A method and system for processing electronic signatures of provident fund business process data

Abstract

The present invention relates to the field of data processing technology, and discloses an electronic signature processing method and system for provident fund business process data. The method comprises the following steps: obtaining a target provident fund contract in PDF format; parsing the target provident fund contract, and respectively determining the most favorable data of each contracting party in the target provident fund contract and the compensation data of the target provident fund contract; extracting the most favorable data and the compensation data in the target provident fund contract to form a sharded contract, and calculating a standard hash value of the sharded contract; and repeatedly executing the following steps: performing a first tampering on the most favorable data in the sharded contract within a first preset range; performing a second tampering on the compensation data in the sharded contract within a second preset range; obtaining a plurality of tampered sharded contracts based on the first tampering and the second tampering, and obtaining a tampered hash value of each tampered sharded contract; and obtaining the tampering risk of the target provident fund contract based on a collision result between the standard hash value and any tampered hash value.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to a method and system for processing provident fund business process data using an electronic signature. Background Art

[0002] With the exponential increase in computing power, traditional electronic signature systems built on the MD5 hash algorithm face the risk of brute force attacks. This means that provident fund business process documents with different key data may generate the same MD5 hash value. Although existing electronic signature systems have been upgraded with new hash algorithms, legacy provident fund business process documents based on the MD5 algorithm still face the potential risk of being tampered with or having been tampered with. Therefore, it is necessary to identify provident fund business process documents at risk of tampering and transfer them to manual processing for further processing. However, how to systematically identify tampering risks in provident fund business process documents is a problem in the existing technology. Summary of the Invention

[0003] The present invention provides a method and system for processing electronic signatures of provident fund business process data, which solve the technical problems raised in the background technology.

[0004] In a first aspect, the present invention provides a method for processing electronic signatures of provident fund business process data, comprising:

[0005] Step 1: Obtain the target provident fund contract in PDF format;

[0006] Step 2: parse the target provident fund contract to determine the most favorable data for each contracting party in the target provident fund contract and the compensation data for the target provident fund contract;

[0007] Step 3: Extract the most favorable data and compensation data from the target provident fund contract to form a shard contract, and calculate the standard hash value of the shard contract;

[0008] Step 4: Repeat the following steps:

[0009] Perform the first tampering on the most beneficial data in the sharding contract within the first preset range;

[0010] Performing a second tampering on the compensation data in the sharding contract within a second preset range;

[0011] Based on the first tampering and the second tampering, several tampered shard contracts are obtained, and the tampered hash value of each tampered shard contract is obtained;

[0012] Step 5: Based on the collision result between the standard hash value and any tampered hash value, the tampering risk of the target provident fund contract is obtained.

[0013] Furthermore, the most favorable data for the contracting parties is determined, including:

[0014] Obtain N data in the target provident fund contract and obtain the dependency relationship of the N data; wherein the dependency relationship means that if the i-th data is modified, the j-th data must be modified accordingly to maintain the correct logical relationship of the data in the target provident fund contract, then there is a dependency relationship between the i-th data and the j-th data;

[0015] Tamper with each character of the i-th data, and based on the dependency relationship, tamper with the data that has a dependency relationship with the i-th data accordingly to obtain a tampering contract;

[0016] Calculate the gain amount of each contracting party in the target provident fund contract based on the tampered contract; where the gain amount represents the difference between the payment amount of the tampered contract and the payment amount of the target provident fund contract for any contracting party;

[0017] Calculate the most favorable number for each contract subject for each data separately. The calculation formula is as follows:

[0018] ;

[0019] in, Indicates the most favorable number of the i-th data to the s-th contract subject, Indicates that each character of the i-th data is tampered with, and the total number of tampered characters is obtained based on the dependency relationship. Indicates the number of data that has a dependency relationship with the i-th data, Indicates the the amount of gain for each contracting party;

[0020] Based on the best value between the sth contract subject and N data, the data with the largest best value between the sth contract subject is selected as the best data of the sth contract subject to obtain the best data of each contract subject.

