Dynamic evaluation system for talent financial value grade based on pricing rudder

Abstract

The present application belongs to the field of human capital evaluation, financial technology and data security technology, and provides a talent financial value grade dynamic evaluation system based on a pricing rudder, which comprises a data source authentication module, a generation identification module, a trust degree calculation module, a verification security protection capability generation pre-authorization rule module, a transmission channel module for field encryption fragmentation of talent sensitive data, a quantum encryption transmission channel and dynamic IP tunnel establishment module, a data source calling storage evidence module for storage evidence API calling behavior and real-time access compliance verification module, a risk decision module for generating dynamic risk score and three-level response strategy, and a pricing rudder module for generating dynamic financial value grade from four dimensions of talent, color, collection and finance based on the processed data. Through the cooperation of multiple modules, the present application realizes talent data full-process safety management and control and dynamic value evaluation, and improves the data interaction security, evaluation accuracy and risk response efficiency.

Description

Technical Field

[0001] This invention belongs to the fields of human capital assessment, financial technology and data security technology, specifically a dynamic assessment system for the financial value of talent based on pricing benchmarks. Background Technology

[0002] This invention belongs to the fields of human capital assessment, financial technology, and data security. In the practice of talent financial value assessment, existing systems have several technical defects:

[0003] First, the data source identification is vague and lacks a unique and tamper-proof identification mechanism, which leads to confusion of identity and difficulty in tracing responsibility in data interaction, making it difficult to guarantee the credibility of the data source;

[0004] Secondly, the transmission and storage of sensitive data involving personnel lacks refined security control. System-level access control is often used, which cannot achieve field-level isolation. This makes it easy for a single vulnerability to cause overall data leakage. Furthermore, the static key management poses a risk of access abuse.

[0005] Third, the data source call behavior lacks end-to-end tamper-proof evidence storage, and compliance verification relies on manual or static rules. The response to violations such as unauthorized access, high-frequency abuse, and timeout calls is delayed, making it difficult to block risks in real time.

[0006] Fourth, the risk assessment model is static, relying solely on fixed thresholds for judgment. It cannot dynamically quantify risks by combining historical behavior and industry benchmarks, resulting in a single response strategy that makes it difficult to balance security and business continuity.

[0007] Fifth, the dimensions of talent value assessment are rigid and rely heavily on static data, lacking real-time adaptation to the dynamic growth of talent and changes in the industry. This results in insufficient accuracy of assessment results, making it difficult to meet the dynamic needs of scenarios such as financial credit and corporate equity incentives.

[0008] Therefore, there is an urgent need for an integrated system that can achieve trusted authentication of data sources, refined protection of sensitive data, compliant evidence storage of call behavior, dynamic risk response, and accurate value assessment to solve the above-mentioned technical problems.

[0009] To this end, the present invention provides a dynamic evaluation system for the financial value of talent based on pricing benchmarks. Summary of the Invention

[0010] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0011] The technical solution adopted by this invention to solve its technical problem is:

[0012] Firstly, this invention provides a dynamic evaluation system for the financial value of talent based on pricing-oriented valuation, including:

[0013] Data source authentication module: generates data source identifiers, calculates the trust level of data transmission without human intervention, verifies the security protection capabilities of data sources, and generates pre-authorization rules;

[0014] Transmission Channel Module: Sends talent-sensitive data to trusted data sources. First, it generates encrypted fragments and temporary keys for the talent-sensitive data according to fields, and then uses the temporary keys to establish a talent-sensitive data transmission channel.

[0015] Data source call evidence storage module: Stores data source API call behavior as evidence and verifies the compliance of data source calls for sensitive talent data in real time;

[0016] Risk Decision Module: Generates dynamic risk scores and defines a three-level response strategy to achieve fully automated processing from risk detection to risk response;

[0017] Pricing Targeting Module: Based on the talent data processed by the above modules, a dynamic financial value level is generated.

[0018] In this invention, as a further improvement, the specific process for generating the data source identifier is as follows:

[0019] A data source identifier is generated using a hash encryption algorithm. The formula for generating the data source identifier is as follows: Where ⊕ represents the concatenation of feature vectors, and Hash(·) is the cryptographic hash function. For hardware feature vectors, For behavioral feature vectors, This is the feature vector for credit scoring.

