A software query information management method and system

Abstract

The invention discloses a software query information management method and system, belonging to the field of software information security technology, and specifically comprises: after receiving a user's query request, creating a dynamic topology network architecture according to the semantic features of the query content; creating a virtual query container corresponding to the dynamic topology network in an isolated memory space, the virtual query container generating a dynamic encryption channel bound to the current topology structure; dynamically embedding user authority labels into topology network nodes through an attribute-based encryption algorithm, and establishing a mapping relationship between authority features and network paths; when executing distributed queries in the virtual query container, dynamically adjusting the topology node connection weights according to the real-time network load; before feeding back the query results to the user, recombining the data elements based on the reverse entropy value of the original topology network architecture; and triggering the dynamic invalidation mechanism of the topology network structure when abnormal access features are detected.

Description

Technical Field

[0001] The present invention relates to the technical field of software information security, and in particular to a software query information management method and system. Background Art

[0002] In today's digital age, software query information management plays a vital role in all fields. Whether it is business operations, scientific research or government administration, it is inseparable from an efficient and secure software query information management system. However, with the rapid development of information technology and the explosive growth of data volume, existing software query information management methods and systems are facing many severe challenges.

[0003] Traditional software query information management methods often adopt a static topology network architecture. This architecture shows obvious limitations when facing ever-changing query requirements and complex network environments. Static topology networks lack flexibility and are difficult to dynamically adjust according to real-time query semantics, resulting in low query efficiency. And because the topology network structure is fixed, the connection relationship between nodes and the data transmission path are relatively stable, which is easy to be identified and exploited by attackers. Once an attacker obtains part of the system information, it is possible to infer the storage location and transmission path of sensitive data by analyzing the network structure, thereby carrying out malicious behaviors such as data theft and tampering. Moreover, traditional encryption methods usually use fixed keys, and their security is difficult to be effectively guaranteed for increasingly complex attack methods. Summary of the invention

[0004] The purpose of the present invention is to provide a software query information management method and system to solve the following technical problems:

[0005] Existing software query information management methods and systems have limitations in flexibility and security.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A software query information management method comprises the following steps:

[0008] S1. After receiving a query request from a user, a dynamic topology network architecture is created based on the semantic features of the query content, wherein each node in the topology network architecture is generated by cross-calculating the query parameter hash value and the timestamp factor;

[0009] S2. Creating a virtual query container corresponding to the dynamic topology network in the isolated memory space, the virtual query container generates a dynamic encryption channel bound to the current topology structure, and each dynamic encryption channel is assigned an independent key matrix;

[0010] S3, dynamically embed user permission labels into topological network nodes through attribute-based encryption algorithm, and establish a mapping relationship between permission features and network paths;

[0011] S4. When executing distributed queries in the virtual query container, the topology node connection weights are dynamically adjusted according to the real-time network load, and sensitive data blocks are preferentially migrated to low-weight paths;

[0012] S5. Before feeding back the query results to the user, the data elements are recombined based on the reverse entropy value of the original topological network architecture to generate an output form that is decoupled from the initial network structure;

[0013] S6. When abnormal access features are detected, the dynamic invalidation mechanism of the topological network structure is triggered, so that all node connection relationships are unbound according to the preset attenuation function within the specified time window.

[0014] As a further solution of the present invention: it also includes establishing a full-link tracking log, encoding the topological network snapshot and the key matrix of the virtual container in time and space association at each user query request, and generating an audit evidence chain that cannot be tampered with.

[0015] As a further solution of the present invention: in S1, the process of creating a dynamic topology network architecture is:

[0016] The query request is analyzed to include the entity objects and operation types involved, and the fingerprint features representing their semantics are extracted. The extracted feature vector is input into the chaotic function to generate an initial network seed. According to the period value of the system clock, the network seed is subjected to fractal iterative operation. Finally, a three-dimensional topological network with non-Euclidean geometric characteristics is generated, and each network node is provided with a dynamic addressing identifier and a self-destruction timer.

[0017] As a further solution of the present invention: in S2, the process of creating the virtual query container is:

[0018] Create a virtual memory space that is not associated with the physical storage address, establish a container boundary protection wall, and divide the protection wall into multiple layers of isolation cabins. Each isolation cabin corresponds to a substructure branch of the topological network. The dynamic encryption channel uses the chameleon hash algorithm to generate path keys. Each element in the key matrix corresponds to the coordinates of the topological node in a nonlinear mapping relationship.

[0019] As a further solution of the present invention: In S3, the process of dynamically embedding the user authority label into the topology network node is:

[0020] The user's role attributes are converted into multi-dimensional feature vectors, the Lorenz attractor model is used to generate the path of permission diffusion, the permission verification trigger is implanted in the topological network node, the dynamic association between permission characteristics and network path curvature is established, and the path of unauthorized access is automatically distorted through the Möbius transformation.

