Intelligent data processing and collaboration architecture integrating data, table and account space

Abstract

The invention discloses a data, table and account space integrated intelligent data processing and collaborative architecture, relates to the field of data processing and intelligent management, and solves the technical problems that in the prior art, management of various tables and accounts is time-consuming and labor-consuming, and is difficult to quickly adapt to dynamic business scenes. The method specifically comprises the steps that a human-computer interaction layer analyzes storage data streams of all business processes, whether the storage data streams have acquirability or not is used as table category division, meanwhile, importance distinguishing is carried out to obtain key types and non-key types, tables are constructed in different scenes, data floating is recorded, and decision making is carried out according to floating marks; the business account checking layer constructs account data, forms an account data chain according to the account data, performs adjustment synchronization according to data floating, keeps adjustment records to construct an account checking chain, and the financial process stores and monitors the account data chain and the account checking chain.

Description

Technical Field

[0001] The present invention relates to the field of data processing and intelligent governance, and specifically to an intelligent data processing and collaborative architecture that integrates data, tables, and account spaces. Background Art

[0002] A business ledger is a tool used by an enterprise or organization to record and track various business information in detail during its business activities, usually in a table form;

[0003] At present, enterprises generally face the following challenges in data management and business processes: serious data silos and low efficiency of data collaboration across departments and platforms; traditional data management tools are difficult to meet the needs of real-time, intelligent and dynamic adjustment; the management of various forms and ledgers is time-consuming and labor-intensive, and it is difficult to quickly adapt to dynamic business scenarios; the lack of intelligent data analysis and feedback mechanisms leads to inefficient decision-making.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose an intelligent data processing and collaboration architecture that integrates digital tables and account spaces.

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

[0007] The intelligent data processing and collaboration architecture of the data table and account space integration includes the data layer, the business reconciliation layer, the human-computer interaction layer and the intelligent ecological layer; the input end of the data layer is electrically connected to the underlying data collection end, the output end of the data layer is electrically connected to the input end of the business reconciliation layer, the output end of the business reconciliation layer is electrically connected to the input end of the human-computer interaction layer, the output end of the human-computer interaction layer is electrically connected to the input end of the intelligent ecological layer, and the output end of the intelligent ecology is electrically connected to the real-time user terminal;

[0008] The data layer constructs data streams and manages the data streams throughout their life cycle. As business processes progress, data increases in the data streams between business processes, and data collection and data governance are performed. After completion, the impact trend data and common parameter data of the current adjacent business processes are inferred through analysis, and the real-time data streams are stored and timestamped.

[0009] The human-computer interaction layer analyzes the storage data flow of all business processes, classifies tables based on whether they are collectible, and distinguishes between key and non-key types based on importance. It also constructs tables and records data fluctuations in different scenarios, and makes decisions based on floating marks.

[0010] The business reconciliation layer constructs ledger data and forms a ledger data chain based on the ledger data. It also adjusts and synchronizes according to data fluctuations, retains adjustment records to build a ledger reconciliation chain, and the financial process stores and monitors the ledger data chain and the ledger reconciliation chain.

[0011] The intelligent ecological layer builds a data sharing platform and fills the data in the platform according to the real-time data flow of the business process. It also sets permissions based on the departments to which each business process belongs and accesses the data sharing platform based on the set permissions.

[0012] As a preferred embodiment of the present invention, the data layer process is as follows:

[0013] Data is collected through real-time data streams and governed. Error data corresponding to adjacent business processes are collected and removed from the data stream. The error data is judged based on the deviation of adjacent input data. If the data cleared and re-entered after the current input deviates from the historical input data, it is inferred that the data input is incorrect.

[0014] As a preferred implementation mode of the present invention, when the data flow storage volume is increased, the front-end ownership of the corresponding business process is analyzed when various types of data are generated in the data flow of the corresponding business process. If the business process front-end directly obtains the data, the front-end has direct control. Conversely, if the business process front-end needs to increase the data collection process, the front-end is used for indirect control, such as customer authorization information of the business department; and the revenue generated by the data in the data flow for the enterprise where the business process is located and the corresponding control rights are marked as data collectibility information; and it is stored together with the data flow data storage.