[0020] In this invention, as a further improvement, the specific process for calculating the trust level of data transmission without human intervention is as follows:

[0021] Based on the characteristics of the data source identifier, without sharing the original data, the system and the data source use federated learning technology to dynamically optimize the characteristics of the data source identifier and quantify the trust level of the data source.

[0022] In this invention, as a further improvement, the specific process of verifying the security protection capabilities of the data source is as follows:

[0023] Based on trusted data sources, the security protection capability value of qualified data sources is verified and quantified by formula: P=α·X+β·Y+γ·Z, where α, β, and γ are weighting coefficients and α+β+γ=1, X is the vulnerability rate of the data source, Y is the encryption strength coefficient, and Z is the control rate.

[0024] In this invention, as a further improvement, the specific process of generating pre-authorization rules is as follows:

[0025] For data sources with qualified security protection capabilities, pre-authorization rules are generated. The pre-authorization rules include: data access scope and data validity window.

[0026] In this invention, as a further improvement, the specific process of generating encrypted fragments and temporary keys for talent-sensitive data by field is as follows:

[0027] Segmenting raw talent data by field granularity: The talent-sensitive data D is split into m independent slices according to fields;

[0028] Each slice is encrypted using the AES symmetric encryption algorithm, generating encrypted data and a temporary key. The temporary key is bound to the data expiration window and expires when the window expires.

[0029] In this invention, as a further improvement, the specific process of establishing a talent-sensitive data transmission channel using a temporary key is as follows:

[0030] An end-to-end encryption key is generated using a quantum communication protocol to protect the security of the transmission of temporary keys and encrypted data. A dynamic IP real-time switching mechanism is established to randomly select a set of IP nodes {IP1, IP2, ..., IP...} for the path of each talent data transmission. n The node combination and path switching mechanism in} is to generate node combinations from the IP node set using a random selection function, use the generated node combinations as talent data transmission channels, and randomly switch node combinations every minute.

[0031] In this invention, as a further improvement, the specific process for real-time verification of the compliance of the data source's access to sensitive talent data is as follows:

[0032] Deploy smart contracts to verify three compliance aspects in real time.

[0033] The first compliance check is scope compliance, which verifies whether the accessed field is within the field set of the pre-authorization rule. If the accessed field is not within the field set of the pre-authorization rule, the data source call is deemed to be in violation.

[0034] The second compliance check is frequency compliance. The call frequency is counted within a unit of time and compared with the call frequency threshold. If the call frequency exceeds the call frequency threshold, the data source call is judged to be in violation.

[0035] The third compliance check is time compliance. It checks whether the call time is within the data timeliness window. If the call time is not within the data timeliness window, the data source call is deemed to be in violation.

[0036] If any compliance check fails, the system will automatically revoke the data source access permission.

[0037] In this invention, as a further improvement, the specific process of generating a dynamic risk score is as follows:

[0038] Using the node embedding vectors output by the graph neural network, the cosine similarity between the current call behavior and historical call behaviors is calculated. This cosine similarity is then combined with the average cosine similarity of industry call behaviors to generate a comprehensive risk score R. The formula is as follows: C S C represents the cosine similarity between the current call behavior and the historical call behavior. S′ This represents the average cosine similarity of industry call behaviors.

[0039] In this invention, as a further improvement, the specific process of defining the three-level response strategy is as follows: corresponding measures are executed based on the comprehensive risk score R.

[0040] When the comprehensive risk score R is greater than or equal to the first judgment threshold and less than the second judgment threshold, a yellow warning notification is triggered, and an early warning message is sent to the system.

[0041] When the comprehensive risk score is greater than or equal to the second judgment threshold and less than the third judgment threshold, an orange isolation warning is triggered, restricting the data source from accessing sensitive talent data.

[0042] When the comprehensive risk score is greater than or equal to the third judgment threshold, a red circuit breaker warning is triggered, and the system cuts off the connection with the data source.

[0043] Secondly, this invention provides a dynamic evaluation method for the financial value level of talent based on pricing targeting, including:

[0044] S1: Generate a data source identifier, calculate the trust level for data transmission of sensitive data without human intervention, verify the security protection capabilities of the data source, and generate pre-authorization rules;

[0045] S2: Send talent-sensitive data to a trusted data source. First, generate encrypted fragments and temporary keys for the talent-sensitive data according to the fields. Use the temporary keys to establish a talent-sensitive data transmission channel.

