A report generation system based on multi-protocol adapted IoT gateway communication

Abstract

This invention discloses a report generation system based on multi-protocol adapted IoT gateway communication, belonging to the field of data communication technology. It unifies the access of multiple heterogeneous devices through various protocols, collects raw record data through a cluster of inspection and testing equipment, and automatically uploads the collected raw record data to a cloud platform. A raw record library is used to store the collected raw record data, which is categorized and stored within the library. The raw record data in the library is then processed and analyzed using corresponding parsing algorithms. This invention enables unified access of heterogeneous devices through multiple protocols, breaking down communication barriers between devices of different brands and models, achieving integrated and unified management of multiple devices. It solves the core pain points of traditional testing, such as scattered equipment and inability to share data. The raw record library also effectively prevents data leakage and tampering risks, ensuring that only authorized personnel can access the relevant raw data.

Description

Technical Field

[0001] This invention relates to the field of data communication technology, and more specifically to a report generation system based on multi-protocol adapted IoT gateway communication. Background Technology

[0002] Currently, the testing and inspection industry, as the core of quality infrastructure, is at a critical stage of digital and intelligent transformation. In industry practice, testing and inspection institutions generally face the challenge of compatibility with heterogeneous equipment. Various brands and models of testing equipment are numerous and their communication protocols are not unified, resulting in serious "data silos." This leads to the need for manual intervention in the collection of raw data, which is not only inefficient but also prone to data transcription errors. It is difficult to achieve the integration and unified management of data from multiple devices. At the same time, most systems cannot achieve automatic adaptation and access to heterogeneous devices with multiple protocols. Data preprocessing capabilities are insufficient, raw data is stored in a chaotic manner, the compatibility of parsing algorithms and testing analysis methods is weak, and the security protection mechanism for data access is imperfect. This makes it impossible to fully meet the needs of testing institutions for full lifecycle data management and to adapt to the stringent requirements for data quality and data security in the era of smart supervision. Summary of the Invention

[0003] The purpose of this invention is to provide a report generation system based on multi-protocol adapted IoT gateway communication to solve the problems in the background technology.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a report generation system based on multi-protocol adapted IoT gateway communication, comprising: The data acquisition end includes a cluster of testing and inspection equipment and a group of physical interfaces; it connects multiple heterogeneous devices through various protocols and collects raw record data through the cluster of testing and inspection equipment. Configure an edge computing gateway for the corresponding data acquisition terminal, and automatically upload the raw record data collected by the acquisition terminal to the cloud platform through the edge computing gateway; The cloud platform includes a raw record library, which is used to store the collected raw record data. The raw record data is stored in the raw record library in categories. When data parsing and reading are required, the raw record data in the raw record library is used to perform calculations and parsing on the data using the corresponding parsing algorithm. After the data is parsed, it is assigned to the original record report according to the preset test report template. The test personnel then review, calibrate, and approve the data. Once approved, the original record report is generated and archived.

[0005] In a preferred embodiment, the multiple protocols include, but are not limited to, one or more of Modbus, OPC UA, TCP / IP, HTTP, and MQTT; Based on the communication protocol type of each heterogeneous device in the testing and inspection equipment cluster, the corresponding adaptation protocol is automatically matched to complete the unified access of multiple heterogeneous devices.

[0006] In a preferred embodiment, the edge computing gateway performs preliminary preprocessing on the original record data, which includes data deduplication, data format standardization, and data integrity verification to remove invalid data and unify the data format.

[0007] In a preferred embodiment, the raw record data in the raw record library is classified and stored according to a preset classification rule, wherein the preset classification rule includes one or more combinations of inspection and testing equipment type, inspection and testing items, and data acquisition time.

[0008] In a preferred embodiment, the cloud platform has multiple parsing algorithms built in, and each parsing algorithm corresponds one-to-one with the analysis method corresponding to the original record data. The corresponding parsing algorithm is automatically called according to the analysis method corresponding to the original record data.