[0021] As a further solution of the present invention: in S5, the process of recombining the data elements is as follows:

[0022] S51. Count the real-time number of connections and weight distribution of each node in the topological network, and generate a degree heat map based on the number of connections and weight distribution; calculate the dynamic activity coefficient of each path based on the query frequency and response delay of each path in the network history data, use the discrete Ricci flow algorithm to calculate the local curvature of the network, and identify high-density interaction clusters and isolated nodes;

[0023] S52. Calculate the influence weight of each data unit in the topological network structure according to the degree heat map and the dynamic activity coefficient. When there is a query request, parse the semantic label of the query request, quantify the semantic association strength of the data unit through the natural language processing model, and build a time decay function by combining the timestamp factor and the data update frequency. Dynamically adjust the weight of the data unit according to the timestamp factor and the data update frequency. The older the data unit is and the less updated it is, the lower its weight is. Fusion of structural entropy, semantic entropy and time series entropy generates a composite entropy weight vector as the priority basis for data element reorganization.

[0024] S53, mapping the nodes in the original topological network to the hyperplanes of the mirror space, and mapping the edges of the original topological network points to the intersection lines of the hyperplanes; performing nonlinear amplification on the data units above the entropy weight threshold according to the composite entropy weight vector; deploying a quantum noise layer at the edge of the mirror space to fuzzy the data below the entropy weight threshold;

[0025] S54, using data units above the entropy weight threshold as anchor points, using the geodesic algorithm to generate the minimum energy path to connect key data clusters; transforming the linear path into a fractal structure through conformal transformation on the Riemann manifold; performing topological dimension reduction on the reorganized fractal structure, stripping off the geometric association between the reorganized data and the original network, and generating a flattened output data stream;

[0026] S55, perform reverse hash verification on the output data stream and the original topology network; if the verification fails, trigger the dynamic correction mechanism of the entropy weight vector, and re-execute the steps S52 to S54 to reorganize the data; finally output the irreversible reorganized data packet carrying the time and space mark.

[0027] As a further solution of the present invention: in S6, when an abnormal access feature is detected, the dynamic invalidation mechanism of the topological network structure is triggered, so that all node connection relationships are unbound according to a preset attenuation function within a specified time window. The specific process is:

[0028] The abnormal resonance frequency of all topological network nodes is monitored in real time. When the failure rate of the node exceeds the dynamic threshold, cascade failure is triggered, and the signal of structural disintegration is propagated along the topological connection path. The optimal failure path is calculated according to the Zermelo navigation problem model, and the entropy increase and dissipation of the network structure are completed in the Riemann manifold space.

[0029] The present invention also includes a software query information management system, which is used to implement the above-mentioned software query information management method, including:

[0030] A dynamic topology generator is used to create a dynamic topology network architecture based on the semantic features of the query content after receiving a query request from the user. Each node in the topology network architecture is generated by cross-calculating the query parameter hash value and the timestamp factor.

[0031] A virtual container manager, used to create a virtual query container corresponding to the dynamic topology network in an isolated memory space, the virtual query container generates a dynamic encryption channel bound to the current topology structure, and each dynamic encryption channel is assigned an independent key matrix;

[0032] The permission topology embedding device is used to dynamically embed the user permission label into the topology network node through the attribute-based encryption algorithm to establish a mapping relationship between the permission feature and the network path;

[0033] Node weight regulator, which is used to dynamically adjust the topological node connection weights according to the real-time network load when executing distributed queries in the virtual query container, and sensitive data blocks are preferentially migrated to low-weight paths;

[0034] The data reorganization engine is used to reorganize the data elements based on the reverse entropy value of the original topological network architecture before feeding back the query results to the user, generating an output form that is decoupled from the initial network structure;

[0035] The failure trigger module is used to trigger the dynamic failure mechanism of the topological network structure when abnormal access features are detected, so that all node connection relationships are unbound according to the preset attenuation function within the specified time window.

[0036] Beneficial effects of the present invention:

