Method and system for checking computer parameter consistency across service links

Abstract

The present application relates to the technical field of data processing, and disclose a computer parameter consistency checking method and system across service links, the method comprises: in the parameter management platform, according to the topology structure of target internet of things service link and the function role of each node, a parameter correlation model is established;Then initiate configuration parameter collection request to each node in the link, receive and obtain the current runtime parameter data returned by each node.Based on the parameter correlation model and the current runtime parameter data, cross-node consistency checking is carried out, and a parameter consistency checking report is generated.If the report indicates that there is parameter inconsistency, according to the inconsistent parameter type and the constraint relationship defined in the model, a parameter repair instruction set is automatically generated, which is distributed to the corresponding inconsistent nodes in the link, to drive the nodes to perform parameter update operation, and the nodes need to feed back the execution result to the parameter management platform;The present application can improve the computer parameter consistency checking efficiency across service links.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for verifying the consistency of computer parameters across service links. Background Technology

[0002] Cross-service links involve a large number of nodes, varied topologies, and significant differences in the functional roles of each node. Existing parameter consistency verification technologies have not established parameter association models that are compatible with the link topology and node functions. This makes it difficult to accurately define the range of parameters to be collected and the parameter constraint relationships between nodes, resulting in redundancy and blindness in the parameter collection process. The acquired parameter data lacks effectiveness and specificity, directly affecting the basic reliability of subsequent verification work.

[0003] Existing technologies, in the cross-node parameter consistency verification stage, do not employ differentiated verification logic for static baseline parameters and dynamically associated parameters. They also lack scientifically adapted methods for calculating the real-time difference of dynamic parameters, making it impossible to accurately identify parameter inconsistencies that exceed reasonable ranges. Furthermore, when faced with parameter inconsistency issues, they lack the ability to automatically generate repair instructions based on parameter type and associated constraints, relying on manual intervention to formulate repair solutions. This not only prolongs the problem-solving cycle but may also lead to new parameter anomalies due to human error, severely impacting the operational stability and data transmission accuracy of cross-service links. Summary of the Invention

[0004] This invention provides a method and system for verifying the consistency of computer parameters across service links to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a cross-service link computer parameter consistency verification method, comprising:

[0006] S1. In the parameter management platform, establish a parameter association model based on the topology of the target IoT service link and the functional role of each node;

[0007] S2. The parameter management platform initiates a configuration parameter collection request to each node in the target IoT service link and receives the current runtime parameter data returned by each node.

[0008] S3. Based on the parameter association model and the current runtime parameter data, perform cross-node consistency verification to obtain a parameter consistency verification report;

[0009] S4. When the parameter consistency verification report indicates that there is parameter inconsistency, a parameter repair instruction set is automatically generated according to the inconsistent parameter type and the constraint relationship defined in the parameter association model.

[0010] S5. Distribute the parameter repair instruction set to the corresponding inconsistent node in the target IoT service link, drive the inconsistent node to perform parameter update operation, and feed back the execution result to the parameter management platform.

[0011] In a preferred embodiment, the step of establishing a parameter association model in the parameter management platform based on the topology of the target IoT service link and the functional role of each node includes:

[0012] Analyze the topology of the target IoT service link and extract the connection relationships and communication path information between nodes;

[0013] Based on the functional role of each node, retrieve the standard parameter template corresponding to the functional role from the pre-configured parameter template library;

[0014] Based on the connection relationship, the communication path information, and the standard parameter template, a parameter association model is constructed.

[0015] In a preferred embodiment, the step of the parameter management platform initiating a configuration parameter collection request to each node in the target IoT service link and receiving the current runtime parameter data returned by each node includes:

[0016] Based on the set of configuration parameters defined in the parameter association model, determine the specific parameter identifiers that need to be collected from each node in the target IoT service link;

[0017] Based on the specific parameter identifier, a structured acquisition request message is generated;

[0018] The collection request message is sent to the corresponding node through the established communication link between the parameter management platform and each node.

[0019] The system receives encapsulated data packets returned by each node in response to the acquisition request message, parses and verifies the encapsulated data packets, and obtains the current runtime parameter data.

[0020] In a preferred embodiment, the step of performing cross-node consistency verification based on the parameter association model and the current runtime parameter data to obtain a parameter consistency verification report includes:

[0021] Extract the configuration parameter set from the parameter association model, and distinguish the parameters in the configuration parameter set into static baseline parameters and dynamic association parameters;

[0022] The static baseline parameter values ​​in the current runtime parameter data are directly compared with the expected baseline parameter values ​​defined in the parameter association model to identify static parameter inconsistencies.

[0023] Based on the constraint relationships defined in the parameter association model, calculate the real-time difference in parameter values ​​of the dynamic association parameter among multiple nodes with association relationships;

[0024] The real-time difference is compared with the preset dynamic drift threshold in the parameter association model to identify dynamic parameter inconsistencies that exceed the threshold.

[0025] By combining the static parameter inconsistencies with the dynamic parameter inconsistencies, a parameter consistency verification report is obtained.

[0026] In a preferred embodiment, calculating the real-time difference in parameter values ​​of the dynamic association parameter among multiple nodes with association relationships, based on the constraint relationships defined in the parameter association model, includes:

[0027] Based on the parameter association model, determine the dynamic parameter groups that need to be verified for consistency.

[0028] Obtain the current runtime parameter data of the parameter returned by all nodes within the dynamic parameter group, and form a real-time parameter value set;

[0029] Select a reference value from the set of real-time parameter values;

[0030] Calculate the deviation between each value in the real-time parameter value set and the reference value, and aggregate the deviations to obtain the real-time difference degree between all nodes.

[0031] In a preferred embodiment, calculating the deviation between each value in the set of real-time parameter values ​​and the reference value, and aggregating the deviations to obtain the real-time difference between all nodes, includes:

[0032] Calculate the absolute difference between each value in the set of real-time parameter values ​​and the reference value to obtain the individual deviation corresponding to each value;

[0033] From the individual biases mentioned above, select the individual bias with the largest value;

[0034] The individual deviation with the largest selected value is taken as the real-time difference degree of the consistency of parameters of all nodes within the dynamic parameter group.