[0009] In a preferred embodiment, the original record library includes storage space and access channel, the access channel being connected to the storage space, wherein the access channel includes a docking channel and a docking chain; The corresponding access channel is marked as an authorized end, and the authorized end obtains the original record data in the storage space through the access channel; The original record data obtained from the authorized end will be processed and analyzed using the corresponding parsing algorithm according to the corresponding analysis method.

[0010] In a preferred embodiment, the access channel includes: In the cloud platform, a preset amount of data access space is configured for the corresponding storage space. Multiple nodes are configured for the corresponding access space. One node is used as the access entry point, and the node far from the access entry point is used as the access point. The access point is connected to the storage space, and the remaining nodes are connected to each other to obtain the docking channel. Multiple docking points are deployed inside the docking channel, and these docking points are linked together to form a docking chain. Each docking point stores multiple sub-docking points, which are isolated from each other. The docking point at one end of the docking chain is connected to the access point, and the docking point at the other end of the docking chain is connected to the access point.

[0011] In a preferred embodiment, the step of the corresponding access channel marking authorization end, and the authorization end obtaining the original record data in the storage space through the access channel, includes: The authorized end records the connection point, and the authorized end accesses the access channel through the access entry point. It is then guided to the access point through the connection chain and accesses the original record data in the storage space through the access point. When an unauthorized port accesses a channel, two scenarios occur. The first is that the unauthorized port accesses a random node and circulates through the network of multiple nodes. The second is that the unauthorized port accesses the access point, and the unauthorized port sequentially accesses the connection chain. If the connection point determines that the port is unauthorized, it will connect to a sub-connection point within the connection point currently accessing the unauthorized port. The unauthorized port is temporarily redirected to any node through the sub-connection point, circulating through the network of multiple nodes, and then disconnected from the sub-connection point.

[0012] In a preferred embodiment, the preset test report template is customized, modified, and saved by personnel according to the inspection and testing standards and test item requirements. The report assignment module automatically identifies the field types in the template and performs parsing and matching of the results with the template fields. Personnel manually calibrate and modify the data parsing results and report values. After calibration and approval, a unique identifier is assigned to the original record report, and it is stored in the cloud platform's archive database according to preset archiving rules, while an archive log is generated.

[0013] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention enables the unified access of heterogeneous devices through multiple protocols, completely breaking down communication barriers between devices of different brands and models. It achieves integrated and unified management of multiple devices, solving the core pain points of traditional testing where devices are scattered and data cannot be shared. Simultaneously, relying on the preliminary preprocessing function of the edge computing gateway, it performs data deduplication, format standardization, and integrity verification on the collected raw data, effectively eliminating invalid and redundant data, standardizing data formats, and avoiding subsequent parsing deviations caused by data disorder or missing data. This ensures that the collected raw data is complete, standardized, and accurate, laying a solid foundation for subsequent data analysis and report generation, significantly reducing the workload of manual data processing, and improving the automation level and efficiency of data collection.

[0014] The cloud platform's built-in raw record library employs a categorized storage mechanism, combining multi-dimensional classification rules such as testing equipment type, testing items, and collection time to systematically archive raw data. This completely solves the problems of chaotic data storage and inconvenient retrieval in traditional systems. The raw record library effectively intercepts unauthorized ports through a dedicated access channel and authorization management mechanism, along with a multi-node layout and sub-interface isolation design. Whether an unauthorized port randomly accesses a node or attempts to intrude through the access point, it will be guided to a node for cyclical access and eventually disconnected, effectively preventing data leakage and tampering risks and ensuring that only authorized personnel can access the relevant raw data. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown in this embodiment, a report generation system based on multi-protocol adapted IoT gateway communication includes: The data acquisition end includes a cluster of testing and inspection equipment and a group of physical interfaces; it connects multiple heterogeneous devices through various protocols and collects raw record data through the cluster of testing and inspection equipment. Configure an edge computing gateway for the corresponding data acquisition terminal, and automatically upload the raw record data collected by the acquisition terminal to the cloud platform through the edge computing gateway; The cloud platform includes a raw record library, which is used to store the collected raw record data. The raw record data is stored in the raw record library in categories. When data parsing and reading are required, the raw record data in the raw record library is used to perform calculations and parsing on the data using the corresponding parsing algorithm. After the data is parsed, it is assigned to the original record report according to the preset test report template. The test personnel then review, calibrate, and approve the data. Once approved, the original record report is generated and archived.

