Goose epidemic disease prevention monitoring data sharing system and method

Abstract

The invention provides a goose epidemic disease prevention monitoring data sharing system and method, and the method comprises the steps: monitoring a sharing record of a sharing node in a goose epidemic disease prevention data sharing network in real time, and further extracting the trust attribute of the sharing node during the data sharing of goose epidemic disease prevention monitoring data from the sharing record; performing structural association analysis on all the trust attributes to obtain association confidence among the shared nodes, and determining a node response state of the epidemic prevention data sharing network according to all the association confidence and the sharing level of each shared node; based on the node response state, trust evaluation is carried out on the sharing behavior of the monitoring end, and the behavior trust degree of data sharing of the monitoring end is obtained; and when the data is shared next time, based on the minimum permission principle, the sharing permission of the monitoring end in the data sharing process is restrained through the behavior credibility. By adopting the scheme of the invention, the adaptive constraint of the sharing authority in the sharing node can be realized, so that the sharing security of the goose epidemic disease prevention monitoring data can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of data sharing, and more specifically, to a goose disease prevention and control monitoring data sharing system and method. Background Art

[0002] Goose disease prevention and control monitoring data provides a scientific basis for early warning of diseases, analysis of transmission trends and formulation of prevention and control strategies. It can help farmers identify and respond to potential disease risks in a timely manner, and effectively reduce economic losses and public health risks caused by diseases. Through systematic data monitoring and management, accurate and timely sharing of epidemic prevention and control monitoring data can help relevant departments respond quickly and take effective prevention and control measures, thereby achieving comprehensive control of diseases and enhancing the overall health level of the goose flock.

[0003] In the prevention and control of goose disease, data sharing is crucial to improving the efficiency of disease monitoring and prevention. However, the current data sharing system has significant deficiencies in authority management and trust assessment, which limits its effectiveness in practical applications. First, the data sharing system lacks an effective sharing authority management mechanism, which makes sensitive data face the risk of leakage or abuse, especially in the goose disease epidemic prevention and monitoring data sharing network with multiple parties. Different sharing nodes involve different data sharing access requirements. If their sharing permissions cannot be reasonably constrained, the data will be improperly accessed or tampered with, affecting the accuracy and timeliness of epidemic prevention decisions. Secondly, there are usually differences in spatial relationships between shared nodes in the data sharing network, which makes the real-time monitoring and evaluation of the trust attributes of each shared node complicated. The current sharing system relies on a static trust mechanism and fails to dynamically adjust the trust level according to real-time sharing records and node behaviors, which greatly reduces the security of the sharing system. Therefore, how to achieve adaptive constraints on sharing permissions in shared nodes, so as to improve the security of goose disease epidemic prevention and monitoring data sharing, has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a goose disease prevention and control monitoring data sharing system and method, which can realize adaptive constraints on sharing permissions in shared nodes, thereby improving the security of goose disease prevention and control monitoring data sharing.

[0005] In a first aspect, the present application provides a method for sharing goose disease prevention and control monitoring data, comprising the following steps: Constructing a goose epidemic prevention data sharing network, and initially assigning a sharing level to each sharing node in the epidemic prevention data sharing network; Monitor the shared records of the shared nodes in the epidemic prevention data sharing network in real time, determine the trust attribute of each shared node in the goose epidemic prevention monitoring data when sharing data according to the spatial relationship between the shared records and each shared node, and then perform structural association analysis on the trust attributes between each shared node to obtain the association confidence between each shared node; Based on the fuzzy evaluation mechanism, the node response status of the epidemic prevention data sharing network is determined according to all associated confidences and the sharing level of each shared node; After each data sharing of the monitoring end is completed, a trust evaluation is performed on the sharing behavior of the monitoring end based on the node response status to obtain the trust degree of the data sharing behavior of the monitoring end; When the monitoring end shares goose disease prevention and control monitoring data next time, the sharing authority of the monitoring end in the data sharing process is constrained based on the principle of least privilege and the behavioral trust.

[0006] Preferably, determining the trust attribute of each shared node in goose disease prevention and control monitoring data during data sharing based on the spatial relationship between the shared record and each shared node specifically includes: Extracting the interaction frequency and sharing quality between each sharing node from the sharing record; Determine the trust reward factor for each sharing node behavior in the epidemic prevention data sharing network based on the interaction frequency and sharing quality between each sharing node; Determine the topological distance matrix of the epidemic prevention data sharing network based on the spatial relationship between each shared node; The trust attribute of each shared node of goose disease prevention and control monitoring data during data sharing is determined through all trust reward factors and the topological distance matrix.