[0046] S3: Store evidence of data source API call behavior and verify the compliance of data source calls for sensitive talent data in real time;

[0047] S4: Generate dynamic risk scores and define a three-level response strategy to achieve fully automated processing from risk detection to risk response;

[0048] S5: Based on the talent data processed by the above modules, a dynamic financial value level is generated.

[0049] The beneficial effects of this invention are as follows:

[0050] The data source authentication module generates unique identifiers to confirm identities and rights. Combined with federated learning, it quantifies trust levels while protecting privacy, filters data sources with security capabilities, and generates pre-authorization rules including access scope and expiration windows. This ensures the credibility and controllability of data interactions from the source, solving the problems of ambiguous identities and abuse of permissions in traditional systems. The transmission channel module encrypts and fragments sensitive data by field, and binds temporary keys to expiration windows to prevent permissions from being valid for a long time. Combined with quantum encrypted transmission and dynamic IP tunnel switching, it achieves field-level isolation and anti-tracing of transmission paths, significantly reducing the risk of data leakage and addressing the insufficient granularity of traditional system-level protection. The data source call evidence storage module records API behavior on the blockchain, and verifies the scope, frequency, and other parameters in real time through smart contracts. Time compliance is ensured by automatically canceling access permissions in case of violations, achieving end-to-end tamper-proof auditing and automated risk response. This solves the problems of lagging manual verification and difficulty in tracing the source in traditional manual methods. The risk decision module generates dynamic risk scores based on graph neural networks combined with historical behavior and industry benchmarks. It accurately handles risks through a three-level response strategy (early warning, isolation, and circuit breaker), balancing security and business continuity. This solves the problem of the single response in traditional static risk assessment. Dynamic and accurate assessment of talent value: The pricing module integrates and processes data from four dimensions—talent, performance, procurement, and finance—to generate dynamic financial value levels. It adapts to talent growth and industry changes in real time, improving the adaptability of assessment results to scenarios such as financial credit and equity incentives. This solves the problems of rigid assessment dimensions and insufficient accuracy in traditional assessment methods. Attached Figure Description

[0051] The invention will now be further described with reference to the accompanying drawings.

[0052] Figure 1 This is a system module diagram of the dynamic evaluation system for the financial value of talent based on pricing targeting, which is the basis of this invention.

[0053] Figure 2 This is a flowchart illustrating the steps of the dynamic evaluation method for the financial value of talent based on pricing benchmarks, as described in this invention. Detailed Implementation

[0054] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0055] Example 1

[0056] like Figure 1 As shown in the embodiment of the present invention, the dynamic evaluation system for talent financial value level based on pricing targeting includes:

[0057] Data source authentication module: generates data source identifiers, calculates the trust level of data transmission without human intervention, verifies the security protection capabilities of data sources, and generates pre-authorization rules;

[0058] The specific process for generating the data source identifier is as follows:

[0059] A data source identifier is generated using a hash encryption algorithm. The formula for generating the data source identifier is as follows: Where ⊕ represents the concatenation of feature vectors, and Hash(·) is the cryptographic hash function. For hardware feature vectors, For behavioral feature vectors, This is the feature vector for credit scoring.

[0060] In this invention, no limitation is made to the hash encryption algorithm; for example, the SHA-256 algorithm can be used to generate the data source identifier.

[0061] For example, the behavioral characteristics, credit scores, and hardware characteristics are obtained by collecting the hardware characteristics of the data source device, including CPU serial number and MAC address, analyzing the interface call frequency of the data source, and generating a quantitative score based on the historical cooperation records and industry reputation data of the data source; converting the hardware characteristics, behavioral characteristics, and credit scores into feature vectors, calculating them through a hash encryption algorithm, and generating a data source identifier.

[0062] The specific process for calculating the trust level of data transmission without human intervention is as follows:

[0063] Based on the characteristics of the data source identifier, without sharing the original data, the system and the data source use federated learning technology to dynamically optimize the characteristics of the data source identifier and quantify the trust level of the data source.