[0019] It should be noted that the cloud platform includes a raw record library (which is built with an isolation architecture to protect data from network attacks). The raw record library is used to store the collected raw record data. The raw record data is stored in the raw record library in a categorized manner. When data parsing and reading are required, the raw record data in the raw record library is processed using the corresponding parsing algorithm. After parsing, the data is assigned a raw record report value according to the preset detection report template. The detection personnel review and calibrate the data. After the review is approved, a raw record report is generated and archived.

[0020] In one embodiment, the multiple protocols include, but are not limited to, one or more of Modbus, OPC UA, TCP / IP, HTTP, and MQTT; Based on the communication protocol type of each heterogeneous device in the testing and inspection equipment cluster, the corresponding adaptation protocol is automatically matched to complete the unified access of multiple heterogeneous devices.

[0021] It should be noted that the system automatically detects and identifies the communication protocol types of heterogeneous devices in the testing and inspection equipment cluster, and automatically matches and loads the corresponding protocol adapter driver. Communication link establishment and data interaction can be completed without manual configuration, thereby realizing unified access, unified data acquisition, unified format conversion and unified uploading of heterogeneous testing and inspection equipment of multiple brands, models and communication methods. This provides a standardized data foundation for subsequent original record data parsing, automatic report assignment and review archiving.

[0022] In one embodiment, the edge computing gateway performs preliminary preprocessing on the raw record data. The preliminary preprocessing includes data deduplication, data format standardization, and data integrity verification, which are used to remove invalid data, unify the data format, and ensure the accuracy and standardization of the data uploaded to the cloud platform's raw record library.

[0023] It should be noted that the initial preprocessing of the edge computing gateway is a lightweight data cleaning, verification, and standardization performed locally in real time before the data is uploaded to the cloud. It does not change the authenticity, value, or source of the original detection data; it only processes the validity, format, and completeness of the data. The purpose is to eliminate invalid, erroneous, and duplicate data, standardize the data structure, prevent dirty data from entering the cloud's original record library, and ensure the accuracy and reliability of the entire process of subsequent parsing, calculation, report assignment, review, and archiving.

[0024] In one embodiment, the raw record data in the raw record library is classified and stored according to a preset classification rule. The preset classification rule includes one or more combinations of inspection and testing equipment type, inspection and testing items, and data acquisition time to facilitate the rapid retrieval, retrieval, and parsing of raw record data.

[0025] It should be noted that the raw record library is used to store raw record data uploaded by the edge computing gateway. The raw record data in the library is automatically classified and stored according to preset classification rules. The preset classification rules include one or more combinations of inspection and testing equipment type, inspection and testing items, and data acquisition time. The corresponding classification is automatically matched based on the equipment identifier, test item identifier, and acquisition timestamp carried in the raw record data, establishing a multi-level classification storage structure and a global retrieval index. Through classified storage, the raw record data is organized in an orderly manner, managed in a standardized manner, located quickly, retrieved accurately, and parsed efficiently, providing efficient data support for subsequent data algorithm analysis, automatic report assignment, inspection personnel review and calibration, and report archiving.