[0021] Furthermore, the compensation data of the target provident fund contract is determined; wherein the compensation data represents the visual signature and visual seal in the target provident fund contract.

[0022] Furthermore, a sharding contract is formed, including the first extraction, the second extraction and data fusion:

[0023] The first extraction includes: extracting the most favorable data of the target provident fund contract, wherein the extraction includes: obtaining the first extracted data by locating the most favorable data in a visualization area of ​​the target provident fund contract;

[0024] The second extraction includes: extracting compensation data from the target provident fund contract, the extraction including: determining the permitted writing position and permitted stamping position of the visual signature and the visual seal in the target provident fund contract based on manual experience; extracting the permitted writing position in the visual area of ​​the target provident fund contract; extracting the permitted stamping position in the visual area of ​​the target provident fund contract, to obtain second extracted data;

[0025] The data fusion includes: initially generating a blank page in PDF format, the blank page having the same size as the target provident fund contract, fitting the first extracted data and the second extracted data to the blank page, and obtaining a fragmented contract.

[0026] Furthermore, the first preset range includes: obtaining the maximum and minimum values ​​of each most favorable data in the target provident fund contract in the historical time period, and using the maximum and minimum values ​​as the maximum and minimum values ​​of the first preset range of the corresponding most favorable data.

[0027] Furthermore, the second preset range includes:

[0028] The constraint conditions for establishing the second preset range based on the visual signature and the visual seal include:

[0029] First constraint: the rewritten area of ​​the visual signature and visual seal must be greater than or equal to a preset area threshold;

[0030] The second constraint is that the position of the visual signature does not exceed the allowed writing position;

[0031] The third constraint: the position of the visual seal does not exceed the allowed stamping position;

[0032] A second preset range is obtained based on the combined constraints of the first constraint, the second constraint, and the third constraint.

[0033] Furthermore, based on the first tampering and the second tampering, several tampered shard contracts are obtained, including performing the following steps on each contract subject:

[0034] Perform the first tampering: obtain the data accuracy of the most favorable data of the s-th contract subject, and based on the maximum value of the s-th contract subject in the first preset range, perform gradient descent according to the minimum unit of data accuracy until the minimum value of the s-th contract subject in the first preset range is reached or the standard hash value and the tampered hash value collide successfully;

[0035] Perform the second tampering: traverse the visual signature and the visual seal in the second preset range with pixel-level accuracy to obtain K tampering compensation schemes;

[0036] In gradient order, for each gradient of the first tampering, K tampering compensation schemes are matched.

[0037] Furthermore, both the standard hash value and the tampered hash value are calculated based on the MD5 hash algorithm.

[0038] Furthermore, the tampering risk of the target provident fund contract is calculated based on the following formula:

[0039] ;

[0040] in, Indicates the tampering risk of the target provident fund contract, Indicates the number of contracting parties of the target provident fund contract, express The index of Indicates the The value of the most favorable data corresponding to the successful collision of the contract subjects, Indicates the The value of the most favorable data corresponding to each contract subject in the target provident fund contract, Represents the absolute value operation.

[0041] In a second aspect, a system for processing provident fund business process data using electronic signatures is provided, which is applied to the method described above and includes:

[0042] Data collection module, used to obtain the target provident fund contract in PDF format;

[0043] A data parsing module is used to parse the target provident fund contract and determine the most favorable data of each contracting party in the target provident fund contract and the compensation data of the target provident fund contract;

[0044] The contract sharding module is used to extract the most favorable data and compensation data from the target provident fund contract, form a sharded contract, and calculate the standard hash value of the sharded contract;

[0045] The hash collision module is used to repeatedly perform the following steps:

[0046] Perform the first tampering on the most beneficial data in the sharding contract within the first preset range;

[0047] Performing a second tampering on the compensation data in the sharding contract within a second preset range;

[0048] Based on the first tampering and the second tampering, several tampered shard contracts are obtained, and the tampered hash value of each tampered shard contract is obtained;

[0049] The risk quantification module is used to obtain the tampering risk of the target provident fund contract based on the collision result between the standard hash value and any tampered hash value.