[0015] As a preferred embodiment of the present invention, the human-computer interaction layer process is as follows:

[0016] According to the collectability information of the data stored in the data flow, the table category is divided according to whether it is collectable or not. At the same time, the importance of the storage data type corresponding to the business process is distinguished. That is, if the storage data type has numerical fluctuations, the corresponding business process needs to be adjusted, and if there are fluctuations in other types of storage data, the corresponding type of storage data will be set as the key type.

[0017] As a preferred implementation mode of the present invention, when there is no floating of the key type of the business process, a table is constructed in sequence according to the appearance time of the data type corresponding to the data flow where the business process is located, and the floating amount of the key type data corresponding to multiple historical moments is marked. When the key type of the business process floats, the table is constructed in the order of the appearance time of the data type, and the floating is marked. At the same time, the floating time of the key type data is used as the time point of the non-key type or key type affected by the floating, and a table is constructed based on the time point and the floating is marked. According to the table template and the actual scenario, various types of data of the data flow are filled, and decisions are made based on the floating mark.

[0018] As a preferred embodiment of the present invention, the process of the business reconciliation layer is as follows:

[0019] Digital reconciliation management is carried out according to the data flow of each business process, with the financial process, supply chain process and sales process as the multi-scenario convergence chain; the sales process is used as the front end to collect external business data, and the supply chain process is used as the middle end to execute the external business data, and there is additional business data during the data execution process; the financial process is used as the terminal to integrate the enterprise ledger and ensure that the enterprise ledger corresponds to the business process data one by one.

[0020] As a preferred implementation mode of the present invention, external business data generates and constructs ledger data; when the ledger data is generated, the corresponding financial process receives the ledger data at the corresponding time, and in the subsequent operation stage, the external business data changes, and the financial process performs real-time data flow adjustments based on the current ledger data. After the adjustment is completed, the process data of the data flow adjustment caused by the change of external business data is hidden; the changed external business data is used as the starting data of the ledger data, and a ledger data chain is formed thereby, and the real-time data flow adjustment is marked as a ledger reconciliation chain.

[0021] As a preferred implementation mode of the present invention, based on real-time external business data, the supply chain process executes data and the generated data enters the ledger data chain together, and when the data generated by the supply chain process changes, the supply chain process adjusts the data execution according to the data change; the generated data after the execution adjustment is completed is included in the ledger data chain, and the original ledger data chain that has undergone data execution adjustment is included in the ledger reconciliation chain.

[0022] As a preferred implementation of the present invention, the financial process stores the ledger data chain and the ledger reconciliation chain, and during ledger management, the data at the corresponding beginning and end of the ledger data chain are checked for consistency, and during the verification, the different parts of the ledger reconciliation chain and the ledger data chain are compared front to back. If the two data chains are consistent, there is no deviation in the ledger data; otherwise, the ledger data deviation is monitored for traceability.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. The present invention supports the full life cycle management of data, from data collection, governance, processing to analysis and storage, forming a complete set of intelligent data processing systems to ensure the accuracy, timeliness and availability of data. The customized data table generation function in the template market simplifies the data sorting process and provides a convenient tool for enterprise data analysis and report generation; through unified standards and governance, it ensures the consistency and effectiveness of data in the entire production process; regards data as core production materials and runs through the entire process from business production to organizational management; through comprehensive management, integration and analysis of data, it ensures the consistency and accuracy of data in various business links.

[0025] 2. In the present invention, at the organizational level, Xiaoshutong organically integrates organizations, departments, personnel, data and business processes through a spatialized data governance model, making the data governance process more transparent and efficient; it supports users to intuitively view key business information through tables, forms, etc., and also improves the intelligent processing and analysis capabilities of business data through integrated large models; on this basis, the system can quickly generate business tables and reports that meet the needs, and decision makers can use the logical relationship between tables, forms and previous and subsequent processes to view business status and market trends in real time; it helps users to collaborate efficiently between various positions; the system integrates a real-time data analysis module, and users can set data trigger conditions according to business needs, so that business processes can be adaptive and provide timely feedback, thereby improving business response speed and decision-making accuracy.