[0026] In one embodiment, the cloud platform incorporates multiple analytical algorithms, each corresponding one-to-one with the analytical method corresponding to the original recorded data. For example, one such analytical method could be the alkaline potassium permanganate titration method for determining oxygen consumption. This method is suitable for determining oxygen consumption in water samples with chloride ion concentrations greater than 300 mg / L, used in groundwater resource surveys, evaluations, monitoring, and utilization. The limit of quantitation for this method is 0.4 mg / L, and the measurement range is 0.4 mg / L to 4.0 mg / L. Other analytical methods include acidic potassium permanganate titration and potassium dichromate titration. The acidic potassium permanganate titration method corresponds to the following analytical algorithm: using potassium permanganate as the oxidant, after titration under acidic conditions, the amount of potassium permanganate consumed is calculated using the original recorded data such as titration volume and reagent concentration, and then the oxygen consumption is calculated. The core algorithm logic is consistent with the alkaline method, but it needs to be adjusted to match the reaction coefficient under acidic conditions and the experimental data of the acidic titration (such as the amount of acidic reagent used and the volume data corresponding to the titration endpoint). The analytical algorithm corresponding to the potassium dichromate titration method is based on the redox reaction between potassium dichromate and reducing substances in the water sample under strongly acidic conditions. The remaining potassium dichromate consumption is determined by titration. Combining this with raw data such as the standard concentration of potassium dichromate, titration volume, and water sample volume, the oxygen consumption is calculated using a redox reaction stoichiometric algorithm. This algorithm needs to be adapted to the reaction equivalence of potassium dichromate (which differs from the stoichiometric coefficient in the potassium permanganate titration method). The corresponding analytical algorithm is automatically invoked based on the analysis method corresponding to the original recorded data, ensuring the accuracy and efficiency of data analysis.

[0027] In one embodiment, the original record library includes storage space and access channel, the access channel being connected to the storage space, wherein the access channel includes docking channel and docking chain; The corresponding access channel is marked as an authorized end, and the authorized end obtains the original record data in the storage space through the access channel; The original record data obtained from the authorized end will be processed and analyzed using the corresponding parsing algorithm according to the corresponding analysis method.

[0028] In one embodiment, the access channel includes: In the cloud platform, a preset amount of data access space is configured for the corresponding storage space. Multiple nodes are configured for the corresponding access space. One node is used as the access entry point, and the node far from the access entry point is used as the access point. The access point is connected to the storage space, and the remaining nodes are connected to each other to obtain the docking channel. Multiple docking points are deployed inside the docking channel, and these docking points are linked together to form a docking chain. Each docking point stores multiple sub-docking points, which are isolated from each other. The docking point at one end of the docking chain is connected to the access point, and the docking point at the other end of the docking chain is connected to the access point.

[0029] In one embodiment, the step of the corresponding access channel marking authorization end, and the authorization end obtaining the original record data in the storage space through the access channel, includes: The authorized end records the connection point, and the authorized end accesses the access channel through the access entry point. It is then guided to the access point through the connection chain and accesses the original record data in the storage space through the access point. When an unauthorized port accesses a channel, two scenarios occur. The first is that the unauthorized port accesses a random node and circulates through the network of multiple nodes. The second is that the unauthorized port accesses the access point, and the unauthorized port sequentially accesses the connection chain. If the connection point determines that the port is unauthorized, it will take over the connection from a sub-connection point within the current connection point. The sub-connection point temporarily guides the unauthorized port to any node (the sub-connection point temporarily establishes a connection with any node), allowing the unauthorized port to circulate through the network of multiple nodes. Finally, the unauthorized port is disconnected from the sub-connection point.