[0037] The present invention creates a dynamic topological network of nodes generated by cross-calculation of query parameter hash values ​​and timestamp factors according to the semantic features of the query content, and cooperates with a dynamic encryption channel generated by a virtual query container that is bound to the topological structure and has an independent key matrix, thereby enhancing the security of information during transmission; an attribute-based encryption algorithm is used to embed user authority tags to establish a mapping relationship between authority and network paths, automatically distort unauthorized access paths, and trigger a dynamic invalidation mechanism of the topological network structure to release node connection relationships according to a preset attenuation function when abnormal access is detected, thereby further ensuring data security; data elements are recombined based on reverse entropy values ​​and reverse hash verification is performed before the query result is fed back, thereby increasing the difficulty of data cracking while ensuring information integrity; in a distributed During query, the connection weights of topological nodes are dynamically adjusted according to the real-time network load, and sensitive data is preferentially migrated to low-weight paths to avoid network congestion and improve query efficiency. By calculating the structural entropy, semantic entropy and temporal entropy, a composite entropy weight vector is generated to reorganize data elements and improve query accuracy and flexibility. A full-link tracking log is established to perform spatiotemporal correlation encoding of the topological network snapshot and the virtual container key matrix to generate an unalterable audit evidence chain for easy auditing and tracing. In addition, the unique non-Euclidean three-dimensional topological network design, mirror space projection and fractal structure processing make the network structure more complex and flexible, increase the complexity of data association, facilitate data processing and transmission, and significantly improve the security, flexibility and reliability of software query information management as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] Figure 1 It is a schematic diagram of the process of the present invention;

[0040] Figure 2 It is a module schematic diagram of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] See also Figure 1 As shown, the present invention is a software query information management method, comprising the following steps:

[0043] S1. Dynamic topology network architecture creation:

[0044] When the system receives a query request from a user, it does not immediately start the regular data query operation, but first deeply analyzes the semantic features contained in the query content. This process is like a linguist interpreting the deep meaning behind a piece of text. The system needs to accurately grasp the true intention of the user's query. The query content may contain a variety of different information, such as specific keywords, related topic descriptions, etc. The system will carefully analyze this information and extract the key elements that can represent the query semantics.

[0045] Based on these semantic features, the system begins to create a dynamic topological network architecture. This network architecture is like a complex and orderly urban transportation network. Each node is an important location in the city, and they are interconnected through roads (connection relationships). Each node here is generated in a unique way. Specifically, it is obtained by cross-operating the hash value of the query parameter with the timestamp factor. The hash value is like the "digital fingerprint" of the query parameter, which is unique and deterministic; the timestamp factor records the specific time when the query occurs, giving the node the characteristics of the time dimension. The nodes generated by this cross-operation make the entire topological network architecture dynamic and unique, and can change according to different query requests and time changes.

[0046] S2. Construction of virtual query container and encrypted channel:

[0047] After creating the dynamic topology network architecture, the system will create a virtual query container corresponding to the topology network in the isolated memory space. The isolated memory space is like an independent "safe house" that is isolated from other parts of the system and can effectively prevent external interference and attacks. The virtual query container is a special area in this "safe house" that is specifically used to process user query requests.

[0048] The virtual query container generates dynamic encrypted channels that are tightly bound to the current topology. These encrypted channels are like secure tunnels, and data is strictly protected when it is transmitted in them. Each dynamic encrypted channel is assigned an independent key matrix, which is like the "access code" of the tunnel. Only information with the correct password can be safely transmitted in the channel. In this way, even if the data is intercepted during transmission, the attacker cannot decipher the content without the correct key matrix, which greatly improves the security of data transmission.

[0049] S3. User permission label embedding and mapping relationship establishment:

[0050] To ensure that only users with the appropriate permissions can access specific data, the system dynamically embeds the user's permission label into the topology network node through the attribute-based encryption algorithm. The attribute-based encryption algorithm is an advanced encryption technology that can encrypt and decrypt data based on different user attributes (such as role, permission level, etc.). The user's permission label is like a "key". Only users with the right "key" can open the corresponding "door" (access specific data).

[0051] After embedding the permission label into the topological network node, the system will establish a mapping relationship between the permission feature and the network path. This is like in a large library, different book areas correspond to different borrowing permissions, and readers need to follow a specific path according to their permissions to reach the corresponding book area. Through this mapping relationship, the system can accurately control the user's access path and scope to the data, further enhancing the security of the data and the accuracy of access control.

[0052] S4. Topological node connection weight adjustment and sensitive data migration:

[0053] When executing distributed queries in a virtual query container, the system monitors the network load in real time. The network load is like the traffic on the road. If the traffic is too heavy, the road will become congested and the efficiency of data transmission will be reduced. To avoid this, the system dynamically adjusts the connection weights of topological nodes according to the real-time network load. The connection weight is like the width of the road. The higher the weight, the wider the road, which can carry more data traffic; the lower the weight, the narrower the road, which has relatively less data traffic.

[0054] The system will give special treatment to sensitive data blocks. Sensitive data is like precious cultural relics and needs to be protected more carefully. The system will prioritize migrating sensitive data to low-weight paths, because low-weight paths usually have less data traffic and are less likely to be attacked. In this way, sensitive data can be better protected during transmission, reducing the risk of leakage.

[0055] S5. Data elements are recombined and decoupled for output:

[0056] Before feeding back the query results to the user, the system will perform an important operation, which is to reorganize the data elements based on the reverse entropy value of the original topological network architecture. The reverse entropy value is an indicator to measure the degree of data chaos. By analyzing the reverse entropy value, the system can understand the distribution and association of the data.