[0007] Preferably, performing structural correlation analysis on the trust attributes between the shared nodes to obtain correlation confidence between the shared nodes specifically includes: Perform structural hierarchical clustering on the trust attributes of all shared nodes to obtain shared node clusters of different trust levels; Associating the trust attributes between the shared nodes in each shared node cluster, and then extracting the trust variance of each shared node cluster; Determine the trust weight of each shared node cluster according to the proportion of the trust variance corresponding to each shared node cluster; The association confidence between each shared node is determined based on all trust weights and the association relationship between each shared node.

[0008] Preferably, based on the fuzzy evaluation mechanism, determining the node response state of the epidemic prevention data sharing network according to all associated confidences and the sharing level of each shared node specifically includes: Initializing the membership of each shared node according to the shared level of each shared node; Perform fuzzification processing on all associated confidences to obtain all associated confidences after fuzzification; According to the membership of all shared nodes, all associated confidences after fuzzification are fuzzily evaluated to generate a fuzzy evaluation matrix, and the node response status of the epidemic prevention data sharing network is reflected through the fuzzy evaluation matrix.

[0009] Preferably, trust evaluation is performed on the sharing behavior of the monitoring terminal based on the node response status to obtain the trust degree of the behavior of data sharing of the monitoring terminal, which specifically includes: Extract behavioral features of shared behaviors of monitoring end; Determining a trust evaluation parameter of a shared node corresponding to the monitoring terminal according to the node response status; The behavior characteristics are evaluated according to the trust evaluation parameters to obtain the behavior trust of the monitoring end data sharing.

[0010] Preferably, based on the principle of least privilege combined with the behavior trust constraint monitoring terminal sharing authority in the data sharing process specifically includes: Obtain the target permissions and redundant permissions of the monitoring end during the data sharing process; Based on the principle of least privilege and the trust level of the behavior, the redundant privileges are constrained to obtain constrained redundant privileges; The sharing authority of the monitoring terminal in the data sharing process is determined according to the target authority and the redundant authority of the constraint.

[0011] Preferably, the sharing record includes: data sharing frequency and number of times, shared data type, and the time when the sharing behavior occurs.

[0012] In the second aspect, the present application provides a goose disease prevention and control monitoring data sharing system, comprising: A construction module is used to construct a goose epidemic prevention data sharing network and initially allocate a sharing level of each sharing node in the epidemic prevention data sharing network; A processing module is used to monitor the shared records of the shared nodes in the epidemic prevention data sharing network in real time, determine the trust attribute of each shared node in the goose epidemic prevention monitoring data when sharing data according to the spatial relationship between the shared records and each shared node, and then perform structural association analysis on the trust attributes between each shared node to obtain the association confidence between each shared node; The processing module is further used to determine the node response state of the epidemic prevention data sharing network based on a fuzzy evaluation mechanism according to all associated confidences and the sharing level of each shared node; The processing module is further used to perform a trust evaluation on the sharing behavior of the monitoring terminal based on the node response state after each data sharing of the monitoring terminal is completed, so as to obtain the trust degree of the data sharing behavior of the monitoring terminal; The execution module is used to constrain the sharing authority of the monitoring terminal in the data sharing process based on the principle of least authority combined with the behavioral trust when the monitoring terminal shares goose disease prevention and control monitoring data next time.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned method for sharing goose disease prevention and monitoring data.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for sharing goose disease prevention and monitoring data.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In an embodiment of the present application, a goose disease prevention data sharing network is first constructed, and the sharing level of each shared node in the epidemic prevention data sharing network is initially assigned; the sharing records of the shared nodes in the epidemic prevention data sharing network are monitored in real time, and the trust attributes of each shared node in the goose disease epidemic prevention monitoring data during data sharing are determined according to the spatial relationship between the shared records and each shared node, and then the trust attributes between each shared node are structurally associated with each other. Analysis is performed to obtain the association confidence between each shared node; based on a fuzzy evaluation mechanism, the node response state of the epidemic prevention data sharing network is determined according to all the association confidences and the sharing level of each shared node; after each data sharing at the monitoring end is completed, a trust evaluation is performed on the sharing behavior of the monitoring end based on the node response state to obtain the behavioral trust of the data sharing at the monitoring end; when the monitoring end shares the goose disease epidemic prevention monitoring data next time, the sharing authority of the monitoring end in the data sharing process is constrained based on the principle of least authority combined with the behavioral trust.