[0035] In a preferred embodiment, when the parameter consistency verification report indicates parameter inconsistency, a parameter repair instruction set is automatically generated based on the inconsistent parameter type and the constraint relationships defined in the parameter association model, including:

[0036] Extract each inconsistent parameter from the parameter consistency verification report;

[0037] Based on each extracted inconsistency item, the parameter association model is queried to determine the parameter type corresponding to the inconsistency item and its association constraint relationship with other node parameters;

[0038] Based on the inconsistency type of the inconsistency item, the parameter type, and the associated constraint relationship, a parameter repair instruction is generated for each inconsistency item.

[0039] In a preferred embodiment, generating a parameter repair instruction for each inconsistency item based on the inconsistency type, the parameter type, and the associated constraint relationship includes:

[0040] If the inconsistency type of the inconsistency item is static parameter inconsistency and the parameter type is static baseline parameter, then the generated parameter repair instruction is as follows: set the parameter value of the inconsistency item corresponding to the node identifier to the expected baseline parameter value defined for the parameter in the parameter association model.

[0041] If the inconsistency type of the inconsistency item is dynamic parameter inconsistency and the parameter type is dynamic associated parameter, then a reference node is determined from the dynamic parameter association group according to the association constraint relationship, and the generated parameter repair instruction content is: synchronize the parameter value of the inconsistency item of the node corresponding to the node identifier to the parameter value of the same parameter identifier in the current runtime parameter data of the reference node.

[0042] The parameter repair instructions generated for all inconsistencies will be integrated and sorted to form a parameter repair instruction set.

[0043] In a preferred embodiment, distributing the parameter repair instruction set to the corresponding inconsistent nodes in the target IoT service link, driving the inconsistent nodes to perform parameter update operations, and feeding back the execution results to the parameter management platform includes:

[0044] The parameter repair instruction set is parsed, and each inconsistent node in the target IoT service link that needs to receive the instruction is determined based on the node identifier contained in each parameter repair instruction.

[0045] Through the secure communication channel between the parameter management platform and each inconsistent node, the corresponding parameter repair instructions are sent to the inconsistent nodes respectively, and after sending, the platform waits for instruction reception confirmation from the nodes.

[0046] Upon receiving a parameter repair instruction, the inconsistent node performs an update operation on its local configuration parameters according to the instruction content and generates a feedback message after the operation is completed.

[0047] The parameter management platform receives feedback messages from each inconsistent node and updates the parameter consistency verification report based on the execution status information in the messages.

[0048] To address the above problems, the present invention also provides a computer parameter consistency verification system across service links, the system comprising:

[0049] The parameter association modeling module is used to establish a parameter association model in the parameter management platform based on the topology of the target IoT service link and the functional role of each node.

[0050] The runtime parameter acquisition module is used to initiate configuration parameter acquisition requests from the parameter management platform to each node in the target IoT service link, and to receive the current runtime parameter data returned by each node.

[0051] The cross-node verification report module is used to perform cross-node consistency verification based on the parameter association model and the current runtime parameter data, and obtain a parameter consistency verification report.

[0052] The repair instruction generation module is used to automatically generate a parameter repair instruction set based on the inconsistent parameter type and the constraint relationship defined in the parameter association model when the parameter consistency verification report indicates that there is a parameter inconsistency.

[0053] The instruction distribution and execution module is used to distribute the parameter repair instruction set to the corresponding inconsistent node in the target IoT service link, drive the inconsistent node to perform parameter update operation, and feed back the execution result to the parameter management platform.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. This invention establishes a parameter association model based on the link topology and node functional roles, accurately defining the range of parameters to be collected and the node constraint relationship, avoiding redundant collection; at the same time, it distinguishes between static benchmark parameters and dynamic associated parameters and adopts differentiated verification logic, with static parameters directly compared to the benchmark value, and dynamic parameters calculating the real-time difference degree and comparing it with the threshold, which greatly improves the accuracy of inconsistency identification and shortens the verification cycle.

[0056] 2. Based on the inconsistency type, parameter type, and associated constraint relationship in the verification report, this invention automatically generates a targeted repair instruction set, synchronizes static parameters with the expected baseline value, synchronizes dynamic parameters with the effective value of the reference node, distributes instructions and receives execution feedback through a secure communication channel, quickly completes parameter correction, reduces human error, and ensures continuous and stable operation of cross-service links. Attached Figure Description

[0057] Figure 1A flowchart illustrating a cross-service link computer parameter consistency verification method provided in an embodiment of the present invention;

[0058] Figure 2 A functional block diagram of a cross-service link computer parameter consistency verification system provided in an embodiment of the present invention;

[0059] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0060] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0061] This application provides a method for verifying the consistency of computer parameters across service links. The executing entity of this method includes, but is not limited to, at least one of the following: a server, a terminal, or any electronic device configured to execute the method provided in this application. In other words, the method for verifying the consistency of computer parameters across service links can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cluster of cloud servers. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0062] Reference Figure 1 The diagram shown is a flowchart illustrating a cross-service link computer parameter consistency verification method according to an embodiment of the present invention. In this embodiment, the cross-service link computer parameter consistency verification method includes:

[0063] S1. In the parameter management platform, establish a parameter association model based on the topology of the target IoT service link and the functional role of each node;

[0064] In this embodiment of the invention, the step of establishing a parameter association model in the parameter management platform based on the topology of the target IoT service link and the functional role of each node includes:

[0065] Analyze the topology of the target IoT service link and extract the connection relationships and communication path information between nodes;

[0066] Based on the functional role of each node, retrieve the standard parameter template corresponding to the functional role from the pre-configured parameter template library;

[0067] Based on the connection relationship, the communication path information, and the standard parameter template, a parameter association model is constructed.

