process of digitizing traditional paper archives, especially real estate archives, dust accumulation may cause blurred scanned images or missing information, and the influence of static electricity will aggravate the adhesion of dust particles to the surface of the archives, further affecting the effect of digitization; real estate archives are usually complex in structure and contain multi-level information. Traditional digitization methods often face the risk of information loss when retaining these structured information; at the same time, the implicit semantics and fine-grained information in the archives are also easily overlooked in the digitization process. The loss of this information will seriously affect the availability and integrity of the archives; existing technologies mainly focus on improving the resolution of digitization equipment or improving the storage environment, but do not systematically ensure the quality of archive digitization from multiple dimensions such as archive storage environment, archive structure integrity and fine-grained information. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a real estate-based digital archive quality detection method to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: The method for detecting the quality of digitalized archives based on real estate includes the following steps: Step S1: analyzing the static electricity accumulation in the real estate archive storage environment to assess the dust accumulation level in the real estate archive storage environment; Step S2: judging whether the real estate archives meet the digitization conditions according to the dust accumulation degree of the real estate archives storage environment; Step S3: Analyze the complex structure files of real estate archives and evaluate the integrity of structured information during the digitization process; Step S4: Analyze the contextual information and implicit semantics of the real estate archives to assess the risk of loss of fine-grained information during the digitization process; Step S5: Comprehensively analyze the dust accumulation level of the real estate archive storage environment, the integrity of structured information, and the risk of loss of fine-grained information, and perform quality inspection on the real estate digital archive.

[0005] In a preferred embodiment, in step S1: in order to accurately reflect the static electricity accumulation in the real estate archive storage environment, the static electricity accumulation index is calculated: ; in, is the static electricity accumulation index; is the average electrostatic field strength; is the resistivity of the archival material surface; is the relative humidity of the storage environment.

[0006] In a preferred embodiment, in step S2: a static electricity accumulation index threshold is preset, and the static electricity accumulation index is compared with the static electricity accumulation index threshold: When the static electricity accumulation index is greater than or equal to the static electricity accumulation index threshold, the archive cannot directly enter the digitization process; When the static electricity accumulation index is less than the static electricity accumulation index threshold, it indicates that the archive can directly enter the digitization process.

[0007] In a preferred embodiment, in step S3: for each archive, the structured information contained in the archive can be expressed as: ; in, It is a collection of information in real estate records; It is a real estate file; Represents a raw structured information unit in a real estate file; is the number of structured information units in the real estate file; Using hierarchical classification algorithm, the archives The text information in is parsed at different levels: ; in, It is a multi-level structure tree after parsing; It is a hierarchical classification algorithm; It is a collection of information in real estate records; The hierarchical information parsed is deeply analyzed using information graph structural analysis to identify structural points that may be lost or damaged during the digitization process. The information units in the archive are set as nodes, and the associations between information are set as edges to form an information graph. In order to evaluate whether each information unit may be lost during the digitization process, a structural loss risk function is defined: ; in, Is a node Risk of structural loss; Is a node The number of neighbor nodes; Is a node Depth in the hierarchy tree; Is a node , The distance between In order to further verify the integrity of structured information, a structural integrity evaluation function is introduced to quantify the retention of archival structured information during the digitization process. The expression is: ; in, is the structural integrity index; Is a node Risk of structural loss; Is a node The weight of .

[0008] In a preferred embodiment, in step S4: a context regeneration chain is constructed based on the context information and implicit semantics of the real estate archives. The formula for constructing the context regeneration chain is: ; in, It is a contextual regeneration chain; Represents a text fragment in a real estate file; Represents a text fragment The implicit semantic complexity of Based on the context regeneration chain, the risk of loss of fine-grained information during the digitization process is assessed: ; in, is the loss risk index of fine-grained information; Represents a text fragment in a real estate file; is the fine-grained information complexity of text fragments in real estate archives; represents the internal consistency of textual fragments in real estate archives; It is the similarity between a text segment in the real estate archive and the preceding and following text segments.

[0009] In a preferred embodiment, in step S5: the static electricity accumulation index corresponding to the dust accumulation degree of the real estate archive storage environment, the structural integrity index corresponding to the integrity of the structured information, and the loss risk index corresponding to the loss risk of the fine-grained information are normalized, and the quality index of the real estate digital archive is calculated by the processed static electricity accumulation index, structural integrity index and loss risk index: ; in, It is the quality index of the digital archive of real estate; is the loss risk index of fine-grained information; is the structural integrity index; is the static electricity accumulation index; , They are adjustment coefficients; Set the quality index threshold and compare the quality index with the quality index threshold, specifically: When the quality index is greater than or equal to the quality index threshold, it means that the digitization quality of the real estate archives has reached the predetermined standard; When the quality index is less than the quality index threshold, it means that the digitization quality of the real estate archives does not meet the standards.

[0010] Technical effects and advantages of the method of the present invention: By analyzing the static electricity accumulation in the storage environment, we can determine the dust accumulation situation and ensure that the archives are digitized in the best condition, thus avoiding imaging problems caused by dust. By evaluating the integrity of the archives' structured information and the risk of loss of fine-grained information, we can ensure that the archives' hierarchical structure and implicit information are fully preserved during the digitization process, thus avoiding the loss of key information or semantic details. By comprehensively analyzing the storage environment of real estate archives, the integrity of structured information and the risk of loss of fine-grained information, we have formed a comprehensive quality inspection mechanism that can determine the quality of archive digitization, provide a stable and reliable basis for subsequent archive management and use, effectively improve the accuracy, integrity and long-term preservation reliability of digitized archives, and provide all-round quality assurance for the archives' digitization process. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the digital quality detection method of archives based on real estate of the present invention; DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] Reference Figure 1 .

[0014] The embodiment provides a method for detecting the quality of digitalized archives based on real estate of the present invention, which comprises the following steps: Step S1: analyzing the static electricity accumulation in the real estate archive storage environment to assess the dust accumulation level in the real estate archive storage environment; Step S2: judging whether the real estate archives meet the digitization conditions according to the dust accumulation degree of the real estate archives storage environment; Step S3: Analyze the complex structure of the real estate archive and evaluate the integrity of the structured information during the digitization process; Step S4: Analyze the contextual information and implicit semantics of the real estate archives to assess the risk of loss of fine-grained information during the digitization process; Step S5: Comprehensively analyze the dust accumulation level of the real estate archive storage environment, the integrity of structured information, and the risk of loss of fine-grained information, and perform quality inspection on the real estate digital archive.

[0015] In step S1: analyzing the static electricity accumulation in the real estate archive storage environment and evaluating the degree of dust accumulation is to ensure the long-term preservation of the archives and the high-quality digitization process; in the storage environment of the real estate archives, static electricity accumulation is an important factor that causes dust particles to be adsorbed on the surface of the archive materials; static electricity accumulation attracts and attaches dust particles in the air, causing dust accumulation on the archive surface, which not only affects the physical integrity of the archive materials, but also reduces the clarity of the image and the fidelity of the information during the scanning or digitization process; by analyzing the static electricity accumulation of the archives, the risk of dust accumulation can be predicted and managed in advance, ensuring that the archives can retain their original quality during the digitization process and reducing the interference and information loss caused by dust coverage; by analyzing the static electricity accumulation in the real estate archive storage environment, the digitization readiness of the archives can be effectively judged.