[0016] It can be seen that the present application performs a trust evaluation on the sharing behavior of the monitoring end based on the node response status, obtains the behavioral trust of the monitoring end data sharing, and further performs target constraints on the sharing permissions in the data sharing process based on the principle of least privilege combined with the behavioral trust; wherein, firstly, the trust attributes of each sharing node are determined according to the spatial relationship between the shared records and the shared nodes, and then the structural association analysis is performed on the trust attributes between the shared nodes, and the fuzzy evaluation mechanism is used to quantify the association confidence between the shared nodes. By analyzing the connection and interaction between the shared nodes, it is helpful to understand the influence and relationship strength between different shared nodes, and can provide data support for subsequent permission management and trust evaluation, so that each data sharing can be carried out on the basis of a full evaluation of the trust, thereby optimizing the sharing permissions of the nodes in the data sharing; then, according to the association confidence and sharing The sharing level of the node determines the node response status of the epidemic prevention data sharing network, and further trusts the behavior of the shared node based on the node response status to ensure that subsequent sharing behavior is subject to reasonable authority constraints. This dynamic trust evaluation mechanism not only makes up for the shortcomings of the traditional static trust mechanism, but also enhances the adaptability of the data sharing system when facing multi-party participation, so that the sharing behavior between different shared nodes can reflect its trust status in real time, thereby effectively reducing the risk of improper access or tampering with data; finally, by introducing the minimum authority mechanism, the potential security risks in data sharing are effectively reduced, ensuring that access to sensitive data is limited to necessary personnel and scenarios, and improving the security and reliability of the entire epidemic prevention data sharing network; in summary, the present application scheme can realize adaptive constraints on shared permissions in shared nodes, thereby improving the security of goose epidemic prevention monitoring data sharing. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flow chart of a method for sharing goose disease prevention and monitoring data according to some embodiments of the present application; Figure 2 It is a schematic diagram of the structure of the epidemic prevention data sharing network shown in some embodiments of the present application; Figure 3 is a schematic diagram of a process for determining association confidence according to some embodiments of the present application; Figure 4 It is a structural schematic diagram of a goose disease prevention and monitoring data sharing system according to some embodiments of the present application; Figure 5 It is a structural schematic diagram of a computer device for implementing a method for sharing goose disease prevention and monitoring data according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is an exemplary flow chart of a method for sharing goose disease prevention and control monitoring data according to some embodiments of the present application. The method 100 for sharing goose disease prevention and control monitoring data mainly includes the following steps: In step 101, a goose epidemic prevention data sharing network is constructed, and the sharing level of each sharing node in the epidemic prevention data sharing network is initially allocated.

[0020] In specific implementation, the construction of goose epidemic prevention data sharing network can be realized in the following ways, namely: Figure 2 As shown, this figure is a schematic diagram of the structure of the epidemic prevention data sharing network in some embodiments of the present application, wherein 1, 2, 3, 4, and 5 all represent shared nodes. First, the goose disease epidemic prevention monitoring site (monitoring end) can be registered as a shared node of the epidemic prevention data sharing network, and then the geographical distance between the goose disease epidemic prevention monitoring sites is used as the trust weight between the shared nodes (i.e., the connecting line), and then the network composed of all registered goose disease epidemic prevention monitoring sites is used as the epidemic prevention data sharing network; the initial allocation of the sharing level of each shared node in the epidemic prevention data sharing network can be achieved in the following manner, namely: The sharing behavior evaluation rule is set to regularly evaluate the historical sharing behavior of each sharing node in the epidemic prevention data sharing network, and then the sharing level of the corresponding sharing node in the epidemic prevention data sharing network is initially assigned according to the evaluation results. For example, the evaluation results can be divided into 4 grades. When the evaluation result is excellent, the sharing level of the sharing node can be assigned to 0.9; when the evaluation result is good, the sharing level of the sharing node can be assigned to 0.8; when the evaluation result is medium, the sharing level of the sharing node can be assigned to 0.7; when the evaluation result is poor, the sharing level of the sharing node can be assigned to 0.6.

[0021] In step 102, the shared records of the shared nodes in the epidemic prevention data sharing network are monitored in real time, and the trust attribute of each shared node of the goose epidemic prevention monitoring data during data sharing is determined according to the spatial relationship between the shared records and each shared node, and then the trust attributes between each shared node are structurally analyzed to obtain the correlation confidence between each shared node; It should be noted that in this application, real-time monitoring of the shared records of shared nodes in the epidemic prevention data sharing network refers to continuous and dynamic tracking and recording of various behaviors and related data of each shared node in the epidemic prevention data sharing network during the data sharing process. The shared records include: data sharing frequency and number, shared data type (such as health status), and the time when the shared behavior occurs.