[0030] It should be noted that, to ensure the security of the original record data, a storage space and an access channel are set up in the cloud platform. As the original record repository, the storage space is used to store the original record data. The storage space is protected by the access channel, which is a data space located in front of the storage space and accessed. The access channel includes the access space, and multiple nodes are configured for the corresponding access space. These nodes are not used to access or connect to the access space, but are only deployed in the access space. One node is selected as the access entry point of the access space and connected to it. The nodes far from the access entry point are used as access points and connected to the storage space. The access point is the only access port of the storage space. The remaining nodes are connected to each other to form an interlaced node network. Here, the nodes are virtual machines. The above is the connection channel. A docking chain is set up between the access entry point and the access point. Multiple spare positions are also deployed at the corresponding docking points in the access space. These spare positions are communication points (communication nodes with closed connections). Multiple docking points in the docking chain are linked together. Each docking point stores multiple sub-docking points. Both docking points and sub-docking points are virtual signal carriers. The connection between docking points is a self-connection. Sub-docking points are not enabled. Each docking point records an authorized end. Authorized ends correspond to access entry points. Authorized ends can directly select access entry points for access. Other access ports cannot obtain confirmed access entry points and will most likely access nodes. Nodes cannot access the access space and access the storage space through access points. Unauthorized ports will most likely access nodes by directly looping through the closed connection network formed by the nodes. If an unauthorized port accesses an access entry point, it will continue to access the docking chain. The docking points on the docking chain determine whether it is an authorized end. The unauthorized end's unauthorized access is then blocked. The authorized port is treated as an abnormal port. Then, a sub-connection point within the connection point of the unauthorized port is activated. The unauthorized port is connected through this sub-connection point. The activated sub-connection point detaches from the connection point and is transferred to any spare point. The selected spare point needs to temporarily establish a connection with the connection point. After the transfer, the connection between the spare point and the connection point is disconnected. The detached sub-connection point is immediately connected to any node, and a temporary connection is established between the spare point and that arbitrary node. The unauthorized port is then guided to the node for access. This process creates a loop of unauthorized ports accessing the network of multiple nodes until the unauthorized port exits access. Afterward, the sub-connection point is returned to its original corresponding connection point, and the spare point is restored to a completely disconnected state. A completely disconnected state means the spare point is not connected to any carrier. This access channel allows for port screening and protection before access to storage space without hindering access to normal ports, thus preventing unauthorized port access and providing good data protection.

[0031] In one embodiment, the preset test report template is customized, modified, and saved by personnel according to the testing standards and test item requirements. The report assignment module automatically identifies the field types in the template and performs parsing and matching of the results with the template fields. Personnel manually calibrate and modify the data parsing results and report values. After calibration and approval, a unique identifier is assigned to the original record report, and it is stored in the cloud platform's archive database according to preset archiving rules, while an archive log is generated.