[0057] The system will rearrange and combine the data elements according to the reverse entropy value, just like reassembling a puzzle into another pattern. The purpose of this is to generate an output form that is decoupled from the initial network structure. Decoupling means that the output result has no direct correlation with the original network structure. Even if an attacker obtains the output result, it is difficult to infer the original network structure and data information from it. This decoupling operation further enhances the security and privacy of the data.

[0058] S6. Abnormal access processing and topology network failure mechanism triggering:

[0059] The system monitors all access behaviors in real time and takes immediate action once abnormal access characteristics are detected. Abnormal access characteristics may include abnormal access frequency, abnormal access source, abnormal access path, etc. These abnormal characteristics are like dangerous signals, reminding the system that there may be security threats.

[0060] When abnormal access features are detected, the system triggers the dynamic failure mechanism of the topological network structure. This is like in a city, when an emergency occurs, the emergency plan will be activated to cut off some important traffic routes. In this system, all node connection relationships of the topological network will be unbound according to the preset decay function within the specified time window. The preset decay function is like a timer and regulator, which can accurately control the speed and degree of node connection release. In this way, the system can quickly cut off the path of abnormal access, prevent attackers from further penetrating the system, and protect the security of data and the stable operation of the system.

[0061] In another preferred embodiment of the present invention, in order to further enhance the data security and operation traceability of the system and meet the compliance audit requirements, a key link of establishing a full-link tracking log is added. This measure is like equipping the entire system operation process with a set of accurate and strict monitoring and recording systems, so that every operation detail can be traced.

[0062] The core of building a full-link tracking log is to comprehensively record all key information from the user initiating a query request to the final acquisition of the query result. It is like a detailed diary, meticulously recording the various dynamic changes of the system when processing each query.

[0063] When a user initiates a query request, the system will immediately capture and save the topological network snapshot at that time. This snapshot is equivalent to a "photo" of the topological network at a specific moment, which fully presents the status of each node in the network, the connection relationship between nodes, and the flow of data. Through the topological network snapshot, the present invention can clearly understand the specific architecture and operation mode of the system when processing queries.

[0064] At the same time, the system will also obtain the key matrix of the virtual container. The key matrix is ​​a key element to ensure the secure transmission and storage of data in the virtual container. It is like a special "password lock" that only information with the correct key can pass through smoothly.

[0065] Next, the system will perform space-time correlation coding on the topology network snapshot and the key matrix of the virtual container. Space-time correlation coding is an advanced technical means that cleverly combines information in the two dimensions of time and space. Through this coding method, each record can be given a unique identifier, accurately reflecting the time and system environment of the event. This is like attaching a precise "time-space coordinate" to each record, so that every operation detail can be accurately located in the timeline and system architecture.

[0066] After being encoded with temporal and spatial correlation, these records will be integrated in an orderly manner to generate an audit evidence chain that cannot be tampered with. This audit evidence chain is like a solid chain, with each link closely connected and indestructible. It provides a highly reliable audit basis for the operation of the system, and can clearly and accurately display the operation of the system and the data processing process, whether in internal security reviews or external compliance checks.

[0067] With this audit evidence chain, when a security incident occurs or a compliance audit is required, relevant personnel can quickly locate the root cause of the problem by tracing back the audit evidence chain and find out which link has abnormal operation or security loopholes. At the same time, since the audit evidence chain cannot be tampered with, the information it provides is highly credible and authoritative, and can provide a solid foundation for subsequent processing and improvement.

[0068] In another preferred embodiment of the present invention, creating a dynamic topology network architecture in step S1 is a precise and innovative process. When the system receives a user's query request, it will not rush to perform conventional response operations, but will first deeply analyze the specific content contained in the query request. This analysis process mainly focuses on two key aspects, namely the entity objects involved in the query request and the operation type. The entity object can be various data records, files or specific business objects in the database, and the operation type covers different operation instructions such as query, modification, and deletion. Through detailed analysis of these contents, the system can fully understand the user's query intention and lay the foundation for subsequent processing.

[0069] After analyzing the entity objects and operation types of the query request, the system will further extract fingerprint features that represent its semantics. The semantic fingerprint feature is like the unique "DNA" of the query request, which can accurately reflect the core meaning and essential characteristics of the query request. The system uses advanced natural language processing and feature extraction algorithms to extract the most representative features from the text information of the query request to form a feature vector. This feature vector is a mathematical expression containing multiple dimensions of information, each of which corresponds to a key feature of the query request.