[0016] Analyze the static electricity accumulation in the real estate archives storage environment and evaluate the dust accumulation level in the real estate archives storage environment, specifically: Generally speaking, the higher the static electricity accumulation, the stronger the dust adsorption capacity and the higher the dust accumulation degree. In order to accurately reflect the static electricity accumulation in the real estate archive storage environment, the static electricity accumulation index is calculated: ; in, is the static electricity accumulation index; is the average electrostatic field strength; is the resistivity of the archival material surface; is the relative humidity of the storage environment; The electrostatic field strength of each key point in the real estate archive storage environment is measured by an electrostatic field strength meter, and the average electrostatic field strength of multiple measurement points is calculated to obtain the average electrostatic field strength representing the entire real estate storage environment; the resistivity of the archive surface material is measured using a surface resistivity meter to reflect the material surface's ability to accumulate and dissipate static electricity; the relative humidity of the archive storage environment is measured using a humidity sensor to reflect the effect of humidity on static electricity dissipation.

[0017] The larger the electrostatic accumulation index, the higher the degree of dust accumulation in the real estate archives storage environment; the larger the electrostatic accumulation index, the stronger the adsorption capacity of static electricity, which is easy to attract and attach dust particles in the air, resulting in increased dust accumulation on the surface of real estate archives, thereby affecting the image clarity and information fidelity of the archives during the digitization process; a higher electrostatic accumulation index not only indicates that dust is more likely to accumulate, but may also indicate that the storage environment is not perfect for the protection of archival materials, and measures need to be taken (reducing humidity or increasing anti-static treatment) to reduce the negative impact of dust accumulation on the quality of archive preservation and ensure the smooth progress of the digitization process.

[0018] In step S2: judging whether the real estate archives meet the digitization conditions according to the dust accumulation degree of the real estate archives storage environment, specifically: The static electricity accumulation index threshold is preset. The static electricity accumulation index threshold is set according to the size of the static electricity accumulation index and the tolerance for static electricity accumulation in the digitalization process of the archives. The static electricity accumulation index is compared with the static electricity accumulation index threshold: When the electrostatic accumulation index is greater than or equal to the electrostatic accumulation index threshold, the archive cannot directly enter the digitization process, which means that there are already a large number of dust particles on the archive surface, which may have an adverse effect on the imaging quality during the digitization process, such as causing image blur or information loss; the cleanliness of the archive surface cannot meet the requirements of high-quality digitization, and additional measures need to be taken to reduce dust accumulation, such as cleaning the archive surface or improving the humidity conditions of the storage environment to reduce static electricity accumulation and ensure that the archive meets the digitization conditions; When the electrostatic accumulation index is less than the electrostatic accumulation index threshold, it indicates that the archives can directly enter the digitization process without worrying about the image quality degradation due to the influence of dust particles; it means that the electrostatic accumulation in the storage environment is low, and the surface of the archives has not reached the critical value for easily absorbing a large number of dust particles. The accumulation of dust particles will not affect the digitization quality of the archives.

[0019] In step S3: In the process of digitizing real estate archives, complex real estate archives usually contain multi-level and multi-dimensional information, which is often highly structured and has complex relationships between them. If these structured information are not correctly preserved during the digitization process, the use value of the archives may decrease. Therefore, by analyzing the complex structure of the real estate archives, potential points of loss of structured information are identified to ensure the integrity of the structured information.

[0020] Analyze the complex structure of real estate archives and evaluate the integrity of structured information during the digitization process, specifically: The hierarchical classification algorithm is used to identify and parse the structured information of real estate archives. By scanning the entire archive, different information levels in the archive are identified, such as legal terms, contract terms, engineering drawings, etc., and the text information in the archive is classified according to the hierarchical structure: For each file, the structured information contained in the file can be expressed as: ; in, It is a collection of information in real estate records; It is a real estate file; Represents the original structured information unit in the real estate file, such as legal terms, contract terms, engineering drawings; It is the number of structured information units in the real estate file.

[0021] Using hierarchical classification algorithm, the archives The text information in is parsed at different levels: ; in, It is a multi-level structure tree after parsing, which represents the result of stratifying the information structure inside the archive through the algorithm; It is a hierarchical classification algorithm used to classify information into layers, identify different information units in archives and classify them; It is a collection of information in real estate files.