[0032] It should be noted that the cloud platform has a built-in template editing module, allowing testing personnel to create preset test report templates based on different testing items, industry standards, and testing needs. Preset test report templates must include fixed fields and configurable fields. Fixed fields include basic information such as test number, test date, testing equipment information, testing personnel information, and test item name. Configurable fields correspond to various result data after parsing the original record data (such as test values, deviation values, pass / fail results, etc.). Each configurable field has a unique identifier, which corresponds one-to-one with the result data identifier output by the parsing algorithm, ensuring accurate assignment. The template editing module also supports template saving, modification, deletion, version management, and access control. Different testing items correspond to different templates, and historical versions are retained after template updates for easy traceability. Only authorized testing personnel can edit templates to prevent accidental modification and ensure template standardization and consistency. Templates also support import and export functions, allowing for quick adjustments to template content based on updated testing standards. After the original record data is processed and parsed by the cloud platform's parsing algorithm, the parsing result carries a unique data identifier (this identifier is associated one-to-one with the result type and test item preset by the parsing algorithm). The cloud platform's report assignment module automatically reads the parsing results and corresponding data identifiers, and simultaneously calls a preset test report template. It identifies the unique identifiers of configurable fields in the template and establishes a mapping relationship between the parsing result data identifiers and template field identifiers. Based on this mapping, the report assignment module automatically fills the parsed data (including raw test values, algorithm-calculated analysis values, deviation analysis results, and pass / fail conclusions) into the corresponding configurable fields of the test report template, achieving automatic assignment of the original record report. During the assignment process, the system automatically verifies the consistency between the data format and the template field requirements. If a data format mismatch occurs (such as inconsistent numerical precision or units), a prompt is immediately triggered, the assignment is paused, and feedback is sent to the testing personnel. The assignment operation is re-executed after calibration. A log is generated throughout the assignment process, recording the assignment time, the parsing result data, the corresponding template name, the assigned fields, and the operator (automatically identified by the system). The log is stored in association with the report to be reviewed, facilitating traceability of the assignment process and error identification during subsequent review. After logging into the cloud platform's calibration review module, the system automatically pushes a list of reports awaiting review. The list is sorted by collection time and testing item for efficient processing. The reviewers examine each report, focusing on verifying the following: the accuracy of the parsed results and template field assignments; the standardization of data format; the consistency between the test values ​​and the original record data; and the reasonableness of the pass / fail conclusions. During the review process, the reviewers can add comments (e.g., "Data assignment is accurate, review approved," "Value deviation in a certain field, calibration required") and mark the review status (pending review, approved, rejected).If errors in assignment, deviations in parsing results, or data anomalies are found during the review process, the testing personnel can initiate a calibration operation. Calibration is performed in two ways: manual calibration and automatic calibration. The testing personnel manually modify the incorrect assignments in the template based on the original recorded data and the parsing algorithm logic. After modification, a calibration description must be filled out (such as the reason for calibration, the basis for calibration, and the values ​​before and after calibration). Automatic calibration is performed when the testing personnel trigger the calibration command. The system then re-calls the corresponding parsing algorithm to recalculate and parse the original recorded data, generating new parsing results that automatically overwrite the original incorrect assignments. The automatic calibration process and results are recorded simultaneously. For high-precision, high-requirement testing projects, a multi-level review process can be set up. After the initial review is passed, the report is automatically pushed to a senior reviewer for a second review. Only after the second review is passed can the report proceed to the subsequent archiving stage. If any level of review fails, the report is returned to the previous stage (assignment stage or initial review stage) for correction and resubmission. Once the testing personnel have completed the review and calibration, and the review status is marked as "Review Passed," the cloud platform's report generation module automatically triggers the report generation operation. The system then reads the audited and calibrated report content, supplements it with key information such as the report number (automatically generated by the system according to preset rules, including the testing date, testing item code, and unique serial number to ensure uniqueness), the auditor's signature (electronic signature, automatically generated by the system with linked auditor information), and the calibration personnel's signature (if applicable), generating a standard format original record report (supporting archiveable formats such as PDF and Word). After the report is generated, the system automatically performs a completeness check, checking for missing fields, incomplete signature information, data anomalies, etc. If any issues are found, a prompt is immediately triggered, the archiving operation is paused, and feedback is sent to the testing personnel for processing; if the check passes, the report enters the archiving stage. The verified original record report is automatically stored in the cloud platform's archiving database by the cloud platform's archiving module according to preset archiving rules. Archiving rules can be customized by the user, including categorizing archiving by testing item, testing date, testing equipment, testing personnel, etc., and simultaneously establishing a report index (linked to key information such as report number, testing item, and collection time) for easy subsequent retrieval and querying. After archiving is completed, the system generates an archiving log, recording information such as archiving time, report number, archiving path, and operator. The log is permanently stored and cannot be tampered with. If issues such as mismatched data identifiers or incorrect data formats occur during the assignment process, the system will automatically pause the assignment, generate an exception message, and provide feedback to the testing personnel. The testing personnel can then verify the consistency between the parsed result identifier and the template field identifier, correct the data format, and re-trigger the assignment operation. If significant data anomalies are discovered during the review process (such as a serious discrepancy between the parsed result and the original record data), the testing personnel can reject the report and trigger a re-parsing instruction for the original record data. The cloud platform will then re-call the parsing algorithm to calculate and parse the original record data, generate new parsing results, and re-execute the assignment and review process.If storage failures or report verification failures occur during the archiving process, the system will automatically record the exception information and attempt to re-archive. If archiving fails multiple times, the system will report back to the administrator to investigate issues such as the storage status of the archiving database and the report format. Once the issues are resolved, archiving will be retried.

[0033] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A report generation system based on multi-protocol adapted IoT gateway communication, characterized in that, include: The data acquisition end includes a cluster of testing and inspection equipment and a group of physical interfaces; Multiple heterogeneous devices are connected in a unified manner through various protocols, and raw record data is collected through a cluster of testing and inspection equipment. Configure an edge computing gateway for the corresponding data acquisition terminal, and automatically upload the raw record data collected by the acquisition terminal to the cloud platform through the edge computing gateway; The cloud platform includes a raw record library, which is used to store the collected raw record data. The raw record data is stored in the raw record library in categories. When data parsing and reading are required, the raw record data in the raw record library is used to perform calculations and parsing on the data using the corresponding parsing algorithm. After the data is parsed, it is assigned to the original record report according to the preset test report template. The test personnel then review, calibrate, and approve the data. Once approved, the original record report is generated and archived.