[0068] A full-link node identification process is conducted for the target IoT service link, systematically identifying all IoT nodes involved in data interaction and business processing within the link. The physical deployment location and business participation of each node in the service link are clarified. Based on the node identification results, a topology diagram of the target IoT service link is drawn, completing the analysis of the topology structure. Then, the interaction behavior of each node in the topology diagram is analyzed step by step, clarifying the direct association between any two nodes with data transmission and command interaction, forming the connection relationship between nodes. Simultaneously, along the business flow direction of the service link, the complete flow path from data reception to data transmission for each node is traced, clarifying the transmission order and flow direction of data and commands between nodes, forming communication path information.

[0069] Each node in the analyzed target IoT service link topology is decomposed into its business functions in all dimensions. The specific business roles that each node actually undertakes in the service link, such as data processing, command forwarding, device sensing, data storage, and communication relay, are clarified. Based on these specific business roles, the functional roles of each node are defined. Then, the defined functional roles of each node are used as unique search keywords to perform full-domain precise matching in a pre-configured parameter template library. The template content that completely corresponds to the functional role is directly retrieved to form a standard parameter template corresponding to the functional role.

[0070] The extracted node connection relationships and communication path information are integrated with the retrieved standard parameter templates. Based on the node connection relationships as the basic framework, the association boundaries between the node corresponding to each standard parameter template and other nodes are clarified. Based on the communication path information as the basis for business flow, the transmission direction and adaptation logic of the node-related content corresponding to each standard parameter template in the service link are sorted out. The standard parameter templates corresponding to each node are arranged hierarchically and connected in a relational manner according to the association boundaries of the node connection relationships and the flow logic of the communication path. The corresponding association relationships and adaptation connection rules between the standard parameter templates of different nodes are clarified, thereby forming a complete parameter association model.

[0071] S2. The parameter management platform initiates a configuration parameter collection request to each node in the target IoT service link and receives the current runtime parameter data returned by each node.

[0072] In this embodiment of the invention, the step of the parameter management platform initiating a configuration parameter collection request to each node in the target IoT service link and receiving the current runtime parameter data returned by each node includes:

[0073] Based on the set of configuration parameters defined in the parameter association model, determine the specific parameter identifiers that need to be collected from each node in the target IoT service link;

[0074] Based on the specific parameter identifier, a structured acquisition request message is generated;

[0075] The collection request message is sent to the corresponding node through the established communication link between the parameter management platform and each node.

[0076] The system receives encapsulated data packets returned by each node in response to the acquisition request message, parses and verifies the encapsulated data packets, and obtains the current runtime parameter data.

[0077] The configuration parameter set defined in the parameter association model is reviewed, and all configuration content within this set is inventoried. The pre-bound node attribute of each configuration item in the configuration parameter set is clarified. This attribute directly points to a specific node in the target IoT service link. For each node in the target IoT service link, all configuration content matching that node is accurately selected from the configuration parameter set based on the node attribute, forming a unique node configuration content set for each node. A unique identifier with exclusive pointing characteristics is assigned to each configuration item in each node configuration content set. This identifier corresponds only to a specific configuration content of a specific node in the target IoT service link. This unique identifier is the specific parameter identifier that needs to be collected from each node in the target IoT service link, thus completing the work of determining the specific parameter identifier corresponding to all nodes in the target IoT service link.

[0078] The entire process involves comprehensively analyzing all identified specific parameter identifiers. These identifiers are then categorized and organized according to the unique identifiers of each node in the target IoT service chain. This ensures that all specific parameter identifiers for each node form an independent and complete specific parameter identifier group. Each specific parameter identifier group is permanently bound to the unique identifier of its corresponding node. A pre-defined structured message framework is retrieved. This framework includes three fixed content areas: a node identification area, a data collection request area, and a message verification area. The unique identifier of the corresponding node is fully entered in the node identification area of ​​the structured message framework. All specific parameter identifiers within the specific parameter identifier group corresponding to that node are fully entered in the data collection request area according to a pre-defined content arrangement. Finally, a unique verification identifier corresponding to the data collection request message is added in the message verification area, thus completing the generation of the structured data collection request message.

[0079] A comprehensive review of all established communication links between the parameter management platform and each node of the target IoT service link is conducted. The unique identifier of each node corresponding to the target IoT service link is identified, forming a one-to-one correspondence table between communication links and nodes. For each generated structured data acquisition request message, the unique node identifier in the node identification area is extracted. Based on this unique node identifier, the dedicated communication link established between the parameter management platform and the node is accurately retrieved from the correspondence table. The transmission format specification of this dedicated communication link is then retrieved, and the content encoding format and field arrangement order of the data acquisition request message are specifically adapted and adjusted according to this specification. This ensures that the adjusted data acquisition request message content format fully complies with the transmission requirements of the dedicated communication link. The format-adapted data acquisition request message is then pushed to the target node through the corresponding dedicated communication link, completing the operation of sending data acquisition request messages to the corresponding nodes.

[0080] A dedicated data receiving channel is opened in the parameter management platform. This channel only receives encapsulated data packets returned by each node in the target IoT service link in response to the collection request message. After receiving the encapsulated data packet, the unique node identifier and exclusive verification identifier contained in the encapsulated data packet are extracted first. The exclusive verification identifier in the encapsulated data packet is compared with the exclusive verification identifier in the corresponding collection request message. If the comparison result is consistent, the encapsulated data packet is determined to be a valid data packet. If the comparison result is inconsistent, the encapsulated data packet is directly discarded. The encapsulated data packets determined to be valid data packets are parsed to extract the raw data of the collection parameters. The raw data of the collection parameters is sorted in order according to the arrangement order of the specific parameter identifiers in the corresponding collection request message, so that each piece of raw data of the collection parameters forms a one-to-one binding relationship with the corresponding specific parameter identifier. The sorted parameter data obtained after sorting and accurately binding with the specific parameter identifier is the current runtime parameter data.

[0081] S3. Based on the parameter association model and the current runtime parameter data, perform cross-node consistency verification to obtain a parameter consistency verification report;

[0082] In this embodiment of the invention, the step of performing cross-node consistency verification based on the parameter association model and the current runtime parameter data to obtain a parameter consistency verification report includes:

[0083] Extract the configuration parameter set from the parameter association model, and distinguish the parameters in the configuration parameter set into static baseline parameters and dynamic association parameters;

[0084] The static baseline parameter values ​​in the current runtime parameter data are directly compared with the expected baseline parameter values ​​defined in the parameter association model to identify static parameter inconsistencies.