[0070] After obtaining the eigenvector, the system inputs it into the chaos function. The chaos function has a high degree of uncertainty and sensitivity. Even a small change in the input eigenvector may lead to a huge difference in the output result. Using this characteristic of the chaos function, the system can generate an initial network seed. This network seed is like a "seed" with unlimited potential. It contains the initial information and rules for building the entire dynamic topology network architecture.

[0071] Next, the system will perform fractal iteration on the generated network seeds according to the period value of the system clock. Fractal iteration is a repetitive, self-similar operation process, and each iteration will generate a more complex and sophisticated structure on the original basis. The period value of the system clock provides time control and rhythm for the iterative operation, ensuring that the iterative process is carried out according to certain rules and frequencies. Through multiple fractal iteration operations, the network seeds gradually grow and evolve, and finally form a network with unique structure and characteristics.

[0072] In another preferred embodiment of the present invention, the creation of a virtual query container in step S2 is an important step in ensuring data security and query efficiency. First, the system creates a virtual memory space that is not associated with a physical storage address. Traditional memory spaces are often closely linked to physical storage addresses, which to a certain extent increases the risk of data being illegally accessed and attacked. The virtual memory space created by the present invention is like a "virtual kingdom" independent of the real physical world. It is completely isolated from the physical storage address, effectively avoiding physical-level security threats.

[0073] After the virtual memory space is created, the system will establish a container boundary protection wall. This protection wall is like the wall of the "virtual kingdom", which can prevent illegal intrusion and interference from the outside. The protection wall uses advanced security technology and algorithms, and has high security and stability. It can strictly check and filter all data and requests entering the virtual memory space, allowing only legal information and operations to pass, thereby ensuring the security of data in the virtual memory space.

[0074] Inside the protective wall, the system divides it into multiple layers of isolation cabins. Each isolation cabin corresponds to a substructure branch of the topological network. This hierarchical isolation design is like dividing the "virtual kingdom" into multiple different areas, each with its own specific functions and uses. By placing the substructure branches of the topological network in different isolation cabins, data isolation and management can be effectively achieved to avoid mutual interference and influence between different substructures. At the same time, this design also improves the scalability and flexibility of the system. When a substructure needs to be modified or expanded, only the corresponding isolation cabin needs to be operated, which will not affect the normal operation of other parts.

[0075] In the virtual query container, the dynamic encryption channel is the key to ensure the security of data transmission. The system uses the Chameleon Hash algorithm to generate path keys. The Chameleon Hash algorithm is a highly flexible and secure hash algorithm that can generate different hash values ​​based on different input information, and these hash values ​​are highly anti-collision and unpredictable. The path key generated by the Chameleon Hash algorithm is like equipping each data transmission path with a unique "password lock". Only information with the correct key can be safely transmitted on the path.

[0076] In another preferred embodiment of the present invention, step S3 dynamically embeds the user permission tag into the topological network node, which is a key step to achieve precise permission control and ensure data security access. When a user initiates a query request, the system will first analyze and process the user's role attributes in detail. The user's role attributes include important information such as the user's identity, responsibilities, and authority scope in the system. For example, the user is a common user, administrator, auditor, etc. Each role has different operating permissions and data access scopes.

[0077] The system converts the user's role attributes into a multidimensional feature vector. A multidimensional feature vector is a mathematical expression that represents the user's role attribute information in the form of multiple dimensions. Each dimension corresponds to a specific aspect of the role attribute. In this way, the system can describe the user's role characteristics more accurately and comprehensively.

[0078] Next, the system will use the Lorenz attractor model to generate a path for the diffusion of permissions. The Lorenz attractor model is a mathematical model used to describe the behavior of chaotic systems, which has complex dynamic characteristics and fractal structures. In the present invention, the permission diffusion path generated by the Lorenz attractor model is like a complex "map" that specifies the way and scope of the propagation and diffusion of user permissions in the topological network. In this way, the system can accurately control the distribution and scope of user permissions in the topological network according to the user's role attributes.

[0079] After generating the permission diffusion path, the system will implant a permission verification trigger in the topology network node. The permission verification trigger is like a "access control system" installed on each node, which can monitor and verify the user permissions passing through the node in real time. When a user tries to access a node, the permission verification trigger will determine whether the user has permission to access the node based on the user's permission label and preset permission rules. If the user's permissions meet the requirements, the trigger will allow the user to access; if the user's permissions do not meet the requirements, the trigger will reject the user's access request and issue an alarm in time.

[0080] In order to further enhance the accuracy and security of permission control, the system will establish a dynamic association between permission characteristics and network path curvature. Network path curvature is an indicator that describes the degree of curvature and complexity of a network path. By establishing a dynamic association between permission characteristics and network path curvature, the system can dynamically adjust the user's permission range according to the different characteristics of the network path. For example, for some network paths with large curvature and high complexity, the system can set higher permission requirements, and only users with corresponding advanced permissions can access them.