[0022] By identifying different subject levels in the archives, the archives are divided into multiple structured information units, providing clear structural guidance for digital processing; ensuring that during the digitization process, information at different levels will not be confused and that structured information can remain clear and complete.

[0023] The hierarchical information parsed is deeply analyzed using information graph structural analysis to identify structural points that may be lost or damaged during the digitization process. The information units in the archive are set as nodes, and the associations between information are set as edges to form an information graph. In order to evaluate whether each information unit may be lost during the digitization process, a structural loss risk function is defined: ; in, Is a node Risk of structural loss; Is a node The number of neighbor nodes indicates the degree of association with other nodes; Is a node Depth in the hierarchy tree; Is a node , The distance between.

[0024] By analyzing the infographic, we can identify key information units that may be lost or damaged during the digitization process. Identifying the risk of loss of information units with complex structures and high correlations ensures that potential risks can be foreseen before digitization and appropriate measures can be taken to reduce information loss.

[0025] In order to further verify the integrity of structured information, a structural integrity evaluation function is introduced to quantify the retention of archival structured information during the digitization process. The expression is: ; in, is the structural integrity index; Is a node Risk of structural loss; Is a node The weight of .

[0026] The larger the structural integrity index, the more complete the structured information of the real estate archives is during the digitization process; the structural integrity index reflects whether the correlation and hierarchical relationship between different information units in the archives remain intact; a larger structural integrity index indicates that the digitization process has not destroyed the structural information of the original archives, the hierarchy, correlation, context, etc. of the information have not been lost or changed, the relationship between information units has been well preserved, and the multi-level structure in the archives, such as the logical relationship between legal provisions and contract terms, the dependency relationship between drawings and explanatory texts, etc., are clearly visible; the availability and accuracy of the archives are guaranteed, and subsequent retrieval, analysis and use are more reliable; it is of great significance to ensure the legal effect of the archives, accurate description and effective transmission of key information.

[0027] In step S4: In the process of digitizing real estate archives, contextual information and implicit semantics in the archives are key parts to ensure that the archive content is still accurately expressed after conversion; the legal documents, technical agreements or other documents with complex structures contained in real estate archives rely on context to convey complete meaning; contextual information is conducive to understanding the relationship between archive text fragments, and implicit semantics represent the deep meaning that is not explicitly expressed in the text fragments but can be deduced through the context; if this information is not fully analyzed and protected during the digitization process, many fine-grained information in the archives (implied meaning of terms, specific semantic associations, etc.) may be lost, affecting the availability and accuracy of the digitized archives; by analyzing contextual information and implicit semantics and evaluating the risk of loss of fine-grained information, it can help identify potential risks that may lead to information loss and take necessary measures to maintain the integrity of information during the digitization process.

[0028] Analyze the contextual information and implicit semantics of real estate archives and assess the risk of loss of fine-grained information during the digitization process, specifically: In real estate archives, contextual information refers to the context of each fragment in the text and its relevance to other fragments. For example, a clause in a contract may be fully interpreted through the relationship between the previous and subsequent clauses, and analyzing a clause alone cannot accurately reflect the full meaning. Implicit semantics refers to the meaning that is not clearly expressed in the text but can be inferred through the contextual relationship. For example, in legal documents, the meaning of certain terms needs to be interpreted through references to legal clauses or other documents.

[0029] According to the context information and implicit semantics of real estate archives, a context regeneration chain is constructed to connect text fragments with the context before and after to form a dynamic context structure, ensuring that each fragment can be fully understood in the context during the digitization process. The formula for constructing the context regeneration chain is: ; in, It is a contextual regeneration chain, which is used to reconstruct the position of text fragments in the context and the chain structure of implicit semantics; Represents a text fragment in a real estate file; Represents a text fragment The implicit semantic complexity of .

[0030] Combine deep learning technology, language models and other methods to capture the deep semantics in text fragments; quantify the implicit semantic complexity of text fragments by analyzing multiple factors such as the fragment's dependency, ambiguity, contextual dependence, etc.