[0085] Based on the constraint relationships defined in the parameter association model, calculate the real-time difference in parameter values ​​of the dynamic association parameter among multiple nodes with association relationships;

[0086] The real-time difference is compared with the preset dynamic drift threshold in the parameter association model to identify dynamic parameter inconsistencies that exceed the threshold.

[0087] By combining the static parameter inconsistencies with the dynamic parameter inconsistencies, a parameter consistency verification report is obtained.

[0088] The step of calculating the real-time difference in parameter values ​​of the dynamic association parameter among multiple nodes with association relationships, based on the constraint relationships defined in the parameter association model, includes:

[0089] Based on the parameter association model, determine the dynamic parameter groups that need to be verified for consistency.

[0090] Obtain the current runtime parameter data of the parameter returned by all nodes within the dynamic parameter group, and form a real-time parameter value set;

[0091] Select a reference value from the set of real-time parameter values;

[0092] Calculate the deviation between each value in the real-time parameter value set and the reference value, and aggregate the deviations to obtain the real-time difference degree between all nodes.

[0093] The step of calculating the deviation between each value in the real-time parameter value set and the reference value, and aggregating the deviations to obtain the real-time difference between all nodes, includes:

[0094] Calculate the absolute difference between each value in the set of real-time parameter values ​​and the reference value to obtain the individual deviation corresponding to each value;

[0095] From the individual biases mentioned above, select the individual bias with the largest value;

[0096] The individual deviation with the largest selected value is taken as the real-time difference degree of the consistency of parameters of all nodes within the dynamic parameter group.

[0097] The entire parameter association model is traversed to extract all preset configuration content according to the node to which the parameter belongs and its functional attributes. All extracted configuration content is integrated into a complete and independent set, which is the configuration parameter set. Then, based on the operating characteristics of the target IoT service link and the actual role of the parameter, the attributes of each parameter in the configuration parameter set are determined. Parameters whose values ​​remain fixed throughout the entire service link operation cycle, serve as the basic basis for node operation, and are not affected by the link operation status and node interaction are defined as static baseline parameters. Parameters whose values ​​change with the service link operation status and the adjustment of inter-node interaction behavior, and have business collaboration relationships with other nodes' similar parameters, are defined as dynamic association parameters. This completes the clear distinction between the two types of parameters and forms corresponding parameter subsets for each.

[0098] Based on the dual dimensions of parameter identifier and node affiliation, the actual running values ​​of all static benchmark parameters are accurately extracted from the current runtime parameter data. Each actual running value is bound to a corresponding node identifier and parameter identifier. Then, from the parameter association model, the expected benchmark parameter value corresponding to each static benchmark parameter is retrieved one by one according to the same node identifier and parameter identifier. Similarly, the corresponding node identifier and parameter identifier are bound to it. The actual running values ​​of static benchmark parameters corresponding to the same node identifier and the same parameter identifier are directly compared with the expected benchmark parameter values. The comparison results of each parameter are recorded. Static benchmark parameters with numerical mismatches are summarized. Each summarized parameter is labeled with a node identifier, parameter identifier, actual running value, and corresponding expected benchmark parameter value, forming a static parameter inconsistency item.

[0099] The entire domain is analyzed to identify all dynamically related parameters defined in the parameter association model. At the same time, the business flow logic of the target IoT service link, the communication and interaction rules between nodes, and the mutual influence relationship of the dynamically related parameters in the operation of the link are retrieved. Based on the above, all dynamically related parameters are classified and integrated. Dynamically related parameters that are in the same business flow link, belong to the same communication and interaction link, have direct mutual influence on parameter changes, and have business collaboration relationships with corresponding nodes are integrated into a whole. Each integrated dynamic related parameter whole is a dynamic parameter group that needs to be verified for consistency. Each dynamic parameter group is marked with a unique group identifier, and all dynamic related parameter identifiers and corresponding all belonging nodes contained in each group are identified.

[0100] For each identified dynamic parameter group that requires consistency verification, based on the list of affiliated nodes marked within the group, all corresponding nodes are identified one by one. From the current runtime parameter data returned by each node, the actual current runtime value of the parameter is accurately extracted according to the dynamic associated parameter identifier within the group. The extracted values ​​are validated, and invalid data with no actual value or missing value is removed. The validated valid actual values ​​are arranged in order according to the identifier of the affiliated node, and each valid actual value is bound to the corresponding node identifier. All valid actual values ​​bound to node identifiers are integrated into a complete set, which is the set of real-time parameter values ​​corresponding to the dynamic parameter group.

[0101] A comprehensive information review is conducted on all valid actual values ​​of the bound node identifiers in the completed real-time parameter value set. According to the preset reference value selection rules in the parameter association model, the actual parameter values ​​of the main control node in the corresponding business scenario in the target IoT service link are selected as reference values. Based on these rules, the actual parameter values ​​corresponding to the main control node are locked in the real-time parameter value set, and these values ​​are determined as the unique reference values ​​of the real-time parameter value set. The corresponding node identifier is bound to this reference value and labeled separately.

[0102] For each valid actual value of a bound node identifier in the real-time parameter value set, excluding the reference value, the value is compared with a uniquely determined and individually labeled reference value. The original difference between the value and the reference value is calculated. If the original difference is positive, it is retained as the initial deviation value. If the original difference is negative, the opposite value of the original difference is taken as the initial deviation value. This initial deviation value, which is neither positive nor negative, is the individual deviation corresponding to the valid actual value. The corresponding individual deviation is calculated for each valid actual value in the real-time parameter value set, and each individual deviation is bound to the corresponding node identifier and the original actual parameter value.