[0081] In another preferred embodiment of the present invention, in S5, the process of recombining the data elements is:

[0082] S51, dynamic topology analysis stage:

[0083] At this stage, the present invention will extract a multi-dimensional connectivity distribution matrix based on the current connectivity of the original topology network, which is like a comprehensive "physical examination" of the network to understand the network status from different angles. Specifically, it includes the following aspects:

[0084] Node degree distribution map: The present invention counts the current number of connections of each node in the network and sees how the importance (i.e. weight) of these connections is distributed. Then, this information is presented in the form of a degree heat map, like a map, where the color depth represents the connection status, so that the present invention can see at a glance which nodes are more connected and which are less connected.

[0085] Path activity graph: This invention refers to the historical query frequency of each path in the network, that is, the number of times this path has been used, and the delay of information transmission on this path. Through this information, the dynamic activity coefficient of each path is calculated, which can reflect the activity of this path in the network.

[0086] Network curvature tensor: The present invention uses the discrete Ricci flow algorithm to calculate the local curvature of the network. Curvature can help the present invention find areas in the network where nodes interact frequently (high-density interaction clusters), like a busy market; it can also find isolated nodes that have few connections with other nodes, like a remote village.

[0087] S52, Entropy Weight Dynamic Assignment Stage:

[0088] At this stage, the present invention needs to perform multi-dimensional entropy calculations on the data units in the topological network. The entropy value can be understood as a measure of the "uncertainty" or "importance" of the data. The specific calculation method is as follows:

[0089] Structural entropy: Based on the node degree distribution and path activity information obtained above, the influence weight of each data unit in the entire topological network structure is calculated. In simple terms, it is to see where this data unit is in the network structure and how important it is.

[0090] Semantic entropy: When a query request comes in, the present invention will analyze the semantic tags in the request, just like understanding the meaning of a sentence. Then, through the natural language processing model, the semantic association strength between each data unit and the query request is calculated, that is, to see how close they are in meaning.

[0091] Time series entropy: This invention takes into account the timestamp of the data, that is, the time when the data was generated, and the frequency of data updates. By constructing a time series decay function, the weight of the data unit is dynamically adjusted according to the time and update situation. The older the data is and the less updated it is, the lower its weight may be.

[0092] Finally, the present invention combines the information of structural entropy, semantic entropy and temporal entropy to generate a composite entropy weight vector. This vector is like a "priority list", telling the present invention which data units should be processed first during reorganization.

[0093] S53, mirror space projection stage:

[0094] At this stage, the present invention uses topological dual transformation technology to map the original network into a mirror space, just like projecting things in the real world into a mirror. The specific operations are as follows:

[0095] Duality rule: The present invention maps the nodes in the original network to the hyperplanes in the mirror space, and maps the edges in the original network to the intersection of the hyperplanes. In this way, the original network is presented in a new form in the mirror space.

[0096] Dynamic projection: Based on the composite entropy weight vector obtained above, the present invention will perform nonlinear scaling on those data units with relatively high weights. This is like magnifying important things, allowing them to occupy the core topological position in the mirror space and be more easily noticed.

[0097] Boundary encryption: At the edge of the mirror space, the present invention deploys a quantum noise layer. For those data with relatively low weight, the present invention uses this quantum noise layer to blur them, just like covering them with a layer of fog to protect these less important data.

[0098] S54, geodesic reconstruction phase:

[0099] Path planning: The present invention treats data units with relatively high entropy weight as "anchor points", just like the cornerstones of building a house. Then, the geodesic algorithm is used to find the minimum energy path connecting these key data clusters. This is like planning a route in the city that saves the most effort and connects important places.

[0100] Morphological evolution: This invention transforms the original linear path into a fractal structure through conformal transformation on the Riemann manifold. The fractal structure is a very complex and self-similar structure, which can increase the complexity of the association between data and make the connection between data closer and more diverse.

[0101] Output decoupling: Topological dimension reduction is performed on the reorganized fractal structure, just like flattening a complex three-dimensional object into two dimensions. Through this operation, the geometric association between the reorganized data and the original network is removed, and finally a flattened output data stream is generated.

[0102] S55, Verification and feedback phase:

[0103] At this stage, the present invention needs to verify whether the reorganized data is accurate. The specific method is to perform a reverse hash check on the output data stream and the original topology network. The hash check is like adding a special "fingerprint" to the data. By comparing the "fingerprint", it is ensured that the data is not lost or wrong during the reorganization process.

[0104] If the verification fails, it means that there is a problem in the data reorganization process, and the present invention will trigger the dynamic correction mechanism of the entropy weight vector. In other words, the present invention will readjust the composite entropy weight vector calculated previously, and then execute steps S52-S54 again to reorganize the data.