[0031] By building a context regeneration chain, we can effectively track the position and association of each text fragment in the context, ensure that implicit semantics are not missed during the digitization process, protect text fragments at a fine-grained level, and avoid semantic deviation or information loss due to being out of context; based on the context regeneration chain, we can evaluate the risk of loss of fine-grained information during the digitization process: ; in, is the loss risk index of fine-grained information; Represents a text fragment in a real estate file; is the fine-grained information complexity of text fragments in real estate archives; represents the internal consistency of textual fragments in real estate archives; It is the similarity between a text segment in the real estate archive and the preceding and following text segments.

[0032] Semantic hierarchical models, vocabulary embedding and dependency parsing are used to quantify the detailed information within the fragment to obtain the fine-grained information complexity of the text fragments in the real estate archives; the consistency within the fragment is evaluated through topic models and vocabulary coherence; and the semantic similarity between the fragment and the context is calculated based on deep learning models such as cosine similarity and BERT.

[0033] The larger the risk index of fine-grained information loss, the higher the risk of fine-grained information loss during the digitization process, which means that some key details, implicit semantics or deep contextual information in the archives may not be fully retained or correctly converted during the digitization process; fine-grained information usually includes subtle differences or term definitions that are difficult to detect, and plays a key role in the understanding and application of file content; the loss of fine-grained information during the digitization process will not only affect the integrity of the archives, but may also lead to information bias or misunderstanding in subsequent use.

[0034] In step S5: the dust accumulation degree of the real estate archive storage environment, the integrity of the structured information and the risk of loss of fine-grained information are comprehensively analyzed, and the quality of the real estate digital archive is tested, specifically: The static electricity accumulation index corresponding to the dust accumulation degree of the real estate archive storage environment, the structural integrity index corresponding to the integrity of the structured information, and the loss risk index corresponding to the loss risk of fine-grained information are normalized. The quality index of the real estate digital archive is calculated through the processed static electricity accumulation index, structural integrity index and loss risk index: ; in, It is the quality index of the digital archive of real estate; is the loss risk index of fine-grained information; is the structural integrity index; is the static electricity accumulation index; , They are adjustment coefficients used to balance the impact of static electricity accumulation and fine-grained information loss on archive quality, which are set based on historical data or expert experience.

[0035] Set the quality index threshold. The quality index threshold is used to calibrate the minimum quality requirements of digital archives. Different quality index thresholds can be set according to different actual application scenarios and user needs. The quality index is compared with the quality index threshold. Specifically: When the quality index is greater than or equal to the quality index threshold, it means that the digitization quality of the real estate archives meets the predetermined standards; the structured information and fine-grained information of the archives are not lost during the digitization process, and the influence of environmental factors such as dust accumulation is also within an acceptable range, which means that the real estate archives have passed the quality inspection and meet the requirements of digital archiving and future use, and do not require additional repair or adjustment.

[0036] When the quality index is less than the quality index threshold, it means that the digitization quality of the real estate archives does not meet the standard, there is information loss or other problems, which may mean that the structured information is not fully retained during the digitization process, resulting in the loss of the logical structure or semantic information of the document; fine-grained information (such as implicit semantics or terms) may also be omitted during the conversion process, affecting the overall readability and accuracy of the archives; at the same time, the dust accumulation in the real estate archives storage environment may exceed the acceptable range, further reducing the image quality or information clarity of the archives; the digitization process or storage environment needs to be further optimized to ensure the integrity and reliability of the archives.

[0037] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0038] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0039] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0040] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0041] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0042] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0043] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0044] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0045] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting the quality of digitalized archives based on real estate, characterized in that: The following steps are involved: Step S1: analyzing the static electricity accumulation in the real estate archive storage environment to assess the dust accumulation level in the real estate archive storage environment; Step S2: judging whether the real estate archives meet the digitization conditions according to the dust accumulation degree of the real estate archives storage environment; Step S3: Analyze the complex structure of the real estate archive and evaluate the integrity of the structured information during the digitization process; Step S4: Analyze the contextual information and implicit semantics of the real estate archives to assess the risk of loss of fine-grained information during the digitization process; Step S5: Comprehensively analyze the dust accumulation level of the real estate archive storage environment, the integrity of structured information, and the risk of loss of fine-grained information, and perform quality inspection on the real estate digital archive.