[0022] In some embodiments, the following steps may be used to determine the trust attribute of each shared node in goose disease epidemic prevention monitoring data when data is shared based on the spatial relationship between the shared record and each shared node: Extracting the interaction frequency and sharing quality between each sharing node from the sharing record; Determine the trust reward factor for each sharing node behavior in the epidemic prevention data sharing network based on the interaction frequency and sharing quality between each sharing node; Determine the topological distance matrix of the epidemic prevention data sharing network based on the spatial relationship between each shared node; The trust attribute of each shared node of goose disease prevention and control monitoring data during data sharing is determined through all trust reward factors and the topological distance matrix.

[0023] It should be noted that the sharing quality in this application refers to the integrity, accuracy and effectiveness of goose disease prevention and control monitoring data during the sharing process. The sharing quality can be used to measure the quality of goose disease prevention and control monitoring data when shared, transmitted and used by different nodes. In addition, first, a unique verification code for shared data can be generated by a hash algorithm in the prior art. After data sharing is completed, the recipient can regenerate a hash value for the data, compare the hash value with the original hash value, and use the comparison result (i.e., the hash verification value) as the integrity of quantitative data sharing. Then, the currently shared goose disease prevention and control monitoring data can be verified for similarity through existing historical data, and the calculated similarity can be used as an indicator of quantitative data sharing accuracy (i.e., accuracy verification value). Finally, business logic rules can be applied to the shared data to check its validity, such as data format, range, logical consistency, etc. The validity verification is performed through the rule engine, and the quantitative value of the validity verification (i.e., validity verification value) is output.

[0024] In specific implementation, the following methods can be used to extract the interaction frequency and sharing quality between each sharing node from the shared record, namely: the number of data sharing between sharing nodes can be used as the interaction frequency between sharing nodes, and the sharing quality between sharing nodes can be evaluated according to the integrity, accuracy and validity of the shared data. For example, the sum of the hash check value, the accuracy check value and the validity check value can be used as a quantitative indicator of the sharing quality between sharing nodes; the trust reward factor of each sharing node behavior in the epidemic prevention data sharing network can be determined based on the interaction frequency and sharing quality between each sharing node. The following methods can be used: the interaction frequency and sharing quality between sharing nodes can be used to evaluate the sharing quality between sharing nodes. A trust reward factor for the corresponding behavior is assigned to each sharing node, where the trust reward factor can be calculated by weighted average, for example: trust reward factor = first weight * interaction frequency + second weight * quantitative index of sharing quality, where the first weight and the second weight are weight coefficients. In the embodiment of the present application, the first weight and the second weight are both 0.5. In other embodiments, the first weight and the second weight can also be adaptively allocated according to the importance of the interaction frequency and the sharing quality, which is not limited here; it should be noted that the trust reward factor in the present application is an indicator to measure the credibility of the sharing node when sharing data, reflecting the reliability of the sharing node in the data exchange process.

[0025] In addition, in the specific implementation, the topological distance matrix of the epidemic prevention data sharing network determined based on the spatial relationship between each shared node can be implemented in the following way, namely: first, the confidence distance between the shared nodes can be calculated according to the geographical location and network path between the shared nodes, and the confidence distance = geographical location * network path complexity. The network path complexity can be quantified by the number of hops in the existing indicators, that is, the average confidence distance between each shared node and other shared nodes can be calculated, and the average confidence distance is used as the topological distance of each shared node, so as to obtain the topological distance of all shared nodes, and all topological distances are calculated according to the corresponding shared nodes. The spatial positions are arranged into a matrix, and the arranged matrix is ​​used as the topological distance matrix of the epidemic prevention data sharing network; the trust attribute of each shared node of the goose epidemic prevention monitoring data during data sharing is determined by all the trust reward factors and the topological distance matrix, which can be implemented in the following way, namely: select a shared node as the selected shared node, obtain the topological distance of the selected shared node from the topological distance matrix, and then use the product of the inverse of the topological distance of the selected shared node and the reward factor of the behavior trust of the selected shared node as the trust attribute of the selected shared node, so as to obtain the trust attribute of each shared node of the goose epidemic prevention monitoring data during data sharing.

[0026] It should be noted that the trust attribute in this application is a feature that measures the reliability of a shared node in data sharing. The trust attribute can be used to evaluate the reliability of a shared node in the process of information sharing, collaboration or interaction.