[0105] Finally, the present invention will output an irreversible reorganized data packet carrying a time-space tag. The form of this data packet is completely different from the original storage structure, just like re-layouting a book into another form, and this change cannot be reversed.

[0106] In another preferred embodiment of the present invention, in S6, when an abnormal access feature is detected, the dynamic invalidation mechanism of the topological network structure is triggered, so that all node connection relationships are unbound according to a preset decay function within a specified time window. The specific process is:

[0107] Real-time monitoring of abnormal resonance frequencies of all topology network nodes. Abnormal resonance frequencies are like "danger signals" sent by nodes. Once abnormal fluctuations occur, they may indicate abnormal access behavior. Each node has its normal operating status and frequency range. When the frequency of a node is detected to be beyond this normal range, the system will be alert.

[0108] When the failure rate of a node exceeds the dynamic threshold, it indicates that the node is already in an unstable state and may have been attacked or failed. At this point, the system will trigger a cascading failure mechanism. Cascading failure is like a domino effect, where the failure of one node will cause other nodes connected to it to fail one after another. The system will quickly spread the signal of structural disintegration along the topological connection path, just like sending an emergency alarm, letting the entire network know that danger is coming.

[0109] To handle this situation more efficiently, the system calculates the optimal failure path based on the Zermelo navigation problem model. This model can find the fastest and most efficient path in a complex network structure, allowing the failure process to proceed in an orderly manner and avoid causing wider chaos.

[0110] Finally, the entropy increase and dissipation of the network structure is completed in the Riemann manifold space. The Riemann manifold space is a space with special geometric properties. In this space, the entropy of the network structure will gradually increase and eventually reach a disordered state, thereby achieving the disintegration and dissipation of the structure, ensuring that abnormal access can no longer cause harm to the system.

[0111] See also Figure 2 As shown, the present invention also includes a software query information management system, which is used to implement the above-mentioned software query information management method, including:

[0112] A dynamic topology generator is used to create a dynamic topology network architecture based on the semantic features of the query content after receiving a query request from the user. Each node in the topology network architecture is generated by cross-calculating the query parameter hash value and the timestamp factor.

[0113] A virtual container manager, used to create a virtual query container corresponding to the dynamic topology network in an isolated memory space, the virtual query container generates a dynamic encryption channel bound to the current topology structure, and each dynamic encryption channel is assigned an independent key matrix;

[0114] The permission topology embedding device is used to dynamically embed the user permission label into the topology network node through the attribute-based encryption algorithm to establish a mapping relationship between the permission feature and the network path;

[0115] Node weight regulator, which is used to dynamically adjust the topological node connection weights according to the real-time network load when executing distributed queries in the virtual query container, and sensitive data blocks are preferentially migrated to low-weight paths;

[0116] The data reorganization engine is used to reorganize the data elements based on the reverse entropy value of the original topological network architecture before feeding back the query results to the user, generating an output form that is decoupled from the initial network structure;

[0117] The failure trigger module is used to trigger the dynamic failure mechanism of the topological network structure when abnormal access features are detected, so that all node connection relationships are unbound according to the preset attenuation function within the specified time window.

[0118] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A software query information management method, characterized in that: The following steps are involved: S1. After receiving the user's query request, a dynamic topology network architecture is created based on the semantic features of the query content. Each node in the topology network architecture is generated by the cross operation of the query parameter hash value and the timestamp factor; S2. A virtual query container corresponding to the dynamic topology network is created in the isolated memory space. The virtual query container generates a dynamic encryption channel bound to the current topology structure, and each dynamic encryption channel is assigned an independent key matrix; S3. The user permission label is dynamically embedded in the topology network node through the attribute-based encryption algorithm to establish a mapping relationship between the permission feature and the network path; S4. When executing distributed queries in the virtual query container, the topology node connection weight is dynamically adjusted according to the real-time network load, and sensitive data blocks are preferentially migrated to low-weight paths; S5. When the query results are sent to the Before feedback to the user, the data elements are recombined based on the reverse entropy value of the original topological network architecture to generate an output form that is decoupled from the initial network structure; S6, when abnormal access features are detected, the dynamic invalidation mechanism of the topological network structure is triggered, so that all node connection relationships are unbound according to the preset decay function within the specified time window; it also includes establishing a full-link tracking log, encoding the topological network snapshot and the key matrix of the virtual container at each user query request in time and space, and generating an audit evidence chain that cannot be tampered with; in S1, the process of creating a dynamic topological network architecture is: analyzing the query request contains the entity objects and operation types involved, extracting the fingerprint features representing its semantics, and inputting the extracted feature vector into the chaotic function to generate an initial network seed; According to the period value of the system clock, the network seed is subjected to fractal iterative operation; finally, a three-dimensional topological network with non-Euclidean geometric characteristics is generated, and each network node is provided with a dynamic addressing identifier and a self-destruct timer; in S2, the creation process of the virtual query container is as follows: a virtual memory space not associated with a physical storage address is created, a container boundary protection wall is established, and the protection wall is divided into multiple layers of isolation cabins, each isolation cabin corresponds to a substructure branch of the topological network, and a dynamic encryption channel uses a chameleon hash algorithm to generate a path key, and each element in the key matrix corresponds to a nonlinear mapping relationship between the coordinates of the topological node.