2. A method for detecting the quality of digitalized archives based on real estate according to claim 1, characterized in that: In step S1: In order to accurately reflect the static electricity accumulation in the real estate archive storage environment, the static electricity accumulation index is calculated: ; in, is the static electricity accumulation index; is the average electrostatic field strength; is the resistivity of the archival material surface; is the relative humidity of the storage environment.

3. A method for detecting the quality of digitalized archives based on real estate according to claim 2, characterized in that: In step S2: The static electricity accumulation index threshold is preset, and the static electricity accumulation index is compared with the static electricity accumulation index threshold: When the static electricity accumulation index is greater than or equal to the static electricity accumulation index threshold, the archive cannot directly enter the digitization process; When the static electricity accumulation index is less than the static electricity accumulation index threshold, it indicates that the archive can directly enter the digitization process.

4. A method for detecting the quality of digitalized archives based on real estate according to claim 3, characterized in that: In step S3: For each file, the structured information contained in the file can be expressed as: ； in, It is a collection of information in real estate records; It is a real estate file; Represents a raw structured information unit in a real estate file; is the number of structured information units in the real estate file; Using hierarchical classification algorithm, the archives The text information in is parsed at different levels: ; in, It is a multi-level structure tree after parsing; It is a hierarchical classification algorithm; It is a collection of information in real estate records; The hierarchical information parsed is deeply analyzed using information graph structural analysis to identify structural points that may be lost or damaged during the digitization process. The information units in the archive are set as nodes, and the associations between information are set as edges to form an information graph. In order to evaluate whether each information unit may be lost during the digitization process, a structural loss risk function is defined: ； in, Is a node Risk of structural loss; Is a node The number of neighbor nodes; Is a node Depth in the hierarchy tree; Is a node , The distance between In order to further verify the integrity of structured information, a structural integrity evaluation function is introduced to quantify the retention of archival structured information during the digitization process. The expression is: ； in, is the structural integrity index; Is a node Risk of structural loss; Is a node The weight of .

5. A method for detecting the quality of digitalized archives based on real estate according to claim 4, characterized in that: In step S4: According to the context information and implicit semantics of real estate archives, a context regeneration chain is constructed. The formula for constructing the context regeneration chain is: ； in, It is a contextual regeneration chain; Represents a text fragment in a real estate file; Represents a text fragment The implicit semantic complexity of Based on the context regeneration chain, the risk of loss of fine-grained information during the digitization process is assessed: ； in, is the loss risk index of fine-grained information; Represents a text fragment in a real estate file; is the fine-grained information complexity of text fragments in real estate archives; represents the internal consistency of textual fragments in real estate archives; It is the similarity between a text segment in the real estate archive and the preceding and following text segments.

6. A method for detecting the quality of digitalized archives based on real estate according to claim 5, characterized in that: In step S5: The static electricity accumulation index corresponding to the dust accumulation degree of the real estate archive storage environment, the structural integrity index corresponding to the integrity of the structured information, and the loss risk index corresponding to the loss risk of fine-grained information are normalized. The quality index of the real estate digital archive is calculated through the processed static electricity accumulation index, structural integrity index and loss risk index: ; in, It is the quality index of the digital archive of real estate; is the loss risk index of fine-grained information; is the structural integrity index; is the static electricity accumulation index; , They are adjustment coefficients; Set the quality index threshold and compare the quality index with the quality index threshold, specifically: When the quality index is greater than or equal to the quality index threshold, it means that the digitization quality of the real estate archives has reached the predetermined standard; When the quality index is less than the quality index threshold, it means that the digitization quality of the real estate archives does not meet the standards.

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