[0026] 3. In the present invention, by aggregating multi-position and multi-scenario tables, the account management in the business process is deeply combined with data management to form a computable and predictable business ledger, realizing comprehensive digitization from financial reconciliation to business process management; various business modules such as finance, supply chain, and sales are connected through platform automated data, eliminating manual operations and human errors, and improving overall operational efficiency; the traditional manual accounting model is converted into intelligent real-time data management, which significantly improves the company's financial transparency, compliance, and operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0028] Figure 1 A flow chart of the overall method of the present invention;

[0029] Figure 2 is a flow chart of the method of the data layer of the present invention;

[0030] Figure 3 This is a method flow chart of the business reconciliation layer of the present invention;

[0031] Figure 4 This is a method flow chart of the human-computer interaction layer of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0034] See also Figure 1 As shown, the intelligent data processing and collaboration architecture of the data table and account space integration includes a data layer, a business reconciliation layer, a human-computer interaction layer and an intelligent ecological layer. The input end of the data layer is electrically connected to the underlying data collection end, such as a sensor, a processor, etc. The output end of the data layer is electrically connected to the input end of the business reconciliation layer, the output end of the business reconciliation layer is electrically connected to the input end of the human-computer interaction layer, the output end of the human-computer interaction layer is electrically connected to the input end of the intelligent ecological layer, and the output end of the intelligent ecology is electrically connected to the real-time user terminal;

[0035] See also Figure 2 As shown, the data layer is data-centric, promoting online organization and business process optimization, determining all business processes and connecting them in sequence according to the upstream and downstream relationships of the business processes, transmitting the data generated between the various business processes to build data streams, and the data layer manages the data streams throughout their life cycle, including collection, governance, analysis, storage and sharing, integrating various types of business data, and ensuring the consistency and effectiveness of data throughout the entire production process through unified standards and governance; treating data as core means of production throughout the entire process from business production to organizational management; ensuring the consistency and accuracy of data in each business link through comprehensive management, integration and analysis of data;

[0036] The data flow between business processes increases as the business processes progress, and the real-time data flow is collected and the collected data is governed. The corresponding input error data of the adjacent business processes are collected and removed from the data flow. The error data is judged by the data deviation of the adjacent input times. If the data cleared and re-entered after the current input deviates from the historical input data, it is inferred that the data input is wrong.

[0037] The data flow is analyzed in real time, and the influencing trend data and common parameter data of the current adjacent business process are inferred based on the data flow of the adjacent business process. Specifically, the influencing trend data in the data flow reflects the performance data of the adjacent business process. For example, if the adjacent business process is the business end and the inventory end, a high order volume received by the business end reflects that the business end process is running well, and a higher real-time delivery timeliness rate of the inventory end reflects that the inventory end process is running well. On the contrary, the lower or smaller the value, the corresponding business process is running poorly. Common parameter data refers to common parameter data after the data flow is generated, such as order placement time, inventory shipment time, etc.

[0038] And store the real-time data stream and build timestamps;

[0039] In the stage of increasing the data flow storage volume, analyze the front-end ownership of the corresponding business process when each type of data in the corresponding business process data flow is generated, specifically the data collection method of the business process corresponding to the data flow. If the business process front-end directly obtains it, the front-end has direct control, such as the order volume of the business department can be directly obtained; conversely, if the business process front-end needs to increase the data collection process, the front-end is used for indirect control, such as the customer authorization information of the business department; and the data in the data flow is marked as the data collectibility information based on the revenue generated by the enterprise where the business process is located and the corresponding control rights; and it is stored together with the data flow data storage;

[0040] See also Figure 3 As shown in the figure, the interactive data display interface provided by the human-computer interaction layer not only supports users to intuitively view key business information through tables and forms, but also improves the intelligent processing and analysis capabilities of business data through integrated large models; on this basis, the system can quickly generate business tables and reports that meet the needs, and decision makers can use the logical relationship between tables, forms and previous and subsequent processes to view business status and market trends in real time; helping users to collaborate efficiently between various positions;

[0041] Through the powerful capabilities of the big model, deeper intelligent query and retrieval functions can be achieved; users can input query conditions through natural language, and the system will quickly give relevant answers and suggestions based on established tables and business logic to complete the needs of their job roles, including data collection, processing, and generation; this process greatly simplifies data processing and decision support, and improves the efficiency and accuracy of information acquisition.