[0064] For example, the weights of each type of feature (behavioral features, credit score, hardware features) are quantified and assigned. Without sharing raw data with the data source, the system uses federated learning technology to calculate the trust level between the system and the data source in the absence of talent data transmission. The specific formula for calculating the trust level is as follows: Among them, w i f is the weight of the i-th type of feature. i Let be the evaluation function for the i-th type of feature;

[0065] The specific process for verifying the security protection capabilities of the data source is as follows:

[0066] In some embodiments, weak data source protection capabilities may lead to data leakage risks, such as low security protection levels of the data source itself. Therefore, it is necessary to assess the security protection capabilities of the data source itself.

[0067] Based on trusted data sources, the security protection capability value of qualified data sources is verified, and the security protection capability value of data sources is quantified by the formula: P=α·X+β·Y+γ·Z, where α, β, and γ are weight coefficients and α+β+γ=1, X is the vulnerability rate of the data source, Y is the encryption strength coefficient, and Z is the control rate.

[0068] For example, by obtaining the vulnerability rate, encryption strength coefficient, and control rate of the data source through security protection capability testing software, the weight values ​​of α, β, and γ can be dynamically generated by the security protection capability testing software, and the security protection capability value can be calculated by the above formula.

[0069] If the security protection capability value is greater than or equal to the industry average security protection capability, then the data source security protection capability is qualified.

[0070] The specific process of generating pre-authorization rules is as follows: generate pre-authorization rules for data sources with qualified security protection capabilities. The pre-authorization rules include: data access scope and data validity window.

[0071] For example, data access scope, such as only allowing access to patent and academic qualification fields, and data timeliness window, which limits the time range of data access, such as XX year XX month XX day 9:00-XX year XX month XX day 9:00;

[0072] Function 1: To ensure the unique identity and traceability of data sources; Function 2: To accurately quantify trust levels while protecting data privacy; Function 3: To screen data sources with security protection capabilities from the source; Function 4: To achieve refined access control for data.

[0073] Transmission Channel Module: Sends talent-sensitive data to trusted data sources. First, it generates encrypted fragments and temporary keys for the talent-sensitive data according to fields, and then uses the temporary keys to establish a talent-sensitive data transmission channel.

[0074] The specific process of generating encrypted fragments and temporary keys for talent-sensitive data by field is as follows: the original talent data is divided into m independent slices by field granularity: the talent-sensitive data D is split into m independent slices by field, such as a slice for education level, a slice for number and name of patents, a slice for salary level, etc.

[0075] Each slice is encrypted using the AES symmetric encryption algorithm, generating encrypted data and a temporary key. The temporary key is bound to the data expiration window and expires when it expires. The data source only obtains the temporary key of the authorized slice to ensure that it cannot access unauthorized fields.

[0076] The specific process of establishing a talent-sensitive data transmission channel using a temporary key is as follows: An end-to-end encryption key is generated through a quantum communication protocol to protect the transmission security of the temporary key and encrypted data. A dynamic IP real-time switching mechanism is established to randomly select a set of IP nodes {IP1, IP2, ..., IP...} for the path of each talent data transmission. n The node combination and path switching mechanism in the} uses a random selection function to generate node combinations from the IP node set, uses the generated node combinations as the talent data transmission channel, and randomly switches the node combinations every minute to improve the anti-tracking of the transmission path;

[0077] Function 1: To achieve field-level isolation and encryption protection for sensitive talent data; Function 2: To prevent abuse of permissions through temporary key time-limited control; Function 3: To ensure the underlying security of the channel through quantum encryption transmission; Function 4: To enhance the anti-tracing resistance of the transmission path through dynamic IP tunneling.

[0078] Data source call evidence storage module: Stores data source API call behavior as evidence and verifies the compliance of data source calls for sensitive talent data in real time;

[0079] The specific process of storing evidence of data source API call behavior is as follows: the stored content includes: data source identifier, timestamp, coordinates of the accessed talent-sensitive data field, and hash values ​​of data before and after the call;

[0080] For example, the coordinates of talent-sensitive data fields, such as in Patent Article 3, atomically record the API call behavior of the data source on the blockchain to ensure that the data is tamper-proof;

[0081] The specific process for real-time verification of the compliance of data sources in accessing sensitive talent data involves deploying smart contracts to verify three compliance aspects in real time.

[0082] The first compliance check is scope compliance, which verifies whether the accessed field is within the field set of the pre-authorization rule. If the accessed field is not within the field set of the pre-authorization rule, the data source call is deemed to be in violation.