[0050] The beneficial effects of the present invention are:

[0051] 1. The present invention implements controlled tampering of the most favorable data and compensation data in the target provident fund contract and establishes an MD5 hash collision detection mechanism. Based on the use of a gradient and refined tampering method for the most favorable data of the contract subject and the multi-scheme traversal of the visual signature and seal at the pixel level, the potential tampering risk of the contract is effectively identified and quantified.

[0052] 2. Without changing the existing system architecture, the present invention realizes early warning and classification processing of risks of historical documents, thereby enhancing the security management and risk prevention and control capabilities of the entire business process. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of a method for processing electronic signatures for provident fund business process data according to the present invention;

[0054] Figure 2 This is a module diagram of an electronic signature processing system for provident fund business process data of the present invention. DETAILED DESCRIPTION

[0055] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. In addition, features described with respect to some examples may also be combined in other examples.

[0056] like Figures 1 and 2 As shown, a method for processing electronic signatures for provident fund business process data includes:

[0057] Step 1: Obtain the target provident fund contract in PDF format;

[0058] Step 2: parse the target provident fund contract to determine the most favorable data for each contracting party in the target provident fund contract and the compensation data for the target provident fund contract;

[0059] Step 3: Extract the most favorable data and compensation data from the target provident fund contract to form a shard contract, and calculate the standard hash value of the shard contract;

[0060] Step 4: Repeat the following steps:

[0061] Perform the first tampering on the most beneficial data in the sharding contract within the first preset range;

[0062] Performing a second tampering on the compensation data in the sharding contract within a second preset range;

[0063] Based on the first tampering and the second tampering, several tampered shard contracts are obtained, and the tampered hash value of each tampered shard contract is obtained;

[0064] Step 5: Based on the collision result between the standard hash value and any tampered hash value, the tampering risk of the target provident fund contract is obtained.

[0065] It should be noted that a typical provident fund contract includes text, signatures, and seals. The text is displayed using a fixed font, size, and predetermined position. While the signature and seal also use fixed fonts and sizes, their display positions are random within the permitted area. Therefore, if the hash value changes due to modifications to the text, the signature and seal can be randomly offset within the permitted area to compensate for the hash value change caused by the text modification, thereby achieving a hash collision.

[0066] In one embodiment of the present invention, determining the most favorable data for the contract subject includes:

[0067] Obtain N data in the target provident fund contract and obtain the dependency relationship of the N data; wherein the dependency relationship means that if the i-th data is modified, the j-th data must be modified accordingly to maintain the correct logical relationship of the data in the target provident fund contract, then there is a dependency relationship between the i-th data and the j-th data;

[0068] Tamper with each character of the i-th data, and based on the dependency relationship, tamper with the data that has a dependency relationship with the i-th data accordingly to obtain a tampering contract;

[0069] Calculate the gain amount of each contracting party in the target provident fund contract based on the tampered contract; where the gain amount represents the difference between the payment amount of the tampered contract and the payment amount of the target provident fund contract for any contracting party;

[0070] Calculate the most favorable number for each contract subject for each data separately. The calculation formula is as follows:

[0071] ;

[0072] in, Indicates the most favorable number of the i-th data to the s-th contract subject, Indicates that each character of the i-th data is tampered with, and the total number of tampered characters is obtained based on the dependency relationship. Indicates the number of data that has a dependency relationship with the i-th data, Indicates the the amount of gain for each contracting party;

[0073] Based on the best value between the sth contract subject and N data, the data with the largest best value between the sth contract subject is selected as the best data of the sth contract subject to obtain the best data of each contract subject.