[0042] At the same time, the application of big models combined with the template market can not only realize the rapid customization of business forms, but also automatically adjust the content and format of the forms according to real-time data and business scenarios to ensure the timeliness and accuracy of information;

[0043] Analyze the storage data streams of all business processes, and infer whether the current type of data in the data stream is collectible based on the collectibility information of the data stream storage data. Use collectibility as the basis for table classification, and distinguish the importance of the storage data types corresponding to the business processes. That is, if the storage data type has numerical fluctuations, the corresponding business process needs to be adjusted, and if there are fluctuations in other types of storage data, the corresponding type of storage data will be set as a key type. For example, if the production speed of real-time order products at the front end of the business process is reduced, the order quantity and delivery time will fluctuate; otherwise, the corresponding type of storage will be set as a non-key type.

[0044] When the key types of the business process do not float, a table is constructed in sequence according to the appearance time of the data type corresponding to the data flow where the business process is located, and the floating amount of the key type data corresponding to multiple historical moments is marked. When the key types of the business process float, the table is constructed in the order of the appearance time of the data type, and the floating is marked. At the same time, the floating time of the key type data is used as the time point of the non-key type or key type affected by the floating, and the table is constructed based on the time point and the floating is marked. The various types of data in the data flow are filled according to the table template and the actual scenario, and decisions are made based on the floating mark;

[0045] See also Figure 4 As shown in the figure, the business reconciliation layer deeply combines account management and data management in the business process by aggregating tables from multiple positions and scenarios, forming a calculable and predictable business ledger, and realizing comprehensive digitalization from financial reconciliation to business process management; various business modules such as finance, supply chain, and sales are connected through platform automated data, eliminating manual operations and manual errors, and improving overall operational efficiency; the traditional manual accounting model is converted into intelligent real-time data management, which significantly improves the company's financial transparency, compliance, and operational efficiency;

[0046] Perform digital reconciliation management based on the data flow of each business process, with financial process, supply chain process and sales process as the multi-scenario convergence chain. If the business ledger management in the operation of the enterprise involves other departments, they can be substituted into the convergence chain;

[0047] The sales process is the front end, collecting external business data, where the external business data is represented by external business orders and other related parameters. The supply chain process is the middle end, executing the external business data, and there is additional business data during the data execution process, which is represented by the loss volume, freight insurance and other related parameters. The financial process is the terminal, integrating the enterprise ledger and ensuring that the enterprise ledger corresponds to the business process data one by one.

[0048] External business data generates and constructs ledger data, where the ledger data covers all aspects of the business, including but not limited to transaction information, customer information, project progress, and material in and out of the warehouse; when the ledger data is generated, the corresponding financial process receives the ledger data at the corresponding time, and in the subsequent operation stage, the external business data changes, such as order cancellation, order addition, customer change, etc. The financial process makes real-time data flow adjustments based on the current ledger data. After the adjustment is completed, the process data of the data flow adjustment caused by the change of external business data is hidden, specifically process data such as order increase, modified customer information, etc.; the changed external business data is used as the starting data of the ledger data, and a ledger data chain is formed, and the real-time data flow adjustment is marked as a ledger reconciliation chain;

[0049] According to real-time external business data, the supply chain process performs data execution and the generated data is entered into the ledger data chain. If the data generated by the supply chain process changes, such as customer returns, transportation delays, transportation damage, etc., the supply chain process will make data execution adjustments based on the data changes, such as replenishment, cost increase, progress delay, etc.; the generated data after the execution adjustment is completed will be included in the ledger data chain, and the original ledger data chain that has undergone data execution adjustment will be included in the ledger reconciliation chain;