[0027] In some embodiments, reference Figure 3 As shown, this figure is a schematic diagram of the process of determining the association confidence in some embodiments of the present application. In this embodiment, the trust attributes between each shared node are structurally analyzed, and the association confidence between each shared node can be obtained by the following steps: In step 1021, the trust attributes of all shared nodes are clustered in a structural hierarchical manner to obtain shared node clusters of different trust levels; In step 1022, the trust attributes between the shared nodes in each shared node cluster are associated, thereby extracting the trust variance of each shared node cluster; In step 1023, the trust weight of each shared node cluster is determined according to the proportion of the trust variance corresponding to each shared node cluster; In step 1024, the association confidence between each shared node is determined according to all trust weights and the association relationship between each shared node.

[0028] In the specific implementation, first, the hierarchical clustering algorithm in the prior art can be used to perform structural clustering analysis on the trust attributes of shared nodes, and all shared nodes can be divided into different clusters according to the similarity between the trust attributes. In the embodiment of the present application, all shared nodes can be divided into 3 clusters, representing high trust, medium trust and low trust respectively. It should be noted that the Euclidean distance can be used to measure the similarity of trust attributes between shared nodes in the present application. In other embodiments, other methods can be used for implementation, which are not limited here. In addition, the present application can cluster shared nodes with similar trust in the same cluster through hierarchical clustering, which is helpful for structured trust analysis and reveals the trust pattern and hierarchical structure between shared nodes; secondly, in each shared node cluster, the difference value between the trust attributes of each pair of shared nodes in the shared node cluster is calculated, and the variance is calculated based on these difference values, and the variance is used as the trust variance of the shared node cluster. The trust variance reflects the discrete degree of trust of shared nodes in a shared node cluster, and the trust variance of all shared node clusters can be obtained; then, the sum of the trust variances of all shared node clusters is taken as the sum of trust variances, and the ratio between the trust variance of a shared node cluster and the sum of trust variances is taken as the trust weight of the shared node cluster; finally, the trust attributes of all shared nodes are obtained, and for each shared node cluster, the absolute difference in trust attributes between shared nodes in the shared node cluster is taken as the associated trust value between shared nodes, and the product of the associated trust value and the trust weight of the shared node cluster is taken as the associated confidence between shared nodes in the shared node cluster, thereby obtaining the associated confidence between shared nodes in all shared node clusters; it should be noted that the associated confidence between shared nodes that are not in the same shared node cluster is set to the default associated confidence, and the default associated confidence is usually the minimum value of all associated confidences.

[0029] It should be noted that the association confidence in the present application refers to a quantitative indicator of the degree of mutual trust formed between every two shared nodes in a shared network based on a trust relationship. It reflects the confidence level of one shared node in another shared node. The larger the association confidence, the higher the confidence level between the shared nodes, and the smaller the association confidence, the lower the confidence level between the shared nodes.

[0030] In step 103, based on the fuzzy evaluation mechanism, the node response status of the epidemic prevention data sharing network is determined according to all associated confidences and the sharing level of each shared node.

[0031] In some embodiments, based on the fuzzy evaluation mechanism, determining the node response status of the epidemic prevention data sharing network according to all associated confidences and the sharing level of each shared node can be achieved by the following steps: Initializing the membership of each shared node according to the shared level of each shared node; Perform fuzzification processing on all associated confidences to obtain all associated confidences after fuzzification; According to the membership of all shared nodes, all associated confidences after fuzzification are fuzzily evaluated to generate a fuzzy evaluation matrix, and the node response status of the epidemic prevention data sharing network is reflected through the fuzzy evaluation matrix.

[0032] In specific implementation, first, an initial membership can be allocated to each shared node according to the sharing level of the shared node. The initial membership represents the initial degree of belonging of each shared node at different trust levels (for example, high, medium, and low trust). A score interval can be set for each level, and a distribution function (such as a triangular membership function) can be used to allocate the membership. The initial membership value is generally in the interval [0, 1]. In the embodiment of the present application, the initial membership assigned to the high trust level is 0.9, the initial membership assigned to the medium trust level is 0.6, and the initial membership assigned to the low trust level is 0.3. In other embodiments, the allocation of the initial membership can also be allocated in other ways, which are not limited here; then, the fuzzy algorithm in the prior art can be used to allocate The association confidence between shared nodes is fuzzified to express the fuzziness of the association confidence. The fuzzification process can be implemented through a fuzzy membership function to map the association confidence to a fuzzy set. For example, a triangular membership function can be used to fuzzify each association confidence into fuzzy levels such as "high trust", "medium trust", and "low trust", and the trust membership is calculated separately. It should be noted that the present application can more effectively represent different levels of association confidence through fuzzification, so that the evaluation system can still have a strong resolution ability when the data is uncertain; finally, a fuzzy trust evaluation of each shared node to other shared nodes is generated through fuzzy operations (such as the averaging method), and the matrix composed of all fuzzy trust scores is used as the fuzzy evaluation matrix. The element M of the fuzzy evaluation matrix ij It represents the fuzzy trust evaluation of shared node i on shared node j, and its value M ij =The trust membership between shared node i and shared node j*(the absolute value of the initial membership between shared node i and shared node j); It should be noted that the node response status in this application reflects the state characteristics of the shared nodes in the data sharing and interaction process in the epidemic prevention data sharing network.