[0074] Specifically, to address the exponentially increasing computational complexity associated with numerous data items in a contract, this solution relies on a brute force collision approach for risk detection. Each additional data item significantly increases computational complexity, necessitating efficient screening of data modification processes. This approach extracts all data items in a contract and establishes dependencies between them (i.e., when modifying a particular data item, the associated data items must be adjusted simultaneously to ensure the overall logical correctness of the contract). This approach allows for character-by-character controlled tampering of each data item. The resulting "gain" (i.e., the difference between the payment after the modification and the original contract payment) and the cumulative number of modified characters are calculated for each item, thereby deriving the "optimal benefit" for each item. This method effectively simulates the tamperer's desire to maximize the benefits of modification with the minimum cost. By selecting the data items with the highest "optimal benefit" as key data, it effectively reduces the overall computational burden while improving the efficiency and accuracy of detecting potential tampering risks.

[0075] In one embodiment of the present invention, compensation data of a target provident fund contract is determined; wherein the compensation data represents a visible signature and a visible seal in the target provident fund contract.

[0076] Specifically, the compensation data refers to the visual signature and seal in the provident fund contract. In a typical provident fund contract, the text content is displayed in a fixed font, size, and position. Therefore, any slight modification to the text content will result in a significant change in the hash value. While the signature and seal also use a fixed font and size, their specific position within the permitted area is random. Because of this, if the optimal data is modified, the appropriate adjustment of the signature and seal position can randomly compensate for the hash value change caused by the modification of the optimal data, thereby achieving hash value collision detection.

[0077] In one embodiment of the present invention, forming a sharding contract includes a first extraction, a second extraction, and data fusion:

[0078] The first extraction includes: extracting the most favorable data of the target provident fund contract, wherein the extraction includes: obtaining the first extracted data by locating the most favorable data in a visualization area of ​​the target provident fund contract;

[0079] The second extraction includes: extracting compensation data from the target provident fund contract, the extraction including: determining the permitted writing position and permitted stamping position of the visual signature and the visual seal in the target provident fund contract based on manual experience; extracting the permitted writing position in the visual area of ​​the target provident fund contract; extracting the permitted stamping position in the visual area of ​​the target provident fund contract, to obtain second extracted data;

[0080] The data fusion includes: initially generating a blank page in PDF format, the blank page having the same size as the target provident fund contract, fitting the first extracted data and the second extracted data to the blank page, and obtaining a fragmented contract.

[0081] It's important to note that hash value calculations exhibit a combinatorial property: for a fixed document portion (denoted as Document 0), if two document portions (denoted as Document 1 and Document 2) with the same size but different content have the same hash value, then concatenating Document 0 with either Document 1 or Document 2 at the same position (forming 1+0 or 2+0) will also produce the same hash value. This property is exploited by dividing the contract into an immutable portion and a controllable tampering portion: during brute force collision detection, the immutable portion is shielded while the hash value is calculated for the controllable tampering portion, thereby reducing the amount of computation and improving efficiency.

[0082] In one embodiment of the present invention, the first preset range includes: obtaining the maximum and minimum values ​​of each most favorable data in the target provident fund contract in the historical time period, and using the maximum and minimum values ​​as the maximum and minimum values ​​of the first preset range of the corresponding most favorable data.

[0083] Specifically, the first preset range is determined by obtaining the maximum and minimum values ​​of each "most favorable data" in the contract over a historical period. In other words, for each key data item, the highest and lowest values ​​recorded in the historical data serve as the upper and lower bounds of acceptable modification, respectively, thereby limiting the range of permissible changes allowed during the controlled manipulation process. By strictly limiting the modification range to a reasonable interval, the number of hash values ​​required to be calculated during the brute force collision process can be significantly reduced, thereby improving overall computational efficiency. This first preset range not only provides a quantitative standard for subsequent gradient-based controlled manipulation but also reduces the computational burden.