2. The report generation system based on multi-protocol adapted IoT gateway communication according to claim 1, characterized in that, The various protocols include, but are not limited to, one or more of Modbus, OPC UA, TCP / IP, HTTP, and MQTT; Based on the communication protocol type of each heterogeneous device in the testing and inspection equipment cluster, the corresponding adaptation protocol is automatically matched to complete the unified access of multiple heterogeneous devices.

3. The report generation system based on multi-protocol adapted IoT gateway communication according to claim 1, characterized in that, The edge computing gateway performs preliminary preprocessing on the original record data, which includes data deduplication, data format standardization, and data integrity verification, to remove invalid data and unify the data format.

4. The report generation system based on multi-protocol adapted IoT gateway communication according to claim 1, characterized in that, The original record data in the original record library is classified and stored according to preset classification rules. The preset classification rules include one or more combinations of inspection and testing equipment type, inspection and testing items, and data acquisition time.

5. A report generation system based on multi-protocol adapted IoT gateway communication according to claim 1, characterized in that, The cloud platform has multiple built-in parsing algorithms, each corresponding to a specific analysis method for the original recorded data. The corresponding parsing algorithm is automatically invoked based on the analysis method for the original recorded data.

6. A report generation system based on multi-protocol adapted IoT gateway communication according to claim 1, characterized in that, The original record library includes storage space and access channels. The access channels are connected to the storage space, and the access channels include docking channels and docking chains. The corresponding access channel is marked as an authorized end, and the authorized end obtains the original record data in the storage space through the access channel; The original record data obtained from the authorized end will be processed and analyzed using the corresponding parsing algorithm according to the corresponding analysis method.

7. A report generation system based on multi-protocol adapted IoT gateway communication according to claim 6, characterized in that, The access channels include: In the cloud platform, a preset amount of data access space is configured for the corresponding storage space. Multiple nodes are configured for the corresponding access space. One node is used as the access entry point, and the node far from the access entry point is used as the access point. The access point is connected to the storage space, and the remaining nodes are connected to each other to obtain the docking channel. Multiple docking points are deployed inside the docking channel, and these docking points are linked together to form a docking chain. Each docking point stores multiple sub-docking points, which are isolated from each other. The docking point at one end of the docking chain is connected to the access point, and the docking point at the other end of the docking chain is connected to the access point.

8. A report generation system based on multi-protocol adapted IoT gateway communication according to claim 7, characterized in that, The step of the authorized end, which is marked as the corresponding access channel, and the authorized end obtains the original record data in the storage space through the access channel includes: The authorized end records the connection point, and the authorized end accesses the access channel through the access entry point. It is then guided to the access point through the connection chain and accesses the original record data in the storage space through the access point. When an unauthorized port accesses a channel, two scenarios occur. The first is that the unauthorized port accesses a random node and circulates through the network of multiple nodes. The second is that the unauthorized port accesses the access point, and the unauthorized port sequentially accesses the connection chain. If the connection point determines that the port is unauthorized, it will connect to a sub-connection point within the connection point currently accessing the unauthorized port. The unauthorized port is temporarily redirected to any node through the sub-connection point, circulating through the network of multiple nodes, and then disconnected from the sub-connection point.

9. A report generation system based on multi-protocol adapted IoT gateway communication according to claim 1, characterized in that, The preset test report template can be customized, modified, and saved by personnel according to the inspection and testing standards and test item requirements. The report assignment module automatically identifies the field types in the template and performs parsing and matching of the results with the template fields. Personnel manually calibrate and modify the data parsing results and report values. After calibration and approval, a unique identifier is assigned to the original record report, and it is stored in the cloud platform's archive database according to preset archiving rules, while an archive log is generated.

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