[0033] In step 104, after each data sharing of the monitoring end is completed, a trust evaluation is performed on the sharing behavior of the monitoring end based on the node response status to obtain the trust degree of the data sharing behavior of the monitoring end.

[0034] In some embodiments, trust evaluation is performed on the sharing behavior of the monitoring terminal based on the node response status, and the trust degree of the behavior of data sharing of the monitoring terminal can be obtained by the following steps: Extract behavioral features of shared behaviors of monitoring end; Determining a trust evaluation parameter of a shared node corresponding to the monitoring terminal according to the node response status; The behavior characteristics are evaluated according to the trust evaluation parameters to obtain the behavior trust of the monitoring end data sharing.

[0035] In specific implementation, first, a feature extraction model can be used to extract relevant behavior features from the shared records of the monitoring end, such as data sharing frequency, shared data integrity, shared data accuracy, sharing success rate and data timeliness, and can also include specific abnormal behaviors, such as sharing failure rate and delay anomaly; secondly, an evaluation model can be initialized, and the node response state (i.e., fuzzy evaluation matrix) is used as a priori parameter of the evaluation model to evaluate the trust level of the monitoring end behavior. Then, an evaluation model suitable for trust evaluation is selected, such as a fuzzy comprehensive evaluation model based on fuzzy logic. During initialization, the input variables (e.g., shared behavior features) and output variables (e.g., behavior trust scores) of the model are defined, and the values ​​in the fuzzy evaluation matrix are used as the initial parameters of the model to set the prior conditions of the model. For example, in the fuzzy comprehensive evaluation model, each trust membership in the fuzzy evaluation matrix is ​​used as the initial membership parameter; finally, the evaluation model is used to dynamically evaluate the behavior trust of the monitoring end behavior features, that is, the evaluation model will output the behavior trust of the monitoring end data sharing according to the defined relationship.

[0036] It should be noted that, in the embodiment of the present application, after each data sharing behavior is completed, the evaluation model can calculate and update the behavior trust of the monitoring end according to the updated behavior feature input and the prior parameters of the fuzzy evaluation matrix; it should also be noted that the behavior trust in the present application is an indicator to measure the reliability of the sharing behavior of the shared node. The greater the behavior trust, the higher the reliability of the sharing behavior of the shared node, and the smaller the behavior trust, the lower the reliability of the sharing behavior of the shared node.

[0037] In step 105, when the monitoring terminal shares goose disease prevention and control monitoring data next time, the sharing authority of the monitoring terminal in the data sharing process is constrained based on the principle of least authority combined with the behavior trust.

[0038] In some embodiments, based on the principle of least privilege combined with the behavior trust level, the sharing privileges of the monitoring terminal during data sharing can be implemented by the following steps: Obtain the target permissions and redundant permissions of the monitoring end during the data sharing process; Based on the principle of least privilege and the trust level of the behavior, the redundant privileges are constrained to obtain constrained redundant privileges; The sharing authority of the monitoring terminal in the data sharing process is determined according to the target authority and the redundant authority of the constraint.

[0039] In the specific implementation, first, the target sharing permissions of the monitoring terminal can be extracted from the sharing permission management system, and the target sharing permissions include target permissions and redundant permissions, wherein the target permissions are the basic permissions required by the monitoring terminal to complete the sharing task, and the redundant permissions are some permissions that exceed the task requirements; then, a mapping rule between behavior trust and residual permissions can be designed first, and the sharing nodes with higher behavior trust can retain higher redundant permissions, and the sharing nodes with lower behavior trust can retain lower redundant permissions or even retain redundant permissions, and further the redundant permissions of the sharing constraints can be obtained according to the designed mapping rules; finally, the sum of the target permissions and the redundant permissions of the constraints can be used as the sharing permissions of the monitoring terminal in the data sharing process.