[0084] In one embodiment of the present invention, the second preset range includes:

[0085] The constraint conditions for establishing the second preset range based on the visual signature and the visual seal include:

[0086] First constraint: the rewritten area of ​​the visual signature and visual seal must be greater than or equal to a preset area threshold;

[0087] The second constraint is that the position of the visual signature does not exceed the allowed writing position;

[0088] The third constraint: the position of the visual seal does not exceed the allowed stamping position;

[0089] A second preset range is obtained based on the combined constraints of the first constraint, the second constraint, and the third constraint.

[0090] It should be noted that the second preset range is primarily used to constrain the position adjustment of the visual signature and visual seal, ensuring effective hash collision detection during controlled tampering without deviating from the actual application rules of the contract. In typical provident fund contracts, the seal is typically affixed above the signature. Therefore, when adjusting the position of the signature and seal, the overlapping area must at least reach a threshold to ensure that the adjusted contract still complies with business practices. Furthermore, the second and third constraints, respectively, ensure that the position adjustment of the signature and seal does not exceed the writing area and stamping area specified in the contract. These constraints ensure that the adjusted contract still complies with the provisions of the provident fund business process and avoid excessive adjustments that render the contract invalid or without legal effect.

[0091] In one embodiment of the present invention, a plurality of tampered shard contracts are obtained based on the first tampering and the second tampering, including performing the following steps on each contract subject:

[0092] Perform the first tampering: obtain the data accuracy of the most favorable data of the s-th contract subject, and based on the maximum value of the s-th contract subject in the first preset range, perform gradient descent according to the minimum unit of data accuracy until the minimum value of the s-th contract subject in the first preset range is reached or the standard hash value and the tampered hash value collide successfully;

[0093] Perform the second tampering: traverse the visual signature and the visual seal in the second preset range with pixel-level accuracy to obtain K tampering compensation schemes;

[0094] In gradient order, for each gradient of the first tampering, K tampering compensation schemes are matched.

[0095] Specifically, two different tampering strategies are adopted for text data and compensation data (signature and seal), and all collision situations are traversed by matching their different adjustment combinations.

[0096] First Tampering: First, the optimal data for each contracting party in the target contract is obtained and its data precision is determined (i.e., the smallest adjustable unit; for example, the minimum unit for the optimal data 20.12 is 0.01). Then, within a first preset range, the optimal data is modified using a gradient descent method, gradually decreasing from the maximum allowable value to the minimum allowable value, or until a collision is achieved. This method simulates the actual tampering strategy of a tamperer seeking to minimize changes while still achieving maximum benefit, while also reducing the computational effort. Simultaneously with the text data adjustments, the position of the signature and seal is adjusted within a second preset range to compensate for the hash value changes caused by the text tampering. Using a pixel-level traversal method (replacing the shape and size of the visual signature and seal, only their position), possible tampering compensation schemes are traversed within the permitted writing and stamping areas. After completing the first tampering (text modification) and the second tampering (signature and seal adjustment), the present invention matches each text modification scheme with K different compensation schemes in gradient order and calculates the tampered hash value.

[0097] This method not only relies on a single data adjustment to achieve hash collisions, but also combines precise gradient modification of text data with flexible adjustments to visual data (signatures and seals). This allows for even partial text tampering to be compensated for by fine-tuning the signature and seal, effectively detecting possible malicious tampering. Furthermore, within the constraints of limited computing resources, this method significantly reduces unnecessary hash calculations, improving the efficiency of tampering risk detection.

[0098] In one embodiment of the present invention, both the standard hash value and the tampered hash value are calculated based on the MD5 hash algorithm.

[0099] In one embodiment of the present invention, the tampering risk of the target provident fund contract is calculated based on the following formula:

[0100] ;

[0101] in, Indicates the tampering risk of the target provident fund contract, Indicates the number of contracting parties of the target provident fund contract, express The index of Indicates the The value of the most favorable data corresponding to the successful collision of the contract subjects, Indicates the The value of the most favorable data corresponding to each contract subject in the target provident fund contract, Represents the absolute value operation.