[0103] For all individual deviations that have been calculated and bound with relevant identifiers, a comprehensive numerical analysis is performed. Numerical filtering is carried out through successive comparisons. First, one individual deviation is selected as the initial comparison benchmark. The initial comparison benchmark is then compared with the next individual deviation. The individual deviation with the larger value is retained as the new comparison benchmark. The new comparison benchmark is then compared with the remaining next individual deviation. This process is repeated until all individual deviations have been compared. The final comparison benchmark is the individual deviation with the largest value among all individual deviations. This individual deviation with the largest value is individually labeled, and its corresponding node identifier and the actual value of the original parameter are bound to it simultaneously.

[0104] The individual deviation with the largest value is checked for the final information. After confirming that the corresponding dynamic parameter group identifier, node identifier and the actual value of the original parameter are correct, no further numerical adjustment is made. The individual deviation with the largest value is directly defined as the real-time difference of parameter consistency of all nodes in the corresponding dynamic parameter group on the dynamic associated parameter. The corresponding dynamic parameter group identifier and dynamic associated parameter identifier are bound to the real-time difference, and the determination of the real-time difference of the dynamic parameter group is completed.

[0105] Based on the dual dimensions of dynamic parameter group identifier and dynamic association parameter identifier from the parameter association model, the preset unique dynamic drift threshold of the dynamic parameter group is accurately retrieved. The real-time difference of the dynamic parameter group with the identified and bound relevant identifiers is directly compared with the retrieved dynamic drift threshold. If the real-time difference is less than or equal to the dynamic drift threshold, the parameter consistency of the dynamic parameter group is determined to meet the verification requirements. If the real-time difference is greater than the dynamic drift threshold, the parameter consistency of the dynamic parameter group is determined to fail to meet the verification requirements. All dynamic parameter groups determined to fail to meet the verification requirements are centrally summarized, and each group is labeled with a group identifier, dynamic association parameter identifier, all involved node identifiers, real-time difference value, and preset dynamic drift threshold value to form a dynamic parameter inconsistency item.

[0106] The system integrates information on both static and dynamic parameter inconsistencies across the entire domain, comprehensively reviewing all detailed information for both types of inconsistencies. Detailed information for static parameter inconsistencies includes node identifiers, parameter identifiers, actual operating values, corresponding expected baseline parameter values, and the specific difference between the actual and expected values. Detailed information for dynamic parameter inconsistencies includes dynamic parameter group identifiers, associated parameter identifiers, all involved node identifiers, real-time difference values, preset dynamic drift threshold values, and specific values ​​where the difference exceeds the threshold. Simultaneously, basic information for this parameter consistency verification is added, including the target IoT service link being verified, the specific verification time, a complete list of all nodes participating in the verification, and the overall parameter range for this verification. Following the preset report formatting specifications, the basic verification information and detailed information for both types of inconsistencies are presented in a modular and orderly manner. All presented information is comprehensively verified to ensure no omissions, errors, or duplications, ultimately generating a complete parameter consistency verification report.

[0107] S4. When the parameter consistency verification report indicates that there is parameter inconsistency, a parameter repair instruction set is automatically generated according to the inconsistent parameter type and the constraint relationship defined in the parameter association model.

[0108] In this embodiment of the invention, when the parameter consistency verification report indicates that there is parameter inconsistency, a parameter repair instruction set is automatically generated based on the inconsistent parameter type and the constraint relationship defined in the parameter association model, including:

[0109] Extract each inconsistent parameter from the parameter consistency verification report;

[0110] Based on each extracted inconsistency item, the parameter association model is queried to determine the parameter type corresponding to the inconsistency item and its association constraint relationship with other node parameters;

[0111] Based on the inconsistency type of the inconsistency item, the parameter type, and the associated constraint relationship, a parameter repair instruction is generated for each inconsistency item.

[0112] The step of generating parameter repair instructions for each inconsistency item based on the inconsistency type, the parameter type, and the associated constraint relationship includes:

[0113] If the inconsistency type of the inconsistency item is static parameter inconsistency and the parameter type is static baseline parameter, then the generated parameter repair instruction is as follows: set the parameter value of the inconsistency item corresponding to the node identifier to the expected baseline parameter value defined for the parameter in the parameter association model.

[0114] If the inconsistency type of the inconsistency item is dynamic parameter inconsistency and the parameter type is dynamic associated parameter, then a reference node is determined from the dynamic parameter association group according to the association constraint relationship, and the generated parameter repair instruction content is: synchronize the parameter value of the inconsistency item of the node corresponding to the node identifier to the parameter value of the same parameter identifier in the current runtime parameter data of the reference node.

[0115] The parameter repair instructions generated for all inconsistencies will be integrated and sorted to form a parameter repair instruction set.

[0116] The entire content structure of the parameter consistency verification report is analyzed, and the detailed presentation module for parameter inconsistencies is precisely located. Following the item order within this module, full information is extracted for each parameter inconsistency. For static parameter inconsistencies, the extracted information includes the corresponding node identifier, parameter identifier, inconsistency type, actual operating parameter value, and expected baseline parameter value. For dynamic parameter inconsistencies, the extracted information includes the corresponding node identifier, parameter identifier, inconsistency type, associated dynamic parameter group, real-time difference value, and preset dynamic drift threshold value. Each extracted parameter inconsistency is individually documented, ensuring complete consistency between the documented information and the extracted full information. During document creation, the verification report is checked against the document to confirm that no inconsistencies are missed and no information extraction errors occur. This completes the extraction of every parameter inconsistency from the parameter consistency verification report.

[0117] Each parameter inconsistency item, after extraction and individual information archiving, is treated as an independent retrieval unit. Using the parameter identifier and node affiliation information corresponding to each inconsistency item as the core retrieval basis, a targeted and precise retrieval is performed within the parameter association model. First, the pre-set parameter attribute definition library within the parameter association model is searched. The attribute labeling information of the corresponding parameter is retrieved from this library. Based on this information, the specific parameter type corresponding to the inconsistency item is clearly determined as either a static baseline parameter or a dynamic association parameter. Then, the pre-set parameter association rule library within the parameter association model is searched. All pre-defined constraint rules between this parameter and related parameters of other nodes in the target IoT service link are retrieved from this library. These rules include collaborative matching requirements for parameter values, linkage specifications for parameter changes, constraint conditions at the parameter level, and the dynamic association grouping range to which the parameter belongs. All retrieved constraint rules are integrated and defined as the association constraint relationship between the inconsistency item and other node parameters. The corresponding parameter type and association constraint relationship are recorded separately for each inconsistency item, completing the determination of the information corresponding to all inconsistencies.