[0050] The financial process stores the ledger data chain and the ledger reconciliation chain, and during ledger management, the data at the beginning and end of the ledger data chain are checked for consistency. During the verification, the different parts of the ledger reconciliation chain and the ledger data chain are compared. If the two data chains are consistent, there is no deviation in the ledger data; otherwise, the deviation in the ledger data is monitored for source tracing;

[0051] The intelligent ecological layer regards space as a virtualized organization and cooperation platform for organization and cooperation, defines flexible enterprise, industry, city and personal trusted data space, and promotes cooperation and resource sharing across departments and enterprises. Based on the position account set architecture within or across organizations, different departments have access rights and role attributes to ensure the security and credibility of the data space. Through this data collaboration space, cross-departmental and cross-enterprise data sharing and resource exchange are more efficient, improving the collaboration quality of the entire cooperation ecosystem.

[0052] Build a data sharing platform and fill the data in the platform according to the real-time data flow of the business process. Set permissions based on the departments to which each business process belongs and access the data sharing platform based on the set permissions.

[0053] When the present invention is in use, the data layer constructs data streams and manages the data streams throughout their life cycle. The data streams between business processes increase data as the business processes proceed, and data collection and data governance are performed. After completion, the impact trend data and common parameter data of the current adjacent business processes are inferred through analysis, and the real-time data streams are stored and timestamps are constructed; the human-computer interaction layer analyzes the stored data streams of all business processes, and divides the tables into categories based on whether they are collectable, and at the same time distinguishes the importance to obtain key types and non-key types, and constructs tables and records data fluctuations in different scenarios, and makes decisions based on floating marks;

[0054] The business reconciliation layer constructs ledger data, forms a ledger data chain based on the ledger data, and makes adjustments and synchronizations based on data fluctuations. The adjustment records are retained to build the ledger reconciliation chain, and the financial process stores and monitors the ledger data chain and the ledger reconciliation chain. The intelligent ecological layer constructs a data sharing platform, fills the data in the platform according to the real-time data flow of the business process, and sets permissions based on the departments to which each business process belongs, and accesses the data sharing platform based on the set permissions.

[0055] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent data processing and collaboration architecture that integrates data, tables, and accounts, characterized by: It includes a data layer, a business reconciliation layer, a human-computer interaction layer and an intelligent ecological layer; the input end of the data layer is electrically connected to the underlying data collection end, the output end of the data layer is electrically connected to the input end of the business reconciliation layer, the output end of the business reconciliation layer is electrically connected to the input end of the human-computer interaction layer, the output end of the human-computer interaction layer is electrically connected to the input end of the intelligent ecological layer, and the output end of the intelligent ecology is electrically connected to the real-time user terminal; The data layer constructs data streams and manages the data streams throughout their life cycle. As business processes progress, data increases in the data streams between business processes, and data collection and data governance are performed. After completion, the impact trend data and common parameter data of the current adjacent business processes are inferred through analysis, and the real-time data streams are stored and timestamped. The human-computer interaction layer analyzes the storage data flow of all business processes, classifies tables based on whether they are collectible, and distinguishes between key and non-key types based on importance. It also constructs tables and records data fluctuations in different scenarios, and makes decisions based on floating marks. The business reconciliation layer constructs ledger data and forms a ledger data chain based on the ledger data. It also adjusts and synchronizes according to data fluctuations, retains adjustment records to build a ledger reconciliation chain, and the financial process stores and monitors the ledger data chain and the ledger reconciliation chain. The intelligent ecological layer builds a data sharing platform and fills the data in the platform according to the real-time data flow of the business process. It also sets permissions based on the departments to which each business process belongs and accesses the data sharing platform based on the set permissions.