[0102] In one embodiment of the present invention, a formula is used to calculate the gain of the contracting parties to quantify the risk of tampering. Furthermore, if the tampering risk of the target provident fund contract is greater than or equal to a preset threshold, the target provident fund contract is transferred to manual processing.

[0103] A system for processing provident fund business process data using an electronic signature, applied to any of the methods described above, comprising:

[0104] Data collection module, used to obtain the target provident fund contract in PDF format;

[0105] A data parsing module is used to parse the target provident fund contract and determine the most favorable data of each contracting party in the target provident fund contract and the compensation data of the target provident fund contract;

[0106] The contract sharding module is used to extract the most favorable data and compensation data from the target provident fund contract, form a sharded contract, and calculate the standard hash value of the sharded contract;

[0107] The hash collision module is used to repeatedly perform the following steps:

[0108] Perform the first tampering on the most beneficial data in the sharding contract within the first preset range;

[0109] Performing a second tampering on the compensation data in the sharding contract within a second preset range;

[0110] Based on the first tampering and the second tampering, several tampered shard contracts are obtained, and the tampered hash value of each tampered shard contract is obtained;

[0111] The risk quantification module is used to obtain the tampering risk of the target provident fund contract based on the collision result between the standard hash value and any tampered hash value.

[0112] The innovative features of the present invention are as follows:

[0113] 1. A large number of historical provident fund contracts still use MD5 for signature verification. The present invention can seamlessly adapt to these existing files without changing the original electronic signature archiving mechanism.

[0114] 2. The collision susceptibility of MD5 enables the present invention to controllably modify contract data through constructive tampering and detect possible tampering risks.

[0115] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation methods. The above specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make many forms based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A method for processing electronic signatures of provident fund business process data, characterized in that: include: Step 1: Obtain the target provident fund contract in PDF format; Step 2: Analyze the target provident fund contract and determine the most favorable data for each contracting party in the target provident fund contract and the compensation data of the target provident fund contract, including: Obtain N data in the target provident fund contract and obtain the dependency relationship of the N data; wherein the dependency relationship means that if the i-th data is modified, the j-th data must be modified accordingly to maintain the correct logical relationship of the data in the target provident fund contract, then there is a dependency relationship between the i-th data and the j-th data; Tamper with each character of the i-th data, and based on the dependency relationship, tamper with the data that has a dependency relationship with the i-th data accordingly to obtain a tampering contract; Calculate the gain amount of each contracting party in the target provident fund contract based on the tampered contract; where the gain amount represents the difference between the payment amount of the tampered contract and the payment amount of the target provident fund contract for any contracting party; Calculate the most favorable number for each contract subject for each data separately; Based on the best value of the s-th contract subject and N data, select the data with the largest best value with the s-th contract subject as the best data of the s-th contract subject, so as to obtain the best data of each contract subject; The compensation data represents the visual signature and visual seal in the target provident fund contract; Step 3: Extract the most favorable data and compensation data from the target provident fund contract to form a shard contract, and calculate the standard hash value of the shard contract; Step 4: Repeat the following steps: Perform the first tampering on the most beneficial data in the sharding contract within the first preset range; Performing a second tampering on the compensation data in the sharding contract within a second preset range; Based on the first tampering and the second tampering, several tampered shard contracts are obtained, and the tampered hash value of each tampered shard contract is obtained; Step 5: Based on the collision result between the standard hash value and any tampered hash value, the tampering risk of the target provident fund contract is obtained.

2. A method for processing electronic signatures of provident fund business process data according to claim 1, characterized in that: The formula for calculating the most favorable number is as follows: ； in, Indicates the most favorable number of the i-th data to the s-th contract subject, Indicates that each character of the i-th data is tampered with, and the total number of tampered characters is obtained based on the dependency relationship. Indicates the number of data that has a dependency relationship with the i-th data, Indicates the The amount of gain of each contract entity.