[0118] For each extracted parameter inconsistency, a dual type check is performed. First, it is checked whether the inconsistency type marked in the information file is static parameter inconsistency. Then, it is checked whether the parameter type already determined for the inconsistency is a static baseline parameter. If both checks are confirmed, a dedicated parameter repair instruction generation process is initiated for the inconsistency. The generated parameter repair instruction clearly and completely specifies the unique identifier of the node performing the repair operation and the unique identifier of the parameter of the inconsistency to be repaired. At the same time, it is clearly marked that the applicable type of the instruction is static parameter repair. The core execution content of the instruction is to directly reset the current value of the parameter corresponding to the inconsistency in the target node corresponding to the node identifier to the expected baseline parameter value predefined uniquely for the parameter in the parameter association model. This ensures that the instruction contains a clear execution object, the object to be repaired, and unambiguous execution requirements.

[0119] For each extracted parameter inconsistency, a dual type check is performed. First, it is checked whether the inconsistency type marked in the information file is dynamic parameter inconsistency. Then, it is simultaneously checked whether the parameter type already determined for the inconsistency is a dynamic associated parameter. If both checks are confirmed, based on the established association constraints between the inconsistency and other node parameters, the dynamic parameter association group to which the parameter belongs is accurately located. All node identifiers within this group and the pre-defined roles of each node in the association constraints are fully analyzed. Then, combined with the master-slave collaboration rules and benchmark node definition specifications clearly defined in the association constraints, a unique reference node is determined from the dynamic parameter association group and clearly marked. The node's unique identifier is noted, and then a dedicated parameter repair instruction generation process is initiated for the inconsistency. The generated parameter repair instruction clearly and completely specifies the unique identifier of the node performing the repair operation, the unique identifier of the inconsistency parameter to be repaired, and the unique identifier of the reference node used as a reference. At the same time, it is clearly marked that the applicable type of the instruction is dynamic parameter repair. The core execution content of the instruction is to synchronously update the current value of the parameter corresponding to the inconsistency in the target node corresponding to the node identifier to the actual running value of the parameter with the same parameter identifier in the current runtime parameter data of the determined reference node. This ensures that the instruction contains a clear execution object, the object to be repaired, the reference basis, and unambiguous execution requirements.

[0120] A temporary instruction library is established to collect all parameter repair instructions generated for each inconsistency item across the entire domain. All instructions are completely entered into this library and checked against each other to ensure no instructions are missing or incorrect. Subsequently, all parameter repair instructions in the library undergo an initial classification and organization. Instructions generated for static baseline parameters are grouped into a static parameter repair instruction sub-library, and those generated for dynamically related parameters are grouped into a dynamic parameter repair instruction sub-library. The instructions in both sub-libraries are then ordered. The static parameter repair instruction sub-library is ordered sequentially according to the business hierarchy of the nodes in the target IoT service chain, from core business nodes to edge business nodes. The dynamic parameter repair instruction sub-library is first sorted according to the business importance of the associated dynamic parameter groups, from core business groups to auxiliary business groups. Instructions within the same associated dynamic parameter group are then sorted according to the coordination priority of the nodes in the link. After all instructions are sorted, a continuous global execution sequence number is assigned to each instruction. This sequence number clarifies the overall order of the instructions in the actual execution process. At the same time, a corresponding inconsistency item core information summary is added to each instruction as the basis for instruction generation. Finally, all parameter repair instructions that have completed classification, sorting, global execution sequence number assignment, and supplementary generation basis are integrated into a structured, ordered, and complete set of information. This set is the parameter repair instruction set.

[0121] S5. Distribute the parameter repair instruction set to the corresponding inconsistent node in the target IoT service link, drive the inconsistent node to perform parameter update operation, and feed back the execution result to the parameter management platform.

[0122] In this embodiment of the invention, the step of distributing the parameter repair instruction set to the corresponding inconsistent node in the target IoT service link, driving the inconsistent node to perform a parameter update operation, and feeding back the execution result to the parameter management platform includes:

[0123] The parameter repair instruction set is parsed, and each inconsistent node in the target IoT service link that needs to receive the instruction is determined based on the node identifier contained in each parameter repair instruction.

[0124] Through the secure communication channel between the parameter management platform and each inconsistent node, the corresponding parameter repair instructions are sent to the inconsistent nodes respectively, and after sending, the platform waits for instruction reception confirmation from the nodes.

[0125] Upon receiving a parameter repair instruction, the inconsistent node performs an update operation on its local configuration parameters according to the instruction content and generates a feedback message after the operation is completed.

[0126] The parameter management platform receives feedback messages from each inconsistent node and updates the parameter consistency verification report based on the execution status information in the messages.

[0127] The entire structure of the parameter repair instruction set is analyzed, and each parameter repair instruction in the instruction set is fully parsed. The node identifier for performing the repair operation is accurately extracted from each instruction. All extracted node identifiers are summarized and deduplicated to obtain a unique list of node identifiers. Then, each node identifier in the list is accurately matched with all node information in the target IoT service link to confirm the actual physical node corresponding to each node identifier. All successfully matched actual physical nodes are the inconsistent nodes in the target IoT service link that need to receive instructions. All corresponding parameter repair instructions are bound to each identified inconsistent node to complete the accurate identification of all inconsistent nodes.

[0128] A comprehensive review of the established secure communication channels between the parameter management platform and all nodes in the target IoT service link is conducted to identify the dedicated secure communication channel for each node. Based on the node identifiers of the identified inconsistent nodes, a corresponding dedicated secure communication channel is matched for each inconsistent node. All parameter repair instructions corresponding to the inconsistent node are adapted to the transmission format requirements of the dedicated secure communication channel to ensure that the instruction content conforms to the channel's transmission specifications. The adapted parameter repair instructions are then sent to the corresponding inconsistent nodes through the matched dedicated secure communication channel. After the instructions are sent, the reception monitoring function for this instruction transmission is enabled in the parameter management platform to continuously wait for and capture instruction reception confirmation messages from each inconsistent node until reception confirmation messages are received from all inconsistent nodes or the preset waiting time is reached.

[0129] After receiving the parameter repair command from the parameter management platform through a dedicated secure communication channel, the inconsistent node first verifies the completeness and validity of the command, confirming that the node identifier and parameter identifier contained in the command completely match its own information and that the command content is complete and unaltered. After verification, the node fully parses the command content, accurately locates the parameter storage location in the local configuration parameters corresponding to the parameter identifier in the command, and performs the corresponding update operation on the current value of the parameter at that location according to the core execution content of the command. After the update operation is completed, the node reads the value of the parameter again and confirms that the update result meets the command requirements. Then, it generates a feedback message containing the node identifier, parameter identifier, command reception status, parameter update operation execution status, parameter value before update, and parameter value after update, and sends the feedback message to the parameter management platform through the dedicated secure communication channel.

[0130] After receiving feedback messages from inconsistent nodes through dedicated secure communication channels, the parameter management platform first verifies the completeness and validity of each feedback message, confirming that the message content is intact, unaltered, and that the node identifier can be accurately matched. Once verified, the execution status information contained in each feedback message is precisely extracted. This information includes the specific status of whether the parameter update operation was successful or failed. All feedback messages are categorized and organized according to their execution status to obtain the overall execution result of the parameter repair operation. Then, the original parameter consistency verification report is retrieved. For each inconsistent parameter item in the report, the verification status of that inconsistent item is updated based on the execution status information in the corresponding feedback message. Simultaneously, core information such as the node identifier, parameter identifier, and values ​​before and after the update are added to the detailed information of that inconsistent item. The verification statistics in the report are adjusted synchronously to complete a comprehensive update of the parameter consistency verification report.

[0131] like Figure 2 The diagram shown is a functional block diagram of a cross-service link computer parameter consistency verification system provided in an embodiment of the present invention.

[0132] The cross-service link computer parameter consistency verification system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the cross-service link computer parameter consistency verification system 100 may include a parameter association modeling module 101, a runtime parameter acquisition module 102, a cross-node verification report module 103, a repair instruction generation module 104, and an instruction distribution and execution module 105. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0133] In this embodiment, the functions of each module / unit are as follows:

[0134] The parameter association modeling module 101 is used to establish a parameter association model in the parameter management platform based on the topology of the target IoT service link and the functional role of each node.

[0135] The runtime parameter acquisition module 102 is used to initiate configuration parameter acquisition requests from the parameter management platform to each node in the target IoT service link, and to receive the current runtime parameter data returned by each node.

[0136] The cross-node verification report module 103 is used to perform cross-node consistency verification based on the parameter association model and the current runtime parameter data, and obtain a parameter consistency verification report.

[0137] The repair instruction generation module 104 is used to automatically generate a parameter repair instruction set based on the inconsistent parameter type and the constraint relationship defined in the parameter association model when the parameter consistency verification report indicates that there is parameter inconsistency.

[0138] The instruction distribution and execution module 105 is used to distribute the parameter repair instruction set to the corresponding inconsistent node in the target IoT service link, drive the inconsistent node to perform parameter update operation, and feed back the execution result to the parameter management platform.

[0139] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0140] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0141] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0142] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0143] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for verifying the consistency of computer parameters across service links, characterized in that, The method includes: S1. In the parameter management platform, establish a parameter association model based on the topology of the target IoT service link and the functional role of each node; S2. The parameter management platform initiates a configuration parameter collection request to each node in the target IoT service link and receives the current runtime parameter data returned by each node. S3. Based on the parameter association model and the current runtime parameter data, perform cross-node consistency verification to obtain a parameter consistency verification report, including: Extract a set of configuration parameters from the parameter association model, and classify the parameters in the set of configuration parameters into static baseline parameters and dynamic association parameters; The static baseline parameter values ​​in the current runtime parameter data are directly compared with the expected baseline parameter values ​​defined in the parameter association model to identify static parameter inconsistencies. Based on the constraint relationships defined in the parameter association model, calculate the real-time difference in parameter values ​​of the dynamic association parameter among multiple nodes with association relationships; The real-time difference is compared with the preset dynamic drift threshold in the parameter association model to identify dynamic parameter inconsistencies that exceed the threshold. By combining the static parameter inconsistencies with the dynamic parameter inconsistencies, a parameter consistency verification report is obtained; S4. When the parameter consistency verification report indicates that there is parameter inconsistency, a parameter repair instruction set is automatically generated based on the inconsistent parameter type and the constraint relationship defined in the parameter association model, including: Extract each inconsistent parameter from the parameter consistency verification report; Based on each extracted inconsistency item, the parameter association model is queried to determine the parameter type corresponding to the inconsistency item and its association constraint relationship with other node parameters; Based on the inconsistency type of the inconsistency item, the parameter type, and the associated constraint relationship, a parameter repair instruction is generated for each inconsistency item, including: If the inconsistency type of the inconsistency item is static parameter inconsistency and the parameter type is static baseline parameter, then the generated parameter repair instruction is as follows: set the parameter value of the inconsistency item corresponding to the node identifier to the expected baseline parameter value defined for the parameter in the parameter association model. If the inconsistency type of the inconsistency item is dynamic parameter inconsistency and the parameter type is dynamic associated parameter, then a reference node is determined from the dynamic parameter association group according to the association constraint relationship, and the generated parameter repair instruction content is: synchronize the parameter value of the inconsistency item of the node corresponding to the node identifier to the parameter value of the same parameter identifier in the current runtime parameter data of the reference node. The parameter repair instructions generated for all inconsistencies will be integrated and sorted to form a parameter repair instruction set; S5. Distribute the parameter repair instruction set to the corresponding inconsistent node in the target IoT service link, drive the inconsistent node to perform parameter update operation, and feed back the execution result to the parameter management platform.

2. The method for verifying computer parameter consistency across service links as described in claim 1, characterized in that, In the parameter management platform, a parameter association model is established based on the topology of the target IoT service link and the functional role of each node, including: Analyze the topology of the target IoT service link and extract the connection relationships and communication path information between nodes; Based on the functional role of each node, retrieve the standard parameter template corresponding to the functional role from the pre-configured parameter template library; Based on the connection relationship, the communication path information, and the standard parameter template, a parameter association model is constructed.

3. The method for verifying computer parameter consistency across service links as described in claim 1, characterized in that, The parameter management platform initiates configuration parameter collection requests to each node in the target IoT service link and receives current runtime parameter data returned by each node, including: Based on the set of configuration parameters defined in the parameter association model, determine the specific parameter identifiers that need to be collected from each node in the target IoT service link; Based on the specific parameter identifier, a structured acquisition request message is generated; The collection request message is sent to the corresponding node through the established communication link between the parameter management platform and each node. The system receives encapsulated data packets returned by each node in response to the acquisition request message, parses and verifies the encapsulated data packets, and obtains the current runtime parameter data.

4. The method for verifying computer parameter consistency across service links as described in claim 1, characterized in that, The step of calculating the real-time difference in parameter values ​​of the dynamic association parameter among multiple nodes with association relationships, based on the constraint relationships defined in the parameter association model, includes: Based on the parameter association model, determine the dynamic parameter groups that need to be verified for consistency. Obtain the current runtime parameter data of the parameter returned by all nodes within the dynamic parameter group, and form a real-time parameter value set; Select a reference value from the set of real-time parameter values; Calculate the deviation between each value in the real-time parameter value set and the reference value, and aggregate the deviations to obtain the real-time difference degree between all nodes.

5. The method for verifying computer parameter consistency across service links as described in claim 4, characterized in that, The step of calculating the deviation between each value in the real-time parameter value set and the reference value, and aggregating the deviations to obtain the real-time difference between all nodes, includes: Calculate the absolute difference between each value in the set of real-time parameter values ​​and the reference value to obtain the individual deviation corresponding to each value; From the individual biases mentioned above, select the individual bias with the largest value; The individual deviation with the largest selected value is taken as the real-time difference degree of the consistency of parameters of all nodes within the dynamic parameter group.

6. The method for verifying computer parameter consistency across service links as described in claim 1, characterized in that, The step of distributing the parameter repair instruction set to the corresponding inconsistent nodes in the target IoT service link, driving the inconsistent nodes to perform parameter update operations, and feeding back the execution results to the parameter management platform includes: The parameter repair instruction set is parsed, and each inconsistent node in the target IoT service link that needs to receive the instruction is determined based on the node identifier contained in each parameter repair instruction. Through the secure communication channel between the parameter management platform and each inconsistent node, the corresponding parameter repair instructions are sent to the inconsistent nodes respectively, and after sending, the platform waits for instruction reception confirmation from the nodes. Upon receiving a parameter repair instruction, the inconsistent node performs an update operation on its local configuration parameters according to the instruction content and generates a feedback message after the operation is completed. The parameter management platform receives feedback messages from each inconsistent node and updates the parameter consistency verification report based on the execution status information in the messages.

7. A cross-service link computer parameter consistency verification system, used to implement the cross-service link computer parameter consistency verification method of claim 1, the system comprising: The parameter association modeling module is used to establish a parameter association model in the parameter management platform based on the topology of the target IoT service link and the functional role of each node. The parameter acquisition module is used to initiate configuration parameter acquisition requests from the parameter management platform to each node in the target IoT service link, and to receive the current runtime parameter data returned by each node. The cross-node consistency verification report module is used to perform cross-node consistency verification based on the parameter association model and the current runtime parameter data, and obtain a parameter consistency verification report, including... Extract the configuration parameter set from the parameter association model, and distinguish the parameters in the configuration parameter set into static baseline parameters and dynamic association parameters; The static baseline parameter values ​​in the current runtime parameter data are directly compared with the expected baseline parameter values ​​defined in the parameter association model to identify static parameter inconsistencies. Based on the constraint relationships defined in the parameter association model, calculate the real-time difference in parameter values ​​of the dynamic association parameter among multiple nodes with association relationships; The real-time difference is compared with the preset dynamic drift threshold in the parameter association model to identify dynamic parameter inconsistencies that exceed the threshold. By combining the static parameter inconsistencies with the dynamic parameter inconsistencies, a parameter consistency verification report is obtained; The repair instruction generation module is used to automatically generate a parameter repair instruction set based on the inconsistent parameter type and the constraint relationships defined in the parameter association model when the parameter consistency verification report indicates that parameter inconsistency exists. This includes... Extract each inconsistent parameter from the parameter consistency verification report; Based on each extracted inconsistency item, the parameter association model is queried to determine the parameter type corresponding to the inconsistency item and its association constraint relationship with other node parameters; Based on the inconsistency type of the inconsistency item, the parameter type, and the associated constraint relationship, a parameter repair instruction is generated for each inconsistency item, including: If the inconsistency type of the inconsistency item is static parameter inconsistency and the parameter type is static baseline parameter, then the generated parameter repair instruction is as follows: set the parameter value of the inconsistency item corresponding to the node identifier to the expected baseline parameter value defined for the parameter in the parameter association model. If the inconsistency type of the inconsistency item is dynamic parameter inconsistency and the parameter type is dynamic associated parameter, then a reference node is determined from the dynamic parameter association group according to the association constraint relationship, and the generated parameter repair instruction content is: synchronize the parameter value of the inconsistency item of the node corresponding to the node identifier to the parameter value of the same parameter identifier in the current runtime parameter data of the reference node. The parameter repair instructions generated for all inconsistencies will be integrated and sorted to form a parameter repair instruction set; The instruction distribution and execution module is used to distribute the parameter repair instruction set to the corresponding inconsistent node in the target IoT service link, drive the inconsistent node to perform parameter update operation, and feed back the execution result to the parameter management platform.

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