2. The intelligent data processing and collaboration architecture integrating data, table, account and space according to claim 1 is characterized in that: The data layer process is as follows: Data is collected through real-time data streams and governed. Error data corresponding to adjacent business processes are collected and removed from the data stream. The error data is judged based on the deviation of adjacent input data. If the data cleared and re-entered after the current input deviates from the historical input data, it is inferred that the data input is incorrect.

3. The intelligent data processing and collaboration architecture integrating data, table, account and space according to claim 2 is characterized in that: When the data flow storage volume increases, analyze the generation of various types of data in the corresponding business process data flow, and the front-end ownership of the corresponding business process. If the business process front-end directly obtains the data, the front-end has direct control. Conversely, if the business process front-end needs to add a data collection process, the front-end is used for indirect control, such as customer authorization information of the business department; And according to the revenue generated by the data in the data flow and the corresponding control rights for the enterprise where the business process is located, the data is marked as the collectibility information of the data; and it is stored together with the data flow data storage.

4. The intelligent data processing and collaboration architecture integrating data, table, account and space according to claim 1 is characterized in that: The human-computer interaction layer process is as follows: According to the collectability information of the data stored in the data flow, the table category is divided according to whether it is collectable or not. At the same time, the importance of the storage data type corresponding to the business process is distinguished. That is, if the storage data type has numerical fluctuations, the corresponding business process needs to be adjusted, and if there are fluctuations in other types of storage data, the corresponding type of storage data will be set as the key type.

5. The intelligent data processing and collaboration architecture integrating data, table, account and space according to claim 4 is characterized in that: When there is no floating of the key types of the business process, the table is constructed in sequence according to the appearance time of the data type corresponding to the data flow where the business process is located, and the floating amount of the key type data corresponding to multiple historical moments is marked. When the key type of the business process floats, the table is constructed in the order of the appearance time of the data type, and the floating is marked. At the same time, the floating time of the key type data is used as the time point of the non-key type or key type affected by the floating, and the table is constructed based on the time point and the floating is marked. According to the table template and the actual scenario, fill in various types of data in the data flow, and make decisions based on the floating mark.

6. The intelligent data processing and collaboration architecture integrating data, table, account and space according to claim 1 is characterized in that: The process of the business reconciliation layer is as follows: Digital reconciliation management is carried out according to the data flow of each business process, with the financial process, supply chain process and sales process as the multi-scenario convergence chain; the sales process is used as the front end to collect external business data, and the supply chain process is used as the middle end to execute the external business data, and there is additional business data during the data execution process; the financial process is used as the terminal to integrate the enterprise ledger and ensure that the enterprise ledger corresponds to the business process data one by one.

7. The intelligent data processing and collaboration architecture integrating data, table, account and space according to claim 6 is characterized in that: External business data generates and constructs ledger data; when the ledger data is generated, the corresponding financial process receives the ledger data at the corresponding time, and in the subsequent operation stage, the external business data changes, and the financial process adjusts the data flow in real time based on the current ledger data. After the adjustment is completed, the process data of the data flow adjustment caused by the change of external business data is hidden; the changed external business data is used as the starting data of the ledger data, and a ledger data chain is formed, and the real-time data flow adjustment is marked as a ledger reconciliation chain.

8. The intelligent data processing and collaboration architecture integrating data, table, account and space according to claim 7 is characterized in that: Based on real-time external business data, the supply chain process executes data and the generated data enters the ledger data chain together. If the data generated by the supply chain process changes, the supply chain process will adjust the data execution according to the data changes; the generated data after the execution adjustment is completed will be included in the ledger data chain, and the original ledger data chain that has undergone data execution adjustment will be included in the ledger reconciliation chain.

9. The intelligent data processing and collaboration architecture integrating data, table, account and space according to claim 8 is characterized in that: The financial process stores the ledger data chain and the ledger reconciliation chain. When managing the ledger, the data at the beginning and end of the ledger data chain are checked for consistency. When checking, the different parts of the ledger reconciliation chain and the ledger data chain are compared. If the two data chains are consistent, there is no deviation in the ledger data; otherwise, the ledger data deviation is monitored for traceability.

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