3. The electronic signature processing method for provident fund business process data according to claim 2 is characterized in that: Forming a sharding contract, including first extraction, second extraction and data fusion: The first extraction includes: extracting the most favorable data of the target provident fund contract, wherein the extraction includes: obtaining the first extracted data by locating the most favorable data in a visualization area of ​​the target provident fund contract; The second extraction includes: extracting compensation data from the target provident fund contract, the extraction including: determining the permitted writing position and permitted stamping position of the visual signature and the visual seal in the target provident fund contract based on manual experience; extracting the permitted writing position in the visual area of ​​the target provident fund contract; extracting the permitted stamping position in the visual area of ​​the target provident fund contract, to obtain second extracted data; The data fusion includes: initially generating a blank page in PDF format, the blank page having the same size as the target provident fund contract, fitting the first extracted data and the second extracted data to the blank page, and obtaining a fragmented contract.

4. A method for processing electronic signatures of provident fund business process data according to claim 3, characterized in that: The first preset range includes: obtaining the maximum value and minimum value of each most favorable data in the target provident fund contract in the historical time period, and using the maximum value and the minimum value as the maximum value and the minimum value of the first preset range of the corresponding most favorable data.

5. The method for processing electronic signatures of provident fund business process data according to claim 4, characterized in that: The second preset range includes: The constraint conditions for establishing the second preset range based on the visual signature and the visual seal include: First constraint: the rewritten area of ​​the visual signature and visual seal must be greater than or equal to a preset area threshold; Second constraint: the position of the visual signature does not exceed the allowed writing position; The third constraint: the position of the visual seal does not exceed the allowed stamping position; A second preset range is obtained based on the combined constraints of the first constraint, the second constraint, and the third constraint.

6. A method for processing electronic signatures of provident fund business process data according to claim 5, characterized in that: Based on the first tampering and the second tampering, several tampered shard contracts are obtained, including performing the following steps on each contract subject: Perform the first tampering: obtain the data accuracy of the most favorable data of the s-th contract subject, and based on the maximum value of the s-th contract subject in the first preset range, perform gradient descent according to the minimum unit of data accuracy until the minimum value of the s-th contract subject in the first preset range is reached or the standard hash value and the tampered hash value collide successfully; Perform the second tampering: traverse the visual signature and the visual seal in the second preset range with pixel-level accuracy to obtain K tampering compensation schemes; In gradient order, for each gradient of the first tampering, K tampering compensation schemes are matched.

7. A method for processing electronic signatures of provident fund business process data according to claim 6, characterized in that: Both the standard hash value and the tampered hash value are calculated based on the MD5 hash algorithm.

8. A method for processing electronic signatures of provident fund business process data according to claim 7, characterized in that: The tampering risk of the target provident fund contract is calculated based on the following formula: ； in, Indicates the tampering risk of the target provident fund contract, Indicates the number of contracting parties of the target provident fund contract, express The index of Indicates the The value of the most favorable data corresponding to the successful collision of the contract subjects, Indicates the The value of the most favorable data corresponding to each contract subject in the target provident fund contract, Represents the absolute value operation.

9. An electronic signature processing system for provident fund business process data, characterized in that: The method according to any one of claims 1 to 8, comprising: Data collection module, used to obtain the target provident fund contract in PDF format; A data parsing module is used to parse the target provident fund contract and determine the most favorable data of each contracting party in the target provident fund contract and the compensation data of the target provident fund contract; The contract sharding module is used to extract the most favorable data and compensation data from the target provident fund contract, form a sharded contract, and calculate the standard hash value of the sharded contract; The hash collision module is used to repeatedly perform the following steps: Perform the first tampering on the most beneficial data in the sharding contract within the first preset range; Performing a second tampering on the compensation data in the sharding contract within a second preset range; Based on the first tampering and the second tampering, several tampered shard contracts are obtained, and the tampered hash value of each tampered shard contract is obtained; The risk quantification module is used to obtain the tampering risk of the target provident fund contract based on the collision result between the standard hash value and any tampered hash value.

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