[0040] It should be noted that the present application scheme has established a basic framework by constructing a goose epidemic prevention data sharing network and preliminarily assigning sharing levels to each shared node, providing a basis for subsequent permission management. This measure not only helps to clearly divide the access rights of different nodes, but also can dynamically adjust sharing permissions according to the importance and sensitivity of the nodes to reduce the risk of data leakage; secondly, the scheme monitors the sharing records of shared nodes in real time, and determines the trust attributes of each shared node based on the spatial relationship between the shared records and the nodes, which significantly improves the accuracy of trust evaluation. This dynamic trust evaluation mechanism not only makes up for the shortcomings of the traditional static trust mechanism, but also enhances the flexibility and adaptability of the system when facing multi-party participation, so that the sharing behavior between different nodes can reflect its trust status in real time, thereby effectively reducing inconsistencies. When the risk of accessing or tampering with data; secondly, the scheme conducts structural correlation analysis on the trust attributes between each shared node, and uses a fuzzy evaluation mechanism to quantify the correlation confidence of each node, so as to achieve accurate judgment of the node response status. Such structured analysis not only improves the security of data sharing, but also provides a basis for subsequent sharing behaviors, so that each data sharing can be carried out on the basis of fully evaluating the trust, thereby optimizing the decision-making process of data sharing; finally, after data sharing at the monitoring end, trust evaluation is performed on the sharing behavior based on the node response status to ensure that subsequent sharing behaviors are subject to reasonable authority constraints and implement the principle of least authority. This mechanism effectively reduces potential security risks, ensures that access to sensitive data is limited to necessary personnel and scenarios, and improves the security and reliability of the entire shared network. In summary, this technical solution effectively improves the security and efficiency of goose epidemic prevention and control monitoring data sharing through measures such as adaptive authority management, dynamic trust evaluation and structural correlation analysis, and provides a solid technical guarantee for epidemic prevention and control.

[0041] On the other hand, in some embodiments, the present application provides a goose disease prevention and control monitoring data sharing system, referring to Figure 4 , which is a structural schematic diagram of a goose disease prevention and control monitoring data sharing system according to some embodiments of the present application. The goose disease prevention and control monitoring data sharing system 400 includes: a construction module 401, a processing module 402 and an execution module 403, which are respectively described as follows: Construction module 401, in this application, construction module 401 is mainly used to construct a goose epidemic prevention data sharing network, and initially allocate a sharing level of each sharing node in the epidemic prevention data sharing network; Processing module 402, in the present application, the processing module 402 is used to monitor the shared records of the shared nodes in the epidemic prevention data sharing network in real time, determine the trust attribute of each shared node of the goose epidemic prevention monitoring data when sharing data according to the spatial relationship between the shared records and each shared node, and then perform structural association analysis on the trust attributes between each shared node to obtain the association confidence between each shared node; In the present application, the processing module 402 is also used to determine the node response status of the epidemic prevention data sharing network based on the fuzzy evaluation mechanism according to all the associated confidences and the sharing level of each shared node; The processing module 402 in the present application is also used to perform a trust evaluation on the sharing behavior of the monitoring end based on the node response status after each data sharing of the monitoring end is completed, so as to obtain the trust degree of the data sharing behavior of the monitoring end; Execution module 403, in this application, execution module 403 is mainly used for constraining the sharing authority of the monitoring terminal in the data sharing process based on the principle of least authority combined with the behavioral trust when the monitoring terminal shares goose disease prevention and control monitoring data next time.

[0042] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores code, and the processor is configured to obtain the code and execute the above-mentioned goose disease prevention and monitoring data sharing method.

[0043] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a method for sharing goose disease prevention and monitoring data according to some embodiments of the present application. A method for sharing goose disease prevention and monitoring data in the above embodiment can be performed by Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0044] The processor 501 may be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0045] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0046] The memory 503 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0047] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. A method for sharing goose disease prevention and monitoring data in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0048] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0049] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0050] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0051] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned goose disease prevention and monitoring data sharing method.

[0052] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for sharing goose disease prevention and monitoring data, characterized in that: The steps include: Constructing a goose epidemic prevention data sharing network, and initially assigning a sharing level to each sharing node in the epidemic prevention data sharing network; Monitor the shared records of the shared nodes in the epidemic prevention data sharing network in real time, determine the trust attribute of each shared node in the goose epidemic prevention monitoring data when sharing data according to the spatial relationship between the shared records and each shared node, and then perform structural association analysis on the trust attributes between each shared node to obtain the association confidence between each shared node; Based on the fuzzy evaluation mechanism, the node response status of the epidemic prevention data sharing network is determined according to all associated confidences and the sharing level of each shared node; After each data sharing of the monitoring end is completed, a trust evaluation is performed on the sharing behavior of the monitoring end based on the node response status to obtain the trust degree of the data sharing behavior of the monitoring end; When the monitoring end shares goose disease prevention and control monitoring data next time, the sharing authority of the monitoring end in the data sharing process is constrained based on the principle of least privilege and the behavioral trust.