2. A software query information management method according to claim 1, characterized in that: In S3, the process of dynamically embedding user permission labels into topological network nodes is as follows: converting user role attributes into multidimensional feature vectors, using the Lorenz attractor model to generate permission diffusion paths, implanting permission verification triggers in topological network nodes, establishing a dynamic association between permission features and network path curvature, and automatically distorting the path of unauthorized access through the Möbius transformation.

3. A software query information management method according to claim 1, characterized in that: In the S5, the process of recombining the data elements is as follows: S51, counting the real-time connection number and weight distribution of each node in the topological network, and generating a degree heat map according to the connection number and weight distribution; According to the query frequency and response delay of each path in the network history data, the dynamic activity coefficient of each path is calculated, and the local curvature of the network is calculated using the discrete Ricci flow algorithm to identify high-density interaction clusters and isolated nodes; S52, according to the degree heat map and the dynamic activity coefficient, the influence weight of each data unit in the topological network structure is calculated. When there is a query request, the semantic label of the query request is parsed, and the semantic association strength of the data unit is quantified through the natural language processing model. The time decay function is constructed by combining the timestamp factor and the data update frequency. The weight of the data unit is dynamically adjusted according to the timestamp factor and the data update frequency. The older the data unit is and the less updated it is, the lower its weight is. The structural entropy, semantic entropy and time series entropy are fused to generate a composite entropy weight vector, which is used as the data element for reorganization Priority basis; S53, mapping the nodes in the original topological network to the hyperplanes of the mirror space, and mapping the edges of the original topological network points to the intersection of the hyperplanes; according to the composite entropy weight vector, nonlinearly amplifying the data units above the entropy weight threshold; deploying a quantum noise layer at the edge of the mirror space, and fuzzifying the data below the entropy weight threshold; S54, using the data units above the entropy weight threshold as anchor points, using the geodesic algorithm to generate the minimum energy path to connect the key data clusters; through the conformal transformation on the Riemann manifold, the linear path is converted into a fractal structure; topological dimension reduction is performed on the reorganized fractal structure, and the geometric association between the reorganized data and the original network is stripped off to generate a flattened output data stream; S55, the output data stream is reverse hashed with the original topological network; If the verification fails, the dynamic correction mechanism of the entropy weight vector is triggered, and steps S52 to S54 are re-executed to reorganize the data; The final output is an irreversible reassembled data packet carrying time and space tags.

4. A software query information management method according to claim 1, characterized in that: In S6, when an abnormal access feature is detected, the dynamic failure mechanism of the topological network structure is triggered, so that all node connection relationships are unbound according to a preset attenuation function within a specified time window. The specific process is: real-time monitoring of the abnormal resonance frequency of all topological network nodes, when the failure rate of the node exceeds the dynamic threshold, triggering cascade failure, propagating the signal of structural disintegration along the topological connection path, calculating the optimal failure path according to the Zermelo navigation problem model, and completing the entropy increase and dissipation of the network structure in the Riemann manifold space.

5. A software query information management system, used to implement a software query information management method according to any one of claims 1 to 4, characterized in that: include: A dynamic topology generator is used to create a dynamic topology network architecture based on the semantic features of the query content after receiving a query request from the user, wherein each node in the topology network architecture is generated by cross-calculating the query parameter hash value and the timestamp factor; a virtual container manager is used to create a virtual query container corresponding to the dynamic topology network in an isolated memory space, wherein the virtual query container generates a dynamic encryption channel bound to the current topology structure, and each dynamic encryption channel is assigned an independent key matrix; a permission topology embedding device is used to dynamically embed the user permission label into the topology network node through an attribute-based encryption algorithm, and establish a mapping relationship between the permission feature and the network path; a node weight regulator is used to dynamically adjust the topology node connection weight according to the real-time network load when executing a distributed query in the virtual query container, and the sensitive data block is preferentially migrated to a low-weight path; a data reorganization engine is used to recombine the data elements based on the reverse entropy value of the original topology network architecture before feeding back the query results to the user, and generate an output form decoupled from the initial network structure; The failure trigger module is used to trigger the dynamic failure mechanism of the topological network structure when abnormal access features are detected, so that all node connection relationships are unbound according to the preset attenuation function within the specified time window.

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