A tea industry chain operation monitoring system based on big data sharing exchange

Abstract

This invention relates to the field of agricultural big data technology, specifically disclosing a tea industry chain operation monitoring system based on big data sharing and exchange, including a sensing and acquisition module, a sharing and exchange module, an operation analysis module, and an application display module. This solution constructs a multi-source sensing system covering the upstream, midstream, and downstream of the tea industry chain, establishes a unified data format standard to achieve structural aggregation of multi-source heterogeneous data, ensures data integrity, and facilitates the traceability and horizontal comparative analysis of the entire life cycle of tea, providing precise data support for quality management, cost optimization, and process reengineering. It introduces dynamic mapping, blockchain storage, and access control to achieve standardized conversion, secure sharing, and multi-node collaborative management of tea industry chain operation data. It constructs a distributed ledger based on blockchain technology to ensure data consistency and immutability, improve the security and compliance of data access, and effectively protect the data rights of all participants in the tea industry chain.

Description

Technical Field

[0001] This invention relates to the field of agricultural big data technology, specifically to a tea industry chain operation monitoring system based on big data sharing and exchange. Background Technology

[0002] The tea industry chain involves multiple links such as planting, processing, distribution, and sales. Using big data technology to achieve data sharing and collaborative management among these links is of great significance for improving the overall operational efficiency of the tea industry chain. However, the existing tea industry chain suffers from serious data silos, poor information flow, invisible operational status, and delayed risk warnings. Traditional data sharing methods are subject to risks of data tampering, loss, or leakage, and the sharing path is difficult to trace. When the data volume increases, response delays and uneven node loads can easily occur, and there is a lack of quantitative standards in the collaborative process. Summary of the Invention

[0003] To address the aforementioned issues and overcome the shortcomings of existing technologies, this invention provides a tea industry chain operation monitoring system based on big data sharing and exchange. Addressing the problems of severe data silos, poor information flow, lack of visibility into operational status, and delayed risk warnings in the current tea industry chain, this solution constructs a multi-source sensing system covering the upstream, midstream, and downstream of the tea industry chain. It establishes a unified data format standard to achieve structural aggregation of multi-source heterogeneous data, ensuring data integrity and facilitating the traceability and horizontal comparative analysis of the entire tea lifecycle. This provides precise data support for quality management, cost optimization, and process reengineering. Furthermore, it addresses the issues of data tampering, loss, or other problems inherent in traditional data sharing methods. The risks of data leakage and the difficulty in tracing shared paths, coupled with issues such as response delays and uneven node loads as data volume increases, and the lack of quantitative standards in the collaboration process, all highlight the challenges of data leakage. This solution introduces dynamic mapping, blockchain storage, and access control to achieve standardized conversion, secure sharing, and multi-node collaborative management of tea industry chain operational data. It improves data parsability, constructs a distributed ledger based on blockchain technology to ensure data consistency and immutability, and logically shards nodes according to their roles to significantly enhance the concurrency and operational efficiency of the blockchain network. This also improves the security and compliance of data access, effectively protecting the data rights of all participants in the tea industry chain.

[0004] This invention provides a tea industry chain operation monitoring system based on big data sharing and exchange, including a sensing and acquisition module, a sharing and exchange module, an operation analysis module, and an application display module;

[0005] The sensing and acquisition module collects basic data from the upstream, midstream, and downstream links of the tea industry chain. It converts the basic data into aggregated data through a unified data format standard and preliminary aggregation, calculates statistical feature values, and sends the aggregated data and statistical feature values ​​to the sharing and exchange module.

[0006] The shared exchange module dynamically maps the aggregated data to generate a data view of the entire tea industry chain. It uses blockchain technology to achieve secure sharing of the data view and controls the access permissions of each participant in the data view.

[0007] The operation analysis module uses a time series analysis model to analyze the data view, assess whether the operation status of each link is abnormal, identify deviations and generate early warning information, and at the same time generate a trend report to send to the application display module.

[0008] The application display module presents the operational status of each link in the tea industry chain through a visual interface. Users can view monitoring parameters through interactive commands and generate interactive logs that are fed back to the operation analysis module.

[0009] Furthermore, the sensing and acquisition module includes a data acquisition unit, a data processing unit, and a data aggregation unit;

[0010] The data acquisition unit covers the upstream, midstream, and downstream of the tea industry chain: the upstream involves the tea planting stage, the midstream involves the tea processing and distribution stage, and the downstream involves the consumer service stage; basic data from each stage is collected and sent to the data processing unit.