2. The method according to claim 1, characterized in that Determining the trust attribute of each shared node of the goose epidemic prevention monitoring data during data sharing based on the spatial relationship between the shared record and each shared node specifically includes: Extracting the interaction frequency and sharing quality between each sharing node from the sharing record; Determine the trust reward factor for each sharing node behavior in the epidemic prevention data sharing network based on the interaction frequency and sharing quality between each sharing node; Determine the topological distance matrix of the epidemic prevention data sharing network based on the spatial relationship between each shared node; The trust attribute of each shared node of goose disease prevention and control monitoring data during data sharing is determined through all trust reward factors and the topological distance matrix.

3. The method according to claim 1, characterized in that The structural correlation analysis is performed on the trust attributes between each shared node, and the correlation confidence between each shared node is obtained, which specifically includes: Perform structural hierarchical clustering on the trust attributes of all shared nodes to obtain shared node clusters of different trust levels; Associating the trust attributes between the shared nodes in each shared node cluster, and then extracting the trust variance of each shared node cluster; Determine the trust weight of each shared node cluster according to the proportion of the trust variance corresponding to each shared node cluster; The association confidence between each shared node is determined based on all trust weights and the association relationship between each shared node.

4. The method according to claim 1, characterized in that Based on the fuzzy evaluation mechanism, the node response status of the epidemic prevention data sharing network is determined according to all associated confidences and the sharing level of each shared node, specifically including: Initializing the membership of each shared node according to the shared level of each shared node; Perform fuzzification processing on all associated confidences to obtain all associated confidences after fuzzification; According to the membership of all shared nodes, all associated confidences after fuzzification are fuzzily evaluated to generate a fuzzy evaluation matrix, and the node response status of the epidemic prevention data sharing network is reflected through the fuzzy evaluation matrix.

5. The method according to claim 1, characterized in that Based on the node response status, the sharing behavior of the monitoring terminal is trusted and the trust degree of the monitoring terminal data sharing behavior is obtained, which specifically includes: Extract behavioral features of shared behaviors of monitoring end; Determining a trust evaluation parameter of a shared node corresponding to the monitoring terminal according to the node response status; The behavior characteristics are evaluated according to the trust evaluation parameters to obtain the behavior trust of the monitoring end data sharing.

6. The method according to claim 1, characterized in that Based on the principle of least privilege and combined with the above behavior trust, the sharing permissions of the monitoring end in the data sharing process specifically include: Obtain the target permissions and redundant permissions of the monitoring end during the data sharing process; Based on the principle of least privilege and the trust level of the behavior, the redundant privileges are constrained to obtain constrained redundant privileges; The sharing authority of the monitoring terminal in the data sharing process is determined according to the target authority and the redundant authority of the constraint.

7. The method according to claim 1, characterized in that The sharing record includes: data sharing frequency and number of times, shared data type, and the time when the sharing behavior occurs.

8. A goose disease prevention and control monitoring data sharing system, characterized in that: include: A construction module is used to construct a goose epidemic prevention data sharing network and initially allocate a sharing level of each sharing node in the epidemic prevention data sharing network; A processing module is used to monitor the shared records of the shared nodes in the epidemic prevention data sharing network in real time, determine the trust attribute of each shared node in the goose epidemic prevention monitoring data when sharing data according to the spatial relationship between the shared records and each shared node, and then perform structural association analysis on the trust attributes between each shared node to obtain the association confidence between each shared node; The processing module is further used to determine the node response state of the epidemic prevention data sharing network based on a fuzzy evaluation mechanism according to all associated confidences and the sharing level of each shared node; The processing module is further used to perform a trust evaluation on the sharing behavior of the monitoring terminal based on the node response state after each data sharing of the monitoring terminal is completed, so as to obtain the trust degree of the data sharing behavior of the monitoring terminal; The execution module is used to constrain the sharing authority of the monitoring terminal in the data sharing process based on the principle of least authority and the behavioral trust when the monitoring terminal shares the goose epidemic prevention monitoring data next time.

9. A computer device, comprising a memory and a processor, wherein the memory stores a code, characterized in that: The processor is configured to obtain the code and execute a goose disease prevention and monitoring data sharing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, a method for sharing goose disease prevention and monitoring data as described in any one of claims 1 to 7 is implemented.

Images