[0083] The second compliance check is frequency compliance. The call frequency is counted within a unit of time and compared with the call frequency threshold. If the call frequency exceeds the call frequency threshold, the data source call is judged to be in violation.

[0084] The third compliance check is time compliance. It checks whether the call time is within the data timeliness window. If the call time is not within the data timeliness window, the data source call is deemed to be in violation.

[0085] If any compliance check fails, the system will automatically revoke the data source access permission.

[0086] Function 1: To achieve tamper-proof end-to-end evidence storage of API call behavior; Function 2: To perform real-time, multi-dimensional compliance verification and dynamically block unauthorized access; Function 3: To automate risk response and reduce the cost of manual intervention; Function 4: To strengthen the rigid enforcement of pre-authorization rules.

[0087] Risk Decision Module: Generates dynamic risk scores and defines a three-level response strategy to achieve fully automated processing from risk detection to risk response;

[0088] The specific process for generating the dynamic risk score is as follows: using the node embedding vectors output by the graph neural network, the cosine similarity between the current call behavior and historical call behavior is calculated. This cosine similarity is then combined with the average cosine similarity of industry call behaviors to generate a comprehensive risk score R, as shown in the formula: C S C represents the cosine similarity between the current call behavior and the historical call behavior. S′ This represents the average cosine similarity of industry call behaviors;

[0089] The specific process for defining the three-level response strategy is as follows: Implement corresponding measures based on the comprehensive risk score R.

[0090] When the comprehensive risk score R is greater than or equal to the first judgment threshold and less than the second judgment threshold, a yellow warning notification is triggered, and an early warning message is sent to the system.

[0091] When the comprehensive risk score is greater than or equal to the second judgment threshold and less than the third judgment threshold, an orange isolation warning is triggered, restricting the data source from accessing sensitive talent data.

[0092] When the comprehensive risk score is greater than or equal to the third judgment threshold, a red circuit breaker warning is triggered, and the system cuts off the connection with the data source.

[0093] The first, second, and third judgment thresholds are reference values ​​set by those skilled in the art.

[0094] Pricing Targeting Module: Based on the talent data processed by the above modules, a dynamic financial value rating is generated;

[0095] Specifically, a dynamic financial value level is generated comprehensively from four dimensions;

[0096] For example, a dynamic financial value rating can be generated by comprehensively considering the following four dimensions:

[0097] Dimension 1, the talent dimension, integrates data such as academic qualifications, professional skills certificates, and project experience;

[0098] Dimension Two, Outstanding Dimension, analyzes the number of patents, honor level, and industry influence;

[0099] Dimension three, data collection dimension, integrates data such as social security records, credit scores, and consumption behavior;

[0100] Dimension four, the wealth dimension, combines salary level, asset status, and investment returns;

[0101] The dynamic rating generation unit combines scores from four dimensions to generate a talent financial value rating.

[0102] Example 2

[0103] Based on Example 1, this invention provides a dynamic evaluation method for the financial value level of talent based on pricing targeting, including:

[0104] S1: Generate a data source identifier, calculate the trust level for data transmission of sensitive data without human intervention, verify the security protection capabilities of the data source, and generate pre-authorization rules;

[0105] The specific process for generating the data source identifier is as follows:

[0106] A data source identifier is generated using a hash encryption algorithm. The formula for generating the data source identifier is as follows: Where ⊕ represents the concatenation of feature vectors, and Hash(·) is the cryptographic hash function. For hardware feature vectors, For behavioral feature vectors, This is the feature vector for credit scoring.

[0107] The specific process for calculating the trust level of data transmission without human intervention is as follows:

[0108] Based on the characteristics of the data source identifier, without sharing the original data, the system and the data source use federated learning technology to dynamically optimize the characteristics of the data source identifier and quantify the trust level of the data source.

[0109] The specific process for verifying the security protection capabilities of the data source is as follows:

[0110] Based on trusted data sources, the security protection capability value of qualified data sources is verified and quantified by formula: P=α·X+β·Y+γ·Z, where α, β, and γ are weighting coefficients and α+β+γ=1, X is the vulnerability rate of the data source, Y is the encryption strength coefficient, and Z is the control rate.

[0111] The specific process for generating pre-authorization rules is as follows:

[0112] For data sources with qualified security protection capabilities, pre-authorization rules are generated. The pre-authorization rules include: data access scope and data validity window.

[0113] S2: Send talent-sensitive data to a trusted data source. First, generate encrypted fragments and temporary keys for the talent-sensitive data according to the fields. Use the temporary keys to establish a talent-sensitive data transmission channel.

[0114] The specific process of generating encrypted fragments and temporary keys for sensitive talent data by field is as follows:

[0115] Segmenting raw talent data by field granularity: The talent-sensitive data D is split into m independent slices according to fields;

[0116] Each slice is encrypted using the AES symmetric encryption algorithm, generating encrypted data and a temporary key. The temporary key is bound to the data expiration window and expires when the window expires.

[0117] The specific process of establishing a talent-sensitive data transmission channel using a temporary key is as follows:

[0118] An end-to-end encryption key is generated using a quantum communication protocol to protect the security of the transmission of temporary keys and encrypted data. A dynamic IP real-time switching mechanism is established to randomly select a set of IP nodes {IP1, IP2, ..., IP...} for the path of each talent data transmission. n The node combination and path switching mechanism in} is to generate node combinations from the IP node set using a random selection function, use the generated node combinations as talent data transmission channels, and randomly switch node combinations every minute.

[0119] S3: Store evidence of data source API call behavior and verify the compliance of data source calls for sensitive talent data in real time;

[0120] The specific process for real-time verification of the compliance of data sources in accessing sensitive talent data is as follows:

[0121] Deploy smart contracts to verify three compliance aspects in real time.

[0122] The first compliance check is scope compliance, which verifies whether the accessed field is within the field set of the pre-authorization rule. If the accessed field is not within the field set of the pre-authorization rule, the data source call is deemed to be in violation.

[0123] The second compliance check is frequency compliance. The call frequency is counted within a unit of time and compared with the call frequency threshold. If the call frequency exceeds the call frequency threshold, the data source call is judged to be in violation.

[0124] The third compliance check is time compliance. It checks whether the call time is within the data timeliness window. If the call time is not within the data timeliness window, the data source call is deemed to be in violation.

[0125] If any compliance check fails, the system will automatically revoke the data source access permission.

[0126] S4: Generate dynamic risk scores and define a three-level response strategy to achieve fully automated processing from risk detection to risk response;

[0127] The specific process for generating dynamic risk scores is as follows:

[0128] Using the node embedding vectors output by the graph neural network, the cosine similarity between the current call behavior and historical call behaviors is calculated. This cosine similarity is then combined with the average cosine similarity of industry call behaviors to generate a comprehensive risk score R. The formula is as follows: C S C represents the cosine similarity between the current call behavior and the historical call behavior. S′ This represents the average cosine similarity of industry call behaviors.

[0129] The specific process for defining the three-level response strategy is as follows: Implement corresponding measures based on the comprehensive risk score R.

[0130] When the comprehensive risk score R is greater than or equal to the first judgment threshold and less than the second judgment threshold, a yellow warning notification is triggered, and an early warning message is sent to the system.

[0131] When the comprehensive risk score is greater than or equal to the second judgment threshold and less than the third judgment threshold, an orange isolation warning is triggered, restricting the data source from accessing sensitive talent data.

[0132] When the comprehensive risk score is greater than or equal to the third judgment threshold, a red circuit breaker warning is triggered, and the system cuts off the connection with the data source.

[0133] S5: Based on the talent data processed by the above modules, a dynamic financial value level is generated.

[0134] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic evaluation system for the financial value of talent based on pricing benchmarks, characterized by: include: Data source authentication module: generates data source identifiers, calculates the trust level of data transmission without human intervention, verifies the security protection capabilities of data sources, and generates pre-authorization rules; The specific process for generating the data source identifier is as follows: A data source identifier is generated using a hash encryption algorithm. The formula for generating the data source identifier is as follows: ,in, This represents the concatenation of eigenvectors. For cryptographic hash functions, For hardware feature vectors, For behavioral feature vectors, This is the feature vector for credit scoring; Transmission Channel Module: Sends talent-sensitive data to trusted data sources. First, it generates encrypted fragments and temporary keys for the talent-sensitive data according to fields, and then uses the temporary keys to establish a talent-sensitive data transmission channel. The specific process of establishing a talent-sensitive data transmission channel using a temporary key is as follows: An end-to-end encryption key is generated using a quantum communication protocol to protect the security of the transmission of temporary keys and encrypted data. A dynamic IP real-time switching mechanism is also established to randomly select a set of IP nodes for the path of each talent data transmission. The node combination and path switching mechanism in the system is to generate node combinations from the IP node set using a random selection function, use the generated node combinations as the data transmission channel for sensitive personnel data, and randomly switch node combinations every minute. Data source call evidence storage module: Stores data source API call behavior as evidence and verifies the compliance of data source calls for sensitive talent data in real time; Risk Decision Module: Generates dynamic risk scores and defines a three-level response strategy to achieve fully automated processing from risk detection to risk response; Pricing Targeting Module: Generates dynamic financial value levels.

2. The dynamic evaluation system for talent financial value level based on pricing targeting as described in claim 1, characterized in that: The specific process for calculating the trust level of sensitive data transmission without human intervention is as follows: Based on the characteristics of the data source identifier, without sharing the original data, the system and the data source use federated learning technology to dynamically optimize the characteristics of the data source identifier and quantify the trust level of the data source.

3. The dynamic evaluation system for talent financial value based on pricing targeting as described in claim 1, characterized in that: The specific process for verifying the security protection capabilities of the data source is as follows: Based on trusted data sources, the security protection capability value of these data sources is verified, and then quantified using a formula: ,in, , , The weighting coefficients and , For the vulnerability rate of the data source, This is the encryption strength coefficient. For control rate.

4. The dynamic evaluation system for talent financial value level based on pricing targeting as described in claim 1, characterized in that: The specific process for generating pre-authorization rules is as follows: For data sources with qualified security protection capabilities, pre-authorization rules are generated. The pre-authorization rules include: data access scope and data validity window.

5. The dynamic evaluation system for talent financial value rating based on pricing targeting as described in claim 1, characterized in that: The specific process of generating encrypted fragments and temporary keys for sensitive talent data by field is as follows: Segmenting raw talent data by field granularity: The talent-sensitive data D is split into m independent slices according to fields; Each slice is encrypted using the AES symmetric encryption algorithm, generating encrypted data and a temporary key. The temporary key is bound to the data expiration window and expires when the window expires.

6. The dynamic evaluation system for talent financial value based on pricing targeting as described in claim 1, characterized in that: The specific process for real-time verification of the compliance of data sources in accessing sensitive talent data is as follows: Deploy smart contracts to verify three compliance aspects in real time. The first compliance check is scope compliance, which verifies whether the accessed field is within the field set of the pre-authorization rule. If the accessed field is not within the field set of the pre-authorization rule, the data source call is deemed to be in violation. The second compliance check is frequency compliance. The call frequency is counted within a unit of time and compared with the call frequency threshold. If the call frequency exceeds the call frequency threshold, the data source call is judged to be in violation. The third compliance check is time compliance. It checks whether the call time is within the data timeliness window. If the call time is not within the data timeliness window, the data source call is deemed to be in violation. If any compliance check fails, the system will automatically revoke the data source access permission.

7. The dynamic evaluation system for talent financial value level based on pricing targeting as described in claim 1, characterized in that: The specific process for generating dynamic risk scores is as follows: Using the node embedding vectors output by the graph neural network, the cosine similarity between the current call behavior and the historical call behavior is calculated. The cosine similarity between the current call behavior and the historical call behavior is combined with the average cosine similarity of the industry call behavior to obtain the comprehensive risk score R.

8. The dynamic evaluation system for talent financial value level based on pricing targeting as described in claim 7, characterized in that: The specific process for defining the three-level response strategy is as follows: Implement corresponding measures based on the comprehensive risk score R. When the comprehensive risk score R is greater than or equal to the first judgment threshold and less than the second judgment threshold, a yellow warning notification is triggered, and an early warning message is sent to the system. When the comprehensive risk score is greater than or equal to the second judgment threshold and less than the third judgment threshold, an orange isolation warning is triggered, restricting the data source from accessing sensitive talent data. When the comprehensive risk score is greater than or equal to the third judgment threshold, a red circuit breaker warning is triggered, and the system cuts off the connection with the data source.

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