[0011] The data processing unit defines a unified data format standard, converts basic data into structured data containing four basic fields: timestamp, geographic location, data type, and numeric field, and performs preliminary cleaning on the structured data to remove outliers and duplicate data.

[0012] The data aggregation unit aggregates structured data according to timestamps to obtain aggregated data, calculates statistical feature values, uploads the aggregated data and statistical feature values ​​to the shared exchange module, and retains a local cache copy at the same time.

[0013] Furthermore, the shared exchange module includes a dynamic mapping unit, a blockchain storage unit, and a permission control unit;

[0014] The dynamic mapping unit constructs a mapping relationship table for the aggregated data, filters the semantics of the data in each link expressed by the core unit, and generates a data view of the entire tea industry chain.

[0015] The blockchain storage unit uses blockchain technology to achieve secure storage and sharing of data views, builds a blockchain network, and allows participants in each link of the tea industry chain to access the blockchain network as nodes.

[0016] The permission control unit controls the permissions of nodes in the blockchain.

[0017] Furthermore, the dynamic mapping unit constructs a mapping relationship table for the aggregated data, including the following steps:

[0018] Step S1: Standard definition, predefine the data standards for each link in the tea industry chain;

[0019] Step S2: Unit decomposition, decompose the aggregated data at the unit level, and calculate the importance weight of each unit in the aggregate structure;

[0020] Step S3: View Generation. Each unit is compared to predefined data standards using a similarity matching algorithm. A threshold is set to filter core units that express the semantics of the overall data. The number of core units should not be less than 30% of the total number of units. A mapping table is constructed, and a data view is generated based on the mapping table. The formula used is as follows:

[0021] ;

[0022] In the formula, The similarity score represents the mapping relationship. Indicates the index of the cell. Indicates the number of units, Represents the actual value of the unit. This represents a predefined data standard. Indicates the first The importance weight of each unit Indicates the first The distance between each unit and the predefined data standard.

[0023] Furthermore, the blockchain storage unit achieves secure storage and sharing of data views through blockchain technology, including the following steps:

[0024] Step P1: Initialize the blockchain network, with each participant in the tea industry chain as a node, and each node assigned a unique identity identifier;

[0025] Step P2: Construct a distributed ledger. Data views are stored in the ledger in the form of blocks. Each node maintains a complete copy of the ledger. Each block contains a block number, timestamp, data view hash value, previous block hash, and current block hash.

[0026] Step P3: Data sharding. Logically shard the blocks according to node roles. Each shard consists of some nodes. New data is mapped to the corresponding shard according to its stage.

[0027] Step P4: Generate a shared report and periodically calculate the data contribution and access frequency of each node.

[0028] Furthermore, the application display module includes a chart display unit, an event-driven unit, a queue monitoring unit, and an interactive feedback unit;

[0029] The chart display unit periodically reads the trend report from the operation analysis module and the data view from the sharing and exchange module, and displays the operation status of each link in the upstream, midstream and downstream of the tea industry chain through visual charts;

[0030] The event-driven unit uses an event-driven mechanism to update the visualization charts in real time, stores the data change events generated in each period in a queue, and calls the data update function to modify the data in the visualization interface.

[0031] The queue monitoring unit monitors the status of the queue through a heartbeat detection mechanism, and automatically triggers a recovery mechanism when the queue is blocked or delayed.

[0032] The interactive feedback unit is equipped with interactive commands. Users can view monitoring parameters through interactive commands, and at the same time, the user's operation behavior is recorded and an interactive log is generated and fed back to the operation analysis module.

[0033] The beneficial effects achieved by the present invention using the above solution are as follows:

[0034] (1) In response to the problems of severe data silos, poor information flow, invisible operation status, and delayed risk warning in the existing tea industry chain, this solution constructs a multi-source perception system covering the upstream, midstream, and downstream of the tea industry chain, establishes a unified data format standard to realize the structural aggregation of multi-source heterogeneous data, ensures data integrity, helps to trace the entire life cycle of tea and conduct horizontal comparative analysis, and provides accurate data support for quality management, cost optimization, and process reengineering.

[0035] (2) In view of the risks of data tampering, loss or leakage in traditional data sharing methods, the difficulty in tracing the sharing path, the problem of response delay and uneven node load when the data volume increases, and the lack of quantitative standards in the collaboration process, this solution introduces dynamic mapping, blockchain storage and access control to realize the standardized conversion, secure sharing and multi-node collaborative management of tea industry chain operation data, improve the parsability of data, build a distributed ledger based on blockchain technology to ensure data consistency and immutability, and logically shard according to node roles to significantly improve the concurrency and operating efficiency of the blockchain network, improve the security and compliance of the data access process, and effectively protect the data rights of all participants in the tea industry chain. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of a tea industry chain operation monitoring system based on big data sharing and exchange proposed in this invention;

[0037] Figure 2 This is a schematic diagram of a shared switching module;

[0038] Figure 3This is a flowchart illustrating the process of constructing a mapping table for aggregated data in Example 6;

[0039] Figure 4 This is a schematic diagram illustrating the process of securely storing and sharing data views using blockchain technology in Example 7.

[0040] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0041] 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, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0042] Example 1, see Figure 1 The present invention provides a tea industry chain operation monitoring system based on big data sharing and exchange, including a sensing and acquisition module, a sharing and exchange module, an operation analysis module, and an application display module;

[0043] The sensing and acquisition module collects basic data from the upstream, midstream, and downstream links of the tea industry chain. It converts the basic data into aggregated data through a unified data format standard and preliminary aggregation, calculates statistical feature values, and sends the aggregated data and statistical feature values ​​to the sharing and exchange module.

[0044] The shared exchange module dynamically maps the aggregated data to generate a data view of the entire tea industry chain. It uses blockchain technology to achieve secure sharing of the data view and controls the access permissions of each participant in the data view.

[0045] The operation analysis module uses a time series analysis model to analyze the data view, assess whether the operation status of each link is abnormal, identify deviations and generate early warning information, and at the same time generate a trend report to send to the application display module.

[0046] The application display module presents the operational status of each link in the tea industry chain through a visual interface. Users can view monitoring parameters through interactive commands and generate interactive logs that are fed back to the operation analysis module.

[0047] Example 2, see Figure 1 This embodiment is based on the above embodiment, and the sensing and acquisition module includes a data acquisition unit, a data processing unit, and a data aggregation unit;

[0048] The data acquisition unit covers the upstream, midstream, and downstream of the tea industry chain: the upstream involves the tea planting stage, the midstream involves the tea processing and distribution stage, and the downstream involves the consumer service stage; basic data from each stage is collected and sent to the data processing unit.

[0049] The data processing unit defines a unified data format standard, converts basic data into structured data containing four basic fields: timestamp, geographic location, data type, and numeric field, and performs preliminary cleaning on the structured data to remove outliers and duplicate data.

[0050] The data aggregation unit aggregates structured data according to timestamps to obtain aggregated data, calculates statistical feature values, uploads the aggregated data and statistical feature values ​​to the shared exchange module, and retains a local cache copy at the same time.

[0051] Example 3, see Figure 1 This embodiment is based on the above embodiment. The data acquisition unit collects basic data from each stage, specifically as follows:

[0052] Environmental sensors and RFID harvesting recorders are deployed in the upstream of the tea industry chain to collect basic upstream data on tea planting, including environmental parameters, equipment operating status, tea harvesting time, harvesters, and harvesting batches. The upstream basic data is then sent to the data processing unit via a LoRa wireless communication interface.

[0053] Video surveillance equipment is used to collect key parameters in the tea processing stage, including fixation temperature, drying temperature and humidity, processing time, equipment operating status, processing batch number, and operator information; RFID tags and cold chain sensors are deployed in the tea circulation process to monitor the environmental and logistical status during tea storage and transportation; the key processing parameters and storage and transportation information are summarized into midstream basic data and sent to the data processing unit through the Internet of Things transmission protocol;

[0054] Collect consumer data from terminal sales platforms and users, including order data, sales volume, inventory changes, and customer reviews, organize it into downstream basic data, and send it to the data processing unit via API interface.

[0055] Example 4, see Figure 1This embodiment is based on the above embodiment. The data aggregation unit aggregates structured data according to timestamps. Specifically, it uses a sliding window algorithm to process time-series data in structured data and an incremental calculation algorithm to process non-time-series data in structured data to generate aggregated data. The aggregated data is grouped according to timestamps, with each group corresponding to a time window. Statistical calculations are performed on the data within each time window to generate statistical feature values, including average, variance, maximum, and minimum values. The aggregated data and statistical feature values ​​are stored in a local cache and simultaneously uploaded to the shared exchange module.

[0056] By performing the aforementioned operations, this solution addresses the problems of severe data silos, poor information flow, invisible operational status, and delayed risk warnings in the existing tea industry chain. It constructs a multi-source perception system covering the upstream, midstream, and downstream of the tea industry chain, establishes a unified data format standard to achieve structural aggregation of multi-source heterogeneous data, ensures data integrity, facilitates the traