Enterprise management information tamper-proof sharing method and system based on Internet

Abstract

The invention discloses an enterprise management information tamper-proof sharing method and system based on the Internet, and relates to the technical field of information security, and the method comprises the steps: obtaining the network state information of an external partner access region; according to the network state information, whether network infrastructure faults exist in the external partner access area is judged, and a network infrastructure fault judgment result is obtained; if the network infrastructure fault judgment result is that the network infrastructure fault exists, obtaining user operation behavior data of an external partner access area; adjusting the weight of a risk assessment factor corresponding to the network state information to obtain a target weight; performing risk assessment on the user operation behavior data according to the target weight to obtain a risk assessment result; and performing authority control on the user operation according to a risk assessment result. According to the invention, tamper-proof sharing of enterprise management information is realized, and the risk assessment accuracy and the system security are improved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to an Internet-based enterprise management information tamper-proof sharing method and system. Background Art

[0002] Enterprises rely heavily on internet-based enterprise information management systems (EIMs) for daily operations. These systems carry vast amounts of critical business data and sensitive information. With the prevalence of remote and mobile work, it has become common for employees to access these systems from diverse locations and using a variety of terminals to share and operate information. This requires enterprises to ensure that critical information is protected from unauthorized tampering, theft, or destruction during sharing and use, and that all operations are effectively traceable. Traditional approaches typically employ security policies based on user behavior analysis and dynamic permission control. These methods collect and analyze historical user data to build behavioral baseline models and dynamically adjust user access permissions based on different behavioral factors. However, in environments where network infrastructure failures are possible, traditional approaches only consider user behavior or terminal status, leading to risk assessment errors, impacting business continuity, and inaccurate risk assessments. Furthermore, network failures can provide cover for potential attacks, potentially missing high-risk operations and compromising system security.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main purpose of the embodiments of the present invention is to propose an Internet-based enterprise management information tamper-proof sharing method and system, which can realize tamper-proof sharing of enterprise management information in combination with the judgment of network infrastructure failure, thereby improving risk assessment accuracy and system security.

[0005] In one aspect, an embodiment of the present invention provides an Internet-based method for tamper-proof sharing of enterprise management information, comprising the following steps: Obtain network status information of the access area of ​​external partners; Determining, based on the network status information, whether there is a network infrastructure failure in the access area of ​​the external partner, and obtaining a network infrastructure failure determination result; If the network infrastructure fault determination result is that a network infrastructure fault exists, obtaining user operation behavior data of the external partner access area; Adjusting the weight of the risk assessment factor corresponding to the network status information to obtain a target weight; Performing a risk assessment on the user operation behavior data according to the target weight to obtain a risk assessment result; Based on the risk assessment results, user operations are subject to permission control.

[0006] In some embodiments, adjusting the weight of the risk assessment factor corresponding to the network status information to obtain a target weight includes: Acquire first attribute information of the network infrastructure fault, where the first attribute information is used to characterize a type or impact degree of the network infrastructure fault; Determining a target weight adjustment parameter corresponding to the first attribute information, wherein the target weight adjustment parameter is used to determine an adjustment method or adjustment range of each risk assessment factor weight; According to the target weight adjustment parameter, the weight of the risk assessment factor corresponding to the network status information is adjusted to obtain the target weight.

[0007] In some embodiments, determining the target weight adjustment parameter corresponding to the first attribute information includes: Retrieving a fault scenario record matching the first attribute information from a preset fault scenario knowledge base to obtain a fault scenario matching result; If the fault scenario matching result is that there is no matching fault scenario record, generating the target weight adjustment parameter according to the first attribute information and a preset parameter adjustment strategy, wherein the preset parameter adjustment strategy includes a parameter derivation strategy or a parameter generalization strategy; If the fault scenario matching result is that there is a matching fault scenario record, then a pre-stored weight adjustment parameter in the fault scenario record is extracted from the preset fault scenario knowledge base as the target weight adjustment parameter.

[0008] In some embodiments, after performing a risk assessment on the user operation behavior data according to the target weight and obtaining a risk assessment result, the method further includes: Obtaining user feedback information corresponding to the risk assessment result; Evaluate the effectiveness of the target weight adjustment parameter or the preset parameter adjustment strategy based on the risk assessment result and the user feedback information to obtain an effectiveness evaluation result; If the effectiveness evaluation result shows that the target weight adjustment parameter has low effectiveness, performing online adjustment processing on the target weight adjustment parameter according to the effectiveness evaluation result and the user feedback information; If the effectiveness evaluation result shows that the effectiveness of the preset parameter adjustment strategy is low, then the preset parameter adjustment strategy is optimized online according to the effectiveness evaluation result and the user feedback information.

[0009] In some embodiments, the effectiveness of the target weight adjustment parameter or the effectiveness of the preset parameter adjustment strategy is evaluated based on the risk assessment result and the user feedback information to obtain the effectiveness evaluation result, including: Analyzing the frequency of false positives and patterns of missed negatives in the risk assessment results to obtain an indication of system-side effectiveness; Processing the user feedback information to obtain a user-side validity indication, wherein the information processing includes classification, aggregation, and credibility evaluation; Determining whether there is a conflict between the system-side validity indication and the user-side validity indication, and obtaining a conflict determination result; If the conflict determination result is that there is no conflict, generating the validity evaluation result according to the system-side validity indication and the user-side validity indication; If the conflict determination result is that a conflict exists, dynamic attribute information of the network infrastructure fault and historical interaction characteristics of the affected user group are obtained, wherein the dynamic attribute information includes information on the fault evolution stage or information on changes in the fault impact scope, and the historical interaction characteristics include historical feedback accuracy information of the user group or information on the user group's sensitivity to system adjustments; Performing conflict resolution on the system-side validity indication and the user-side validity indication according to a preset conflict resolution strategy, the dynamic attribute information, and the historical interaction characteristics to obtain a conflict resolution result; The validity evaluation result is generated according to the conflict handling result.

[0010] In some embodiments, obtaining dynamic attribute information of the network infrastructure failure and historical interaction characteristics of the affected user group includes: Obtaining network performance data of the access area of ​​the external partner; Analyzing the network performance data to obtain the dynamic attribute information; Obtain historical operation data and historical feedback data of the affected user groups; The historical operation data and the historical feedback data are analyzed to obtain the historical interaction features.

[0011] In some embodiments, performing conflict handling on the system-side validity indication and the user-side validity indication according to a preset conflict handling strategy, the dynamic attribute information, and the historical interaction characteristics to obtain a conflict handling result includes: Determining a conflict handling path according to the preset conflict handling strategy, the dynamic attribute information, and the historical interaction characteristics, wherein the conflict handling path includes a parameterized adjustment path or a decision-making selection path; If the conflict handling path is the parameterized adjustment path, determining a first adjustment coefficient corresponding to the system-side validity indication and a second adjustment coefficient corresponding to the user-side validity indication according to the preset conflict handling strategy, the dynamic attribute information, and the historical interaction characteristics; modifying a first value of the system-side effectiveness indicator according to the first adjustment coefficient; Correcting the second value of the user-side validity indicator according to the second adjustment coefficient; generating the conflict resolution result according to the corrected first value and the corrected second value; If the conflict handling path is the decision-making selection path, determining a decision label based on the preset conflict handling strategy, the dynamic attribute information, and the historical interaction characteristics; According to the adoption decision label, one indication is selected from the system-side validity indication and the user-side validity indication as the conflict handling result.

[0012] In some embodiments, generating the conflict resolution result according to the corrected first value and the corrected second value includes: Determining, according to a preset fusion strategy, a first fusion weight corresponding to the revised first value and a second fusion weight corresponding to the revised second value; multiplying the corrected first value by the first fusion weight to obtain a first multiplication result; multiplying the corrected second value by the second fusion weight to obtain a second multiplication result; The first multiplication result and the second multiplication result are added to obtain the conflict handling result.

[0013] In some embodiments, determining, according to a preset fusion strategy, a first fusion weight corresponding to the revised first value and a second fusion weight corresponding to the revised second value includes: The first fusion weight and the second fusion weight are determined according to the preset fusion strategy, the dynamic attribute information, and the historical interaction characteristics.

[0014] On the other hand, an embodiment of the present invention provides an Internet-based enterprise management information tamper-proof sharing system, comprising: The network status information acquisition module is used to obtain the network status information of the access area of ​​the external partner; a fault determination module, configured to determine, based on the network status information, whether there is a network infrastructure fault in the access area of ​​the external partner, and obtain a network infrastructure fault determination result; A user behavior data acquisition module is configured to acquire user operation behavior data in the external partner access area if the network infrastructure fault determination result is that a network infrastructure fault exists; A weight adjustment module, configured to adjust the weight of the risk assessment factor corresponding to the network status information to obtain a target weight; A risk assessment module, configured to perform a risk assessment on the user operation behavior data according to the target weight to obtain a risk assessment result; The authority control module is used to perform authority control on user operations based on the risk assessment results.

[0015] The embodiments of the present application include at least the following beneficial effects: the embodiments of the present application first obtain the network status information of the external partner access area, then determine whether there is a network infrastructure failure in the external partner access area, and obtain a network infrastructure failure determination result; if the network infrastructure failure determination result is that there is a network infrastructure failure, then obtain the user operation behavior data of the external partner access area, and adjust the weight of the risk assessment factor corresponding to the network status information to obtain the target weight, then perform a risk assessment on the user operation behavior data to obtain a risk assessment result, and finally, perform permission control on the user operation based on the risk assessment result, thereby being able to combine the judgment of the network infrastructure failure to realize tamper-proof sharing of enterprise management information, thereby improving the accuracy of risk assessment and system security.

[0016] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the structures particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 This is a flow chart of an Internet-based enterprise management information tamper-proof sharing method according to an embodiment of the present invention; Figure 2 This is a structural diagram of an Internet-based enterprise management information tamper-proof sharing system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions, and advantages of this application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only intended to illustrate this application and are not intended to limit this application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements.

[0020] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0021] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0023] Before explaining the embodiments of the present application in detail, some of the nouns and terms involved in the embodiments of the present application are first explained. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0024] Enterprise information management system: refers to a system used to manage various enterprise information, such as management information systems, decision support systems, expert systems, various ERP systems, customer relationship management systems, human resource management systems, etc.

[0025] In related technologies, enterprises widely deploy and use internet-based enterprise information management systems in their daily operations. These systems carry a large amount of critical management information within the enterprise, such as product design documents, core financial data, marketing strategies, and important customer data. Enterprise employees are typically located across headquarters and numerous branches. With the prevalence of remote and mobile working, employees are increasingly accessing these information systems via the internet from diverse locations, using a variety of terminals, including personal devices, to perform various operations required by their job responsibilities, such as reviewing, editing, creating, deleting, and downloading management information. Therefore, enterprises have very clear and strict requirements for the integrity, confidentiality, and traceability of all operations of this management information. Their core security goal is to prevent unauthorized tampering, theft, or accidental destruction of this information during sharing, storage, and use.

[0026] To achieve refined and dynamic management of information access rights, traditional methods typically collect and analyze historical employee data over a period of time, including access time and duration, operation type distribution, access frequency, IP address location, terminal hardware fingerprint, and operating system type. This automatically builds a personalized baseline model of information access behavior for each employee, which is then periodically learned and adjusted based on subsequent compliance actions. When an employee attempts to operate information, the system verifies it based on pre-set roles, static permissions, and deviations from the baseline. The management system also assesses the security compliance status of the employee's current access device, including operating system patches, security software status, and malware indicators. Furthermore, information assets within the management system are classified into different sensitivity levels based on their importance and potential impact of a breach. Based on user behavior deviations, terminal device security status, and target information sensitivity, the dynamic permission policy engine automatically adjusts employee access rights, taking intervention measures such as downgrading permissions, disabling operations, additional authentication, or switching to manual approval. The entire process is automated to minimize response delays and maintain comprehensive audit records. Appeal and temporary exemption mechanisms are also provided to balance security control with business flexibility.

[0027] However, in scenarios where employees access geographically dispersed enterprise information management systems via the internet using a variety of terminals, including personal devices, to share and manipulate management information, including sensitive information, relying solely on deviation analysis of single-point behavior and static risk assessment dimensions is ineffective in addressing more complex and hidden risks. This can lead to misjudgments in risk assessment, impacting business continuity, and resulting in low risk assessment accuracy. This is particularly true when employee operations are context-dependent (i.e., individual operations are part of a business process, requiring analysis of a continuous sequence of operations), or when attackers may employ a series of seemingly independent but actually related low-risk operations to gradually achieve their malicious objectives. Traditional permission control methods present challenges in identifying and addressing these continuous and hidden operational risks. Furthermore, network failures can provide cover for potential attacks, potentially causing the system to miss high-risk operations, resulting in low system security. Furthermore, balancing strict information tampering security requirements with the smoothness and efficiency of employees' normal business operations while ensuring a complete record of all decision-making processes for tamper-proof audits, while carefully handling high-risk operational context fragments such as data write, modify, and delete instructions without immediately blocking potential compliance operations, and providing users with the opportunity to explain or cancel them, is a pressing technical challenge. Enterprises need to further improve the accuracy of identifying and handling complex, continuous attacks or misoperations while addressing challenges such as sudden changes in user behavior patterns and uncontrollable terminal security status.

[0028] To illustrate this issue more clearly, let's use an example. Consider a multinational enterprise whose external partner accesses sensitive project documents via the internet through its enterprise information management system. This external partner is located in an area with unstable network infrastructure. When a localized network failure occurs in that area, such as routing instability leading to data transmission delays and increased packet loss, the external partner attempts to modify a critical project document. The management system detects the user's action (document modification), but due to network anomalies, the data received by the management system may be incomplete or delayed. Traditional risk assessments may rely solely on historical user behavior and document sensitivity, ignoring the impact of current network conditions on risk assessment accuracy. Network failures can cause the management system to mistakenly interpret the user's action as abnormal (for example, a long response time could be misinterpreted as a malicious probe) or fail to obtain the complete and timely context for the action, making it impossible to accurately assess the true risk level of the modification in the current network environment.

[0029] Due to these issues, enterprise management information systems may fail to accurately assess the risks of user operations during network infrastructure outages. This can lead to two undesirable consequences: first, excessively restricting user permissions can lead to normal operations being misjudged as high-risk, impacting the normal business operations and collaboration efficiency of external partners; second, failing to identify truly risky operations, such as malicious tampering disguised as network anomalies, leading to the illegal modification or destruction of critical enterprise management information, compromising data integrity and potential financial losses. Furthermore, inaccurate risk assessment and permission control can distort audit records, hindering subsequent tracing and accountability.

[0030] In view of this, an embodiment of the present invention provides an Internet-based enterprise management information tamper-proof sharing method and system. In the specific scenario where enterprise employees use diverse terminals to access geographically dispersed enterprise management information systems that store sensitive information through the Internet to share and operate management information, the system can dynamically control read and write permissions. The system can dynamically adjust the weights of various risk factors based on user behavior baselines, real-time operation context fragments, terminal security status, data sensitivity level, operation environment characteristics, and the current network environment to perform accurate risk assessment. For example, when network transmission stability is poor, the weights of risk factors related to data integrity and operation timing can be increased; when network latency is high, the weights of risk factors related to operation response time can be adjusted. In this way, the risk assessment model can adapt to the current network environment, more accurately assess the risks of user operation behaviors under specific network failures, and perform corresponding permission control based on this. At the same time, a suspended execution and user interaction interpretation mechanism is introduced for high-risk change instructions such as write, modify, and delete, ultimately achieving a balance between ensuring information tamper-proof security and business operation fluency, and ensuring the traceability of the decision-making process.

[0031] The following is a detailed explanation of the embodiments of the present application with reference to the accompanying drawings: Figure 1 This is an optional flowchart of an Internet-based enterprise management information tamper-proof sharing method provided by an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0032] Step S101: Obtain network status information of an external partner's access area; Step S102: Determine whether there is a network infrastructure failure in the external partner access area based on the network status information, and obtain a network infrastructure failure determination result; Step S103: If the network infrastructure fault determination result is that a network infrastructure fault exists, user operation behavior data of the external partner access area is obtained; Step S104, adjust the weight of the risk assessment factor corresponding to the network state information to obtain a target weight; Step S105, according to the target weight, risk assessment is performed on the user operation behavior data to obtain a risk assessment result; Step S106, according to the risk assessment result, permission control is performed on the user operation.

[0033] The steps S101 to S106 shown in the embodiments of the present application can realize enterprise management information anti-tampering sharing in combination with the judgment of network infrastructure failure, and improve the risk assessment accuracy and system security.

[0034] In some embodiments, steps S101-S106, the embodiments of the present application combine the judgment of network infrastructure failure with dynamic weight adjustment of risk assessment factors, so that when there is an anomaly in the network environment, the potential risks of user operation can be more accurately identified, and the overall effect of the enterprise management information anti-tampering ability is improved. The network state information of the external partner access area can be obtained first. Exemplarily, the network state information can be data reflecting the network connection quality, stability and availability of the area, which can be realized by monitoring network delay, packet loss rate, bandwidth fluctuation or service connectivity, etc. Since the failure of network infrastructure can cause unstable data transmission, increased delay or connection interruption, these abnormal states can be exploited by malicious actors, or increase the risk of data damage caused by misoperation. According to the network state information, it can be determined whether there is a network infrastructure failure in the external partner access area, and a network infrastructure failure determination result is obtained. Exemplarily, the network infrastructure failure can include line interruption, device failure or congestion, which can be identified by setting a threshold, pattern recognition or comparison with known failure characteristics, etc.

[0035] If the network infrastructure failure determination result is that there is a network infrastructure failure, the user operation behavior data of the external partner access area is obtained. Exemplarily, the user operation behavior data can be the collection and recording of various operations and related information of the users in the enterprise management information system, such as operation type, time, object, source IP, etc., which can be obtained by log recording, traffic analysis or terminal monitoring, etc. Then the weight of the risk assessment factor corresponding to the network state information is adjusted to obtain a target weight. Exemplarily, when the network is unstable, the weight of the risk factor related to data integrity or operation sensitivity may be increased. This dynamic adjustment makes the risk assessment better reflect the actual risk level under the current network environment. The risk assessment factor can be various indicators for assessing the risk of user operation, such as operation type, data sensitivity, user historical behavior, etc.

[0036] Based on the target weights, a risk assessment is then performed on the user's operational behavior data to obtain a risk assessment result. Based on the risk assessment result, user operations are then subject to permission control. For example, blocking high-risk operations or requiring additional verification can effectively prevent tampering or sabotage that may occur during network failures. The risk assessment process can employ methods such as weighted summation, Bayesian networks, or machine learning models to calculate the risk level or likelihood of user operations, thereby quantifying the potential risk of user operations in the current environment. The permission control process can employ methods such as denying operations, reducing permissions, increasing verification, or recording audit logs to restrict or intervene in user attempts to perform operations, thereby limiting high-risk operations and ensuring information security.

[0037] To more clearly illustrate this technical solution, a specific example is provided below. Probe packets can be periodically sent to key network nodes in the external partner's access area, and the returned performance indicators, such as latency and packet loss rate, are collected as network status information. A threshold is then set as the criteria for determining a network infrastructure failure. For example, if the packet loss rate exceeds 20% or the average latency exceeds 500 milliseconds for three consecutive probes, a network infrastructure failure is determined. If a failure is determined, user operation behavior data is collected. This can include recording every file modification, deletion, upload, or download operation performed by a user in the enterprise information management system, along with the user identity, timestamp, target file path, and operation result. A mapping table is then pre-set to map different network failure types (e.g., high latency, high packet loss) or severity (e.g., minor, moderate, severe) to a set of weight adjustment parameters to adjust the weights of risk assessment factors. For example, when a moderate packet loss failure is detected, the weight of the risk factor "deletion type" is increased by 1.5 times, and the weight of the factor "accessing a top secret file" is increased by 2 times. Finally, a simple weighted summation model can be used to multiply the value of each risk factor by its adjusted weight and add the weighted risk factor values ​​to obtain an overall risk score. A risk score threshold can also be set. When the total risk score exceeds the risk score threshold, the operation is directly rejected. When the total risk score is less than the risk score threshold and greater than zero, a detailed audit log is recorded and an alert is sent to the administrator.

[0038] Through the above technical solutions, this embodiment can effectively solve the problem that traditional methods have difficulty in accurately assessing risks when there are faults in the network infrastructure. By dynamically adjusting the weights of risk assessment factors, this embodiment can make risk assessment more adaptable to abnormal network environments and improve the accuracy of risk identification. This enables more precise permission control of user operations when the network is unstable or there are potential risks, avoiding excessive restrictions on normal operations, while also effectively preventing malicious tampering or sabotage that may exploit network failures. This embodiment provides a more robust and adaptable solution for ensuring the anti-tampering security of enterprise management information.

[0039] In some embodiments, in step S104, the weight of the risk assessment factor corresponding to the network status information is adjusted to obtain a target weight, which may include but is not limited to steps S201 to S203.

[0040] Step S201: Acquire first attribute information of a network infrastructure fault, where the first attribute information is used to characterize the type or impact of the network infrastructure fault; Step S202: determining a target weight adjustment parameter corresponding to the first attribute information, where the target weight adjustment parameter is used to determine an adjustment method or adjustment range for each risk assessment factor weight; Step S203: According to the target weight adjustment parameter, the weight of the risk assessment factor corresponding to the network status information is adjusted to obtain the target weight.

[0041] In some embodiments, because the types and impacts of network infrastructure failures vary, simply uniformly adjusting the weights of risk assessment factors may not fully reflect the risk characteristics under different failure scenarios, resulting in inaccurate risk assessment results and affecting the effectiveness of permission control. To improve the accuracy of risk assessment results, this embodiment can first obtain first attribute information of the network infrastructure failure to identify the current specific failure scenario. The first attribute information is used to characterize the type or impact of the network infrastructure failure. It can be implemented using a fault type identifier, a fault impact range indicator, a fault duration record, or a fault severity code. Its purpose is to distinguish and quantify different faults.

[0042] Then a target weight adjustment parameter corresponding to the first attribute information is determined, wherein the target weight adjustment parameter is used to determine an adjustment manner or an adjustment range of the weight of each risk assessment factor, which can be realized by using a weight multiplier list, a weight offset set, an adjustment function model or an adjustment rule set, and the purpose thereof is to provide a customized weight adjustment strategy for different fault attributes. Then, according to the target weight adjustment parameter, the weight of the risk assessment factor corresponding to the network state information is adjusted to obtain a target weight. Exemplarily, for a wide-range network outage fault, the weight of the user behavior anomaly factor can need to be greatly increased; for a DNS hijacking fault, the weight of the terminal security factor can need to be increased. In this way, the embodiment overcomes the limitation of simply and uniformly adjusting the weight, so that the risk assessment can more accurately reflect the real risk in different network infrastructure fault scenarios, thereby improving the accuracy of risk assessment and the effectiveness of permission control after determining whether the network infrastructure fault exists in the access region of the external cooperation party and obtaining the user operation behavior data.

[0043] It can be understood that the adjustment manner or the adjustment range of the weight of each risk assessment factor refers to a method or a degree of actually performing the weight adjustment operation, which can be realized by directly adding, directly multiplying, mapping based on a lookup table or calculating through an algorithm model, and the purpose thereof is to apply the determined adjustment parameter to an actual weight value.

[0044] In order to more clearly illustrate the technical solution, specific examples are used for explanation below. When it is detected that the network infrastructure fault exists in the access region of the external cooperation party, for example, it is detected that the network delay is significantly increased and the packet loss rate is increased, the system obtains first attribute information of the fault. The first attribute information can include that the fault type is “network congestion / delay” and the influence degree is “high” (determined based on the packet loss rate and the delay threshold). Then, according to the first attribute information, a corresponding target weight adjustment parameter is found in a preset mapping relationship table. For example, for the “network congestion / delay, high influence” scenario, the preset parameter can indicate that the weights of the “user operation frequency anomaly” and the “operation geographic location deviation” risk assessment factors are respectively increased by 20% and 15%, and the weight of the “terminal device security score” factor is reduced by 5%. Then, according to these target weight adjustment parameters, the weights of the factors currently used for risk assessment are adjusted correspondingly to obtain a new set of target weights. For example, if the weight of the “user operation frequency anomaly” before adjustment is 0.15, after adjustment, it will become 0.15 * (1 + 0.20) = 0.18. In order to subsequently use this set of adjusted target weights to perform risk assessment in combination with the obtained user operation behavior data.

[0045] Through the above technical solution, this embodiment can fine-tune the weights of risk assessment factors according to the specific type and impact of network infrastructure failures, so that the risk assessment results can more accurately reflect the risk characteristics under different failure scenarios, thereby improving the accuracy of risk assessment and further improving the effectiveness of authority control based on risk assessment results.

[0046] In some embodiments, in step S202 , determining the target weight adjustment parameter corresponding to the first attribute information may include but is not limited to steps S301 to S303 .

[0047] Step S301: searching a preset fault scenario knowledge base for a fault scenario record that matches the first attribute information to obtain a fault scenario matching result; Step S302: If the fault scenario matching result is that there is no matching fault scenario record, generating a target weight adjustment parameter based on the first attribute information and a preset parameter adjustment strategy, where the preset parameter adjustment strategy includes a parameter derivation strategy or a parameter generalization strategy; Step S303: If the fault scenario matching result indicates that a matching fault scenario record exists, then extracting a pre-stored weight adjustment parameter in the fault scenario record from a preset fault scenario knowledge base as a target weight adjustment parameter.

[0048] In some embodiments, due to the diversity and complexity of network infrastructure failures, simply determining the target weight adjustment parameter based on the first attribute information may not reflect the actual risk situation to a high degree. For example, for new or rare types of failures, there may be a lack of corresponding weight adjustment experience, resulting in a low degree of conformity between the risk assessment results and the actual situation. In addition, the preset parameter adjustment strategy may have limitations when responding to changes in the situation and cannot adapt to the ever-changing network environment and attack methods. To improve adaptability to the network environment and attack methods, this embodiment can search for fault scenario records that match the first attribute information in a preset fault scenario knowledge base to obtain a fault scenario matching result. If the fault scenario matching result is that there is no matching fault scenario record, it indicates that the current fault may be a new or uncommon type. The target weight adjustment parameter is generated based on the first attribute information and the preset parameter adjustment strategy, where the preset parameter adjustment strategy includes a parameter derivation strategy or a parameter generalization strategy. If the fault scenario matching result is that there is a matching fault scenario record, the pre-stored weight adjustment parameter in the fault scenario record is extracted from the preset fault scenario knowledge base as the target weight adjustment parameter. This allows for the rapid application of parameter adjustment solutions that have been verified to achieve the desired results for previously encountered and handled fault types, thereby improving processing efficiency and the degree to which assessment results align with actual conditions. This embodiment, by combining knowledge base query and policy generation with branching processing logic, enables the determination of weight adjustment parameters that meet requirements for both known and unknown faults, providing more accurate input for subsequent risk assessments.

[0049] It can be understood that the parameter derivation strategy refers to a parameter adjustment strategy that can infer the required weight adjustment parameters by analyzing the relationship between fault attributes or their impact on network behavior. The parameter generalization strategy refers to another parameter adjustment strategy that can generate parameters by extending or applying the weight adjustment experience of known fault scenarios to similar unmatched fault scenarios.

[0050] To more clearly illustrate this technical solution, a specific example is provided below. Assume that a preset fault scenario knowledge base stores recommended risk assessment factor weight adjustment parameters for different fault types (e.g., link down, DNS hijacking, and DDoS attacks) occurring in different regions (e.g., headquarters, branch A, and branch B). When the system detects a link down at branch A and obtains its first attribute information (fault type: link down, affected region: branch A), the system first queries the knowledge base. If the knowledge base contains a record with the fault type "link down" and the affected region "branch A," and the record contains pre-stored weight adjustment parameters for this scenario (e.g., increasing the weight of the "connection stability" factor by 30% and decreasing the weight of the "data transmission rate" factor by 20%), these parameters are directly extracted as the target weight adjustment parameters. If the knowledge base does not have a complete matching record, for example, only a record for a "link down" occurring at headquarters or a record for a "DNS hijack" occurring at branch A, the system will apply the preset parameter adjustment policy. If a parameter generalization strategy is employed, the system might search for the known scenario most similar to "link outage at branch A" (for example, "link outage at headquarters"). Based on the size or business importance differences between branch A and headquarters, the system adjusts the pre-existing parameters for "link outage at headquarters" to generate target weight adjustment parameters for "link outage at branch A." ​​If a parameter derivation strategy is employed, the system might calculate a set of weight adjustment parameters based on the impact of the "link outage" fault type on network performance and the business characteristics of "branch A" using preset rules or models. Ultimately, whether through knowledge base extraction or policy generation, the target weight adjustment parameters for adjusting the weights of risk assessment factors are obtained.

[0051] Through the above technical solution, this embodiment utilizes a preset fault scenario knowledge base. For known network infrastructure fault scenarios, weight adjustment parameters that have been verified to achieve the expected results can be directly applied, thereby improving the efficiency of parameter determination and the degree of conformity of the evaluation results with the actual situation. At the same time, by introducing a preset parameter adjustment strategy, when encountering a new or rare fault for which there is no matching record in the knowledge base, the system can automatically generate weight adjustment parameters that meet the requirements based on the fault attribute information, thereby enhancing the solution's ability to cope with unknown situations and improving the adaptability of risk assessment. This makes the process of determining the target weight adjustment parameters more detailed and can be adjusted according to changes in the situation, thereby improving the degree of conformity of the risk assessment results with the actual situation and the ability to achieve the intended purpose.

[0052] In some embodiments, after performing risk assessment on the user operation behavior data according to the target weight and obtaining the risk assessment result, the method of this embodiment may further include but is not limited to steps S401 to S404.

[0053] Step S401: Obtain user feedback information corresponding to the risk assessment result; Step S402: Evaluate the effectiveness of the target weight adjustment parameter or the effectiveness of the preset parameter adjustment strategy based on the risk assessment result and user feedback information to obtain an effectiveness evaluation result; Step S403: If the effectiveness evaluation result shows that the target weight adjustment parameter has low effectiveness, the target weight adjustment parameter is adjusted online according to the effectiveness evaluation result and user feedback information; Step S404: If the effectiveness evaluation result shows that the preset parameter adjustment strategy has low effectiveness, the preset parameter adjustment strategy is optimized online according to the effectiveness evaluation result and user feedback information.

[0054] In some embodiments, due to the complexity of the network environment and the diversity of user behavior, the preset parameter adjustment strategy or the pre-stored weight adjustment parameters may not be able to fully adapt to all situations, resulting in deviations in the risk assessment results, affecting the accuracy of the authority control, and the effectiveness of the preset parameter adjustment strategy may also decrease over time. In order to reduce the deviation of the risk assessment results, this embodiment can first obtain user feedback information corresponding to the risk assessment results. These user feedback information can directly reflect the user's acceptance or objection to the risk assessment results and the subsequent authority control measures, providing an important external verification signal for the system. Then, based on the risk assessment results and user feedback information, the effectiveness of the target weight adjustment parameters or the effectiveness of the preset parameter adjustment strategy is evaluated to obtain an effectiveness evaluation result. For example, by comparing the risk level evaluated by the system with the degree of recognition of the evaluation implied by the user feedback, it can be determined whether the current parameters or strategies accurately identify the risks and whether there are false positives or false negatives, so as to obtain an effectiveness evaluation result.

[0055] If the effectiveness evaluation result shows that the target weight adjustment parameter has low effectiveness, it means that the currently used parameter value fails to accurately guide the risk assessment. Based on the effectiveness evaluation result and user feedback information, the target weight adjustment parameter can be adjusted online, such as fine-tuning the parameter value to make it more in line with the actual situation.

[0056] If the effectiveness evaluation result shows that the preset parameter adjustment strategy is of low effectiveness, it means that the strategy for generating parameters itself has defects or is no longer suitable for the current environment. Based on the effectiveness evaluation results and user feedback information, the preset parameter adjustment strategy can be optimized online, such as modifying the strategy logic or updating the model on which the strategy depends.

[0057] This embodiment incorporates user feedback and online adaptive optimization capabilities, enabling the system to not only quickly respond and adjust weights when a fault occurs, but also continuously optimize the weight adjustment parameters and strategies based on actual results and user feedback. This continuous optimization capability addresses the problem of preset parameters or strategies being unable to fully adapt to complex and changing environments, significantly improving the accuracy of risk assessments and the long-term adaptability of the system.

[0058] It can be understood that user feedback information refers to the user's direct or indirect response to the risk assessment results or the permission control measures triggered thereby. It can be achieved through complaints actively submitted by the user, the number of attempts made by the user to restrict operations recorded by the system, or the user's interactive behavior in response to system prompts. Its purpose is to introduce the user's perspective and provide external verification for evaluating the accuracy of system behavior.

[0059] To more clearly illustrate this technical solution, a specific example is provided below. Assume that after a network infrastructure failure occurs, the system generates target weight adjustment parameters based on the first attribute information of the failure using a preset parameter adjustment policy and adjusts the weights of the risk assessment factors accordingly. Then, based on the adjusted target weights, a risk assessment is performed on user operation behavior data. A certain user operation is assessed as high risk, triggering permission control measures, such as blocking the user's operation. The user believes the operation is compliant and submits user feedback through the system's feedback channels, claiming the risk assessment result is a false positive. Upon receiving the user feedback, the system compares and analyzes the high-risk risk assessment result with the user feedback (false positive complaint). If the analysis reveals a high frequency of similar user false positive complaints under the current target weight adjustment parameters, the system will assess the effectiveness of the current target weight adjustment parameters as low. Based on the false positive pattern and the details of the user feedback, the target weight adjustment parameters can be adjusted online. For example, if the false positive is caused by an overly high weight adjustment for a specific risk factor, the adjustment of that factor will be reduced. If the system analyzes and finds that the preset parameter adjustment strategy used to generate the target weight adjustment parameter (for example, consistently significantly increasing the weight of a risk factor for a certain fault type) leads to systematic misjudgments, the system will assess the effectiveness of the preset parameter adjustment strategy as low. Based on the evaluation results and user feedback, the system will perform online optimization of the preset parameter adjustment strategy. For example, the system may modify the strategy rules to more precisely determine the weight adjustment parameters for this fault type by incorporating more contextual information (such as historical user behavior and the sensitivity of the operation content).

[0060] Through the above technical solution, this embodiment can self-correct and improve according to actual operating results and user feedback, thereby improving the accuracy of risk assessment, reducing false positives and missed positives, and enabling the preset parameter adjustment strategy to better adapt to the ever-changing network environment and user behavior, thereby improving the adaptability of risk assessment.

[0061] In some embodiments, in step S402, the effectiveness of the target weight adjustment parameters or the effectiveness of the preset parameter adjustment strategy is evaluated based on the risk assessment results and user feedback information to obtain an effectiveness evaluation result, which may include but is not limited to steps S501 to S507.

[0062] Step S501: Analyze the frequency of false positives and the pattern of missed negatives in the risk assessment results to obtain a system-side effectiveness indication; Step S502: Process the user feedback information to obtain a user-side validity indication. The information processing includes classification, aggregation, and credibility evaluation. Step S503: Determine whether there is a conflict between the system-side validity indication and the user-side validity indication, and obtain a conflict determination result; Step S504: If the conflict determination result is that there is no conflict, then generate a validity evaluation result based on the system-side validity indication and the user-side validity indication; Step S505: If the conflict determination result is that a conflict exists, dynamic attribute information of the network infrastructure fault and historical interaction characteristics of the affected user group are obtained. The dynamic attribute information includes information about the fault evolution stage or changes in the fault impact range. The historical interaction characteristics include historical feedback accuracy information of the user group or information about the user group's sensitivity to system adjustments. Step S506: Conflict resolution is performed on the system-side validity indication and the user-side validity indication according to a preset conflict resolution strategy, dynamic attribute information, and historical interaction characteristics, to obtain a conflict resolution result. Step S507: Generate a validity evaluation result based on the conflict resolution result.

[0063] In some embodiments, there may be a conflict between the evaluation on the system side and the evaluation on the user side. Simply accepting the evaluation results of any party may lead to deviations, especially in the case of dynamic changes in network infrastructure failures and large differences in user groups, the deviation of the evaluation results is more significant. In order to generate more accurate effectiveness evaluation results, the frequency of false positives and the pattern of missed positives in the risk assessment results can be analyzed first to obtain a system-side effectiveness indication, which reflects the system's own judgment on the effectiveness of parameters or policies. Then, the user feedback information is processed to obtain a user-side effectiveness indication, wherein the information processing includes classification, aggregation and credibility assessment. For example, classification can distinguish feedback information by type, aggregation can summarize similar or related feedback information, and credibility assessment can judge the reliability of feedback information based on user historical feedback records or the feedback content itself. Its purpose is to extract valuable user-side effectiveness indications from original, scattered user feedback. And determine whether there is a conflict between the system-side effectiveness indication and the user-side effectiveness indication, and obtain a conflict determination result; If the conflict determination result is no conflict, an effectiveness evaluation result is generated based on the system-side effectiveness indicator and the user-side effectiveness indicator. If the conflict determination result is a conflict, dynamic attribute information of the network infrastructure fault and historical interaction characteristics of the affected user groups are obtained. Dynamic attribute information includes information about the fault's evolution stage or changes in the fault's impact range, describing the changes in the network infrastructure fault over time and providing context for understanding potential biases in the system-side assessment. Historical interaction characteristics include information about the accuracy of user group feedback or the user group's sensitivity to system adjustments, describing the characteristics of past interactions with the system by the user group affected by the network infrastructure fault and providing context for understanding potential biases in the user-side assessment. Finally, based on the preset conflict resolution strategy, dynamic attribute information, and historical interaction characteristics, conflict resolution is performed on the system-side effectiveness indicator and the user-side effectiveness indicator, such as weighted fusion or selective acceptance, to obtain a conflict resolution result. Based on this conflict resolution result, an effectiveness evaluation result is generated.

[0064] It can be understood that the preset conflict handling strategy refers to a pre-set set of rules or algorithms for handling conflicts between system-side validity indications and user-side validity indications. It can be implemented using rule-based decision trees, machine learning models or weighted fusion algorithms, etc. Its purpose is to comprehensively consider multiple factors to generate more accurate evaluation results when the two types of indications conflict.

[0065] This embodiment comprehensively considers the system's own judgment, user feedback, fault dynamics, and user group characteristics. It handles conflicting assessments between the system and user sides with greater precision and accuracy, avoiding biases caused by simplistic assumptions and generating more reliable effectiveness assessment results. This, in addition to adjusting weights based on network status for risk assessment and preliminary effectiveness evaluation, enhances the robustness and accuracy of the assessment process itself. This is particularly true in environments with complex and volatile network failures and user behavior, enabling more effective guidance for subsequent parameter or policy optimization and improving the accuracy of overall risk control.

[0066] In some embodiments, in step S505 , obtaining dynamic attribute information of the network infrastructure failure and historical interaction characteristics of the affected user group may include but is not limited to steps S601 to S604 .

[0067] Step S601: Acquire network performance data of the external partner access area; Step S602: Analyze network performance data to obtain dynamic attribute information; Step S603: Acquire historical operation data and historical feedback data of the affected user group; Step S604: Analyze historical operation data and historical feedback data to obtain historical interaction features.

[0068] In some embodiments, network performance data for the access area of ​​an external partner can be obtained and analyzed to obtain dynamic attribute information. Network performance data can reflect the operating status of the network in real time or near real time. By monitoring and analyzing changes in this data over time, the dynamic evolution of faults, such as occurrence, development, deterioration, or recovery, as well as the expansion or reduction of the fault impact range, can be accurately captured. Network performance data refers to a data set that characterizes the operating status and quality of the network, which can be measured using indicators including but not limited to network latency, packet loss rate, bandwidth utilization, and error rate.

[0069] Then, the historical operation data and historical feedback data of the affected user group are obtained and analyzed to obtain historical interaction features. Among them, historical operation data refers to the behavioral records generated by the affected user group when interacting with the system over a period of time. It can be recorded in the form of, but not limited to, login records, file access records, and function usage records. Historical feedback data refers to the information provided by the affected user group to the system or service provider regarding system operation, failures, or suggestions over a period of time. It can be recorded in the form of, but not limited to, user-submitted failure reports, consultation records, and satisfaction evaluations. The user's historical operation behavior in the system and the historical feedback information they provide contain information such as the user's acceptance of system adjustments, how they perceive failures, and the reliability of their feedback. Through in-depth mining and analysis of this historical data, a behavioral profile of the user group and a feedback credibility model can be constructed.

[0070] This embodiment comprehensively and dynamically acquires detailed information related to faults and users from both the system side (network performance data) and the user side (historical operation and feedback data), converting it into dynamic attribute information and historical interaction characteristics. This allows for subsequent conflict resolution based on a richer and more accurate data foundation when determining conflicts between system-side and user-side validity indications. This comprehensive data acquisition approach, combining system operating status and historical user behavior, overcomes the limitations of relying solely on a single information source or static information, providing a more comprehensive and accurate data foundation for subsequent conflict resolution between system-side and user-side validity indications, thereby improving the accuracy and rationality of conflict resolution.

[0071] In some embodiments, in step S506, conflict handling is performed on the system side validity indication and the user side validity indication according to a preset conflict handling strategy, dynamic attribute information and historical interaction characteristics to obtain a conflict handling result, which may include but is not limited to steps S701 to S707.

[0072] Step S701: Determine a conflict handling path based on a preset conflict handling strategy, dynamic attribute information, and historical interaction characteristics. The conflict handling path includes a parameterized adjustment path or a decision-making selection path. Step S702: If the conflict handling path is a parameterized adjustment path, determine a first adjustment coefficient corresponding to the system-side validity indication and a second adjustment coefficient corresponding to the user-side validity indication based on a preset conflict handling strategy, dynamic attribute information, and historical interaction characteristics; Step S703: Correct the first value of the system-side validity indicator according to the first adjustment coefficient; Step S704: Correct the second value of the user-side validity indicator according to the second adjustment coefficient; Step S705: Generate a conflict resolution result based on the corrected first value and the corrected second value; Step S706: If the conflict handling path is a decision-making selection path, determine the adoption decision label based on the preset conflict handling strategy, dynamic attribute information, and historical interaction characteristics; Step S707: According to the acceptance decision label, one indication is selected from the system-side validity indication and the user-side validity indication as the conflict resolution result.

[0073] In some embodiments, when a conflict occurs between system-side and user-side validity indications, simply combining the two or adopting one over the other may not accurately reflect the actual situation, resulting in biased validity assessment results, which in turn affects subsequent parameter adjustments or policy optimizations, ultimately reducing the performance of the entire risk assessment system. To effectively handle conflicts, a conflict resolution path can be determined based on a preset conflict resolution strategy, dynamic attribute information, and historical interaction characteristics, thereby providing refined conflict resolution. This conflict resolution path includes either a parameterized adjustment path or a decision-making selection path.

[0074] If the conflict handling path is a parameterized adjustment path, then the first adjustment coefficient corresponding to the system-side validity indication and the second adjustment coefficient corresponding to the user-side validity indication are determined according to the preset conflict handling strategy, dynamic attribute information and historical interaction characteristics. The first value of the system-side validity indication is corrected according to the first adjustment coefficient, and the second value of the user-side validity indication is corrected according to the second adjustment coefficient. The conflict handling result is generated based on the corrected first value and the corrected second value. If the conflict handling path is a decision-making selection path, then the acceptance decision label is determined according to the preset conflict handling strategy, dynamic attribute information and historical interaction characteristics, and one indication is selected from the system-side validity indication and the user-side validity indication as the conflict handling result according to the acceptance decision label. The dynamic processing method based on context and historical data enables more accurate and reliable conflict handling results to be generated when the system-side and user-side indications conflict.

[0075] It is understood that the preset conflict handling strategy refers to a set of pre-set rules, models or algorithms used to guide the conflict handling process. It can be implemented using a rule-based expert system, machine learning model or fuzzy logic system. Its purpose is to provide a basis for handling conflicts between system-side and user-side indications. The parameterized adjustment path refers to a processing path that weights or corrects the values ​​of system-side and user-side indications by calculating adjustment coefficients and then fuses them. Its purpose is to flexibly quantitatively adjust the two indications. The decision-making selection path refers to a processing path that directly selects one of the system-side or user-side indications as the final result based on specific rules or conditions. Its purpose is to directly adopt the more reliable one in certain situations. The acceptance decision label is an identifier used to indicate whether the system-side validity indication or the user-side validity indication should be ultimately adopted. Its purpose is to clarify the final choice of conflict handling.

[0076] Through the above technical solution, when a conflict occurs between system-side and user-side validity indications, a parameterized adjustment or decision-making selection process can be selected based on the preset conflict resolution strategy, dynamic attribute information, and historical interaction characteristics. The corresponding correction or selection operation can then be performed, resulting in a more accurate conflict resolution result. This resolves the problem of biased evaluation results that can result from simplistic conflict resolution, improves the accuracy and reliability of validity assessment results, and can more effectively guide subsequent parameter adjustments or policy optimization.

[0077] In some embodiments, in step S705 , a conflict resolution result is generated based on the corrected first value and the corrected second value, which may include but is not limited to steps S801 to S804 .

[0078] Step S801: Determine a first fusion weight corresponding to the corrected first value and a second fusion weight corresponding to the corrected second value according to a preset fusion strategy; Step S802: multiply the corrected first value by the first fusion weight to obtain a first multiplication result; Step S803: multiply the corrected second value by the second fusion weight to obtain a second multiplication result; Step S804: Add the first multiplication result and the second multiplication result to obtain a conflict resolution result.

[0079] In some embodiments, since the values are only obtained by simple averaging or direct selection, the differences in credibility of the system side and the user side indications cannot be fully considered, which may result in inaccurate fusion results and cannot maximize the use of the corrected information, thereby affecting the accuracy of the final effectiveness evaluation. To improve the accuracy of the fusion results, the first fusion weight corresponding to the corrected first value and the second fusion weight corresponding to the corrected second value can be determined according to a preset fusion strategy. For example, the preset fusion strategy can be to dynamically adjust the weights according to external information, such as dynamic attribute information and historical interaction features, so that the indications with higher credibility obtain greater weights. Then, the corrected first value is multiplied by the first fusion weight to obtain a first multiplication result, and the corrected second value is multiplied by the second fusion weight to obtain a second multiplication result, which is a weighting process that reflects the differences in contributions of different indications in the fusion. The first multiplication result and the second multiplication result are then added to obtain a conflict processing result, which comprehensively reflects the evaluation information of the system side and the user side. In this embodiment, after determining the parameterized adjustment path and correcting the values, the weighted fusion method can more finely process the corrected information, dynamically adjust the weights, so that the fusion result more accurately reflects the actual situation, improves the accuracy of the conflict processing result, and further improves the accuracy of the subsequent effectiveness evaluation.

[0080] Through the above technical solutions, under the parameterized adjustment path, the fusion weights of the system side and the user side effectiveness indications can be dynamically determined according to the preset fusion strategy, and weighted fusion can be performed. This weighted fusion method can fully consider the credibility of the system side and the user side indications, thereby overcoming the bias that may be caused by simple averaging or direct selection, so that the indications with higher credibility have a greater proportion in the final result, thereby generating more accurate conflict processing results and improving the accuracy of the effectiveness evaluation.

[0081] In some embodiments, in step S801, according to the preset fusion strategy, the first fusion weight corresponding to the corrected first value and the second fusion weight corresponding to the corrected second value can be determined, which can include the following steps: According to the preset fusion strategy, the dynamic attribute information and the historical interaction features, the first fusion weight and the second fusion weight are determined.

[0082] In some embodiments, a basic framework provided by a preset fusion strategy can be utilized, which defines the basic rules or model for weight determination and combines dynamic attribute information and historical interaction characteristics. By comprehensively considering this information, the first fusion weight and the second fusion weight can be dynamically adjusted. For example, when the fault is expanding and user feedback may be lagging, the first fusion weight can be appropriately increased; when the user group's historical feedback is accurate and sensitive to system adjustments, the second fusion weight can be appropriately increased. This dynamic adjustment mechanism enables the weighted fusion of the revised first value and the revised second value to more reasonably reflect the relative credibility of the system-side effectiveness indication and the user-side effectiveness indication in the current context, and more reasonably allocate the proportion of the system-side effectiveness indication and the user-side effectiveness indication in the fusion process, thereby generating a more accurate conflict handling result. This overcomes the bias in the fusion result that may be caused by the use of static or simple preset weights, and improves the accuracy and reliability of the effectiveness evaluation results generated when there is a conflict between the system-side effectiveness indication and the user-side effectiveness indication.

[0083] The beneficial effects of implementing the embodiments of the present invention include: the embodiments of the present application first obtain the network status information of the external partner access area, then determine whether there is a network infrastructure failure in the external partner access area, and obtain a network infrastructure failure determination result; if the network infrastructure failure determination result is that there is a network infrastructure failure, then obtain the user operation behavior data of the external partner access area, and adjust the weight of the risk assessment factor corresponding to the network status information to obtain the target weight, and then perform a risk assessment on the user operation behavior data to obtain a risk assessment result; finally, based on the risk assessment result, perform permission control on the user operation, thereby being able to combine the judgment of the network infrastructure failure to realize tamper-proof sharing of enterprise management information, thereby improving the accuracy of risk assessment and system security.

[0084] like Figure 2 As shown, an embodiment of the present invention further provides an Internet-based enterprise management information tamper-proof sharing system, comprising: The network status information acquisition module 901 is used to obtain the network status information of the access area of ​​the external partner; A fault determination module 902 is configured to determine whether there is a network infrastructure fault in the external partner access area based on the network status information, and obtain a network infrastructure fault determination result; The user behavior data acquisition module 903 is configured to acquire user operation behavior data in the external partner access area if the network infrastructure fault determination result is that a network infrastructure fault exists; A weight adjustment module 904 is used to adjust the weight of the risk assessment factor corresponding to the network status information to obtain a target weight; The risk assessment module 905 is used to perform risk assessment on the user operation behavior data according to the target weight and obtain a risk assessment result; The authority control module 906 is used to perform authority control on user operations based on risk assessment results.

[0085] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0086] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0087] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

Claims

1. A method for sharing enterprise management information based on the Internet to prevent tampering, characterized in that: The following steps are involved: Obtain network status information of the access area of ​​external partners; Determining, based on the network status information, whether there is a network infrastructure failure in the access area of ​​the external partner, and obtaining a network infrastructure failure determination result; If the network infrastructure fault determination result is that a network infrastructure fault exists, obtaining user operation behavior data of the external partner access area; Adjusting the weight of the risk assessment factor corresponding to the network status information to obtain a target weight; Performing a risk assessment on the user operation behavior data according to the target weight to obtain a risk assessment result; Based on the risk assessment results, user operations are subject to permission control.

2. The method according to claim 1, characterized in that The adjusting the weight of the risk assessment factor corresponding to the network status information to obtain a target weight includes: Acquire first attribute information of the network infrastructure fault, where the first attribute information is used to characterize a type or impact degree of the network infrastructure fault; Determining a target weight adjustment parameter corresponding to the first attribute information, wherein the target weight adjustment parameter is used to determine an adjustment method or adjustment range of each risk assessment factor weight; According to the target weight adjustment parameter, the weight of the risk assessment factor corresponding to the network status information is adjusted to obtain the target weight.

3. The method according to claim 2, characterized in that The determining of the target weight adjustment parameter corresponding to the first attribute information includes: Retrieving a fault scenario record matching the first attribute information from a preset fault scenario knowledge base to obtain a fault scenario matching result; If the fault scenario matching result is that there is no matching fault scenario record, generating the target weight adjustment parameter according to the first attribute information and a preset parameter adjustment strategy, wherein the preset parameter adjustment strategy includes a parameter derivation strategy or a parameter generalization strategy; If the fault scenario matching result is that there is a matching fault scenario record, then a pre-stored weight adjustment parameter in the fault scenario record is extracted from the preset fault scenario knowledge base as the target weight adjustment parameter.

4. The method according to claim 3, characterized in that After performing a risk assessment on the user operation behavior data according to the target weight and obtaining a risk assessment result, the method further includes: Obtaining user feedback information corresponding to the risk assessment result; Evaluate the effectiveness of the target weight adjustment parameter or the preset parameter adjustment strategy based on the risk assessment result and the user feedback information to obtain an effectiveness evaluation result; If the effectiveness evaluation result shows that the target weight adjustment parameter has low effectiveness, performing online adjustment processing on the target weight adjustment parameter according to the effectiveness evaluation result and the user feedback information; If the effectiveness evaluation result shows that the effectiveness of the preset parameter adjustment strategy is low, then the preset parameter adjustment strategy is optimized online according to the effectiveness evaluation result and the user feedback information.

5. The method according to claim 4, characterized in that The evaluating, based on the risk assessment result and the user feedback information, the effectiveness of the target weight adjustment parameter or the effectiveness of the preset parameter adjustment strategy to obtain the effectiveness evaluation result includes: Analyzing the frequency of false positives and patterns of missed negatives in the risk assessment results to obtain an indication of system-side effectiveness; Processing the user feedback information to obtain a user-side validity indication, wherein the information processing includes classification, aggregation, and credibility evaluation; Determining whether there is a conflict between the system-side validity indication and the user-side validity indication, and obtaining a conflict determination result; If the conflict determination result is that there is no conflict, generating the validity evaluation result according to the system-side validity indication and the user-side validity indication; If the conflict determination result is that a conflict exists, dynamic attribute information of the network infrastructure fault and historical interaction characteristics of the affected user group are obtained, wherein the dynamic attribute information includes information on the fault evolution stage or information on changes in the fault impact scope, and the historical interaction characteristics include historical feedback accuracy information of the user group or information on the user group's sensitivity to system adjustments; Performing conflict resolution on the system-side validity indication and the user-side validity indication according to a preset conflict resolution strategy, the dynamic attribute information, and the historical interaction characteristics to obtain a conflict resolution result; The validity evaluation result is generated according to the conflict handling result.

6. The method according to claim 5, characterized in that The acquisition of dynamic attribute information of the network infrastructure failure and historical interaction characteristics of the affected user groups includes: Obtaining network performance data of the access area of ​​the external partner; Analyzing the network performance data to obtain the dynamic attribute information; Obtain historical operation data and historical feedback data of the affected user groups; The historical operation data and the historical feedback data are analyzed to obtain the historical interaction features.

7. The method according to claim 5, characterized in that The performing conflict handling on the system-side validity indication and the user-side validity indication according to the preset conflict handling strategy, the dynamic attribute information, and the historical interaction characteristics to obtain a conflict handling result includes: Determining a conflict handling path according to the preset conflict handling strategy, the dynamic attribute information, and the historical interaction characteristics, wherein the conflict handling path includes a parameterized adjustment path or a decision-making selection path; If the conflict handling path is the parameterized adjustment path, determining a first adjustment coefficient corresponding to the system-side validity indication and a second adjustment coefficient corresponding to the user-side validity indication according to the preset conflict handling strategy, the dynamic attribute information, and the historical interaction characteristics; modifying a first value of the system-side effectiveness indicator according to the first adjustment coefficient; Correcting the second value of the user-side validity indicator according to the second adjustment coefficient; generating the conflict resolution result according to the corrected first value and the corrected second value; If the conflict handling path is the decision-making selection path, determining a decision label based on the preset conflict handling strategy, the dynamic attribute information, and the historical interaction characteristics; According to the adoption decision label, one indication is selected from the system-side validity indication and the user-side validity indication as the conflict handling result.

8. The method according to claim 7, characterized in that Generating the conflict resolution result according to the corrected first value and the corrected second value includes: Determining, according to a preset fusion strategy, a first fusion weight corresponding to the revised first value and a second fusion weight corresponding to the revised second value; multiplying the corrected first value by the first fusion weight to obtain a first multiplication result; multiplying the corrected second value by the second fusion weight to obtain a second multiplication result; The first multiplication result and the second multiplication result are added to obtain the conflict handling result.

9. The method according to claim 8, characterized in that The determining, according to a preset fusion strategy, a first fusion weight corresponding to the corrected first value and a second fusion weight corresponding to the corrected second value includes: The first fusion weight and the second fusion weight are determined according to the preset fusion strategy, the dynamic attribute information, and the historical interaction characteristics.

10. An Internet-based enterprise management information tamper-proof sharing system, characterized in that: include: The network status information acquisition module is used to obtain the network status information of the access area of ​​the external partner; a fault determination module, configured to determine, based on the network status information, whether there is a network infrastructure fault in the access area of ​​the external partner, and obtain a network infrastructure fault determination result; A user behavior data acquisition module is configured to acquire user operation behavior data in the external partner access area if the network infrastructure fault determination result is that a network infrastructure fault exists; A weight adjustment module, configured to adjust the weight of the risk assessment factor corresponding to the network status information to obtain a target weight; A risk assessment module, configured to perform a risk assessment on the user operation behavior data according to the target weight to obtain a risk assessment result; The authority control module is used to perform authority control on user operations based on the risk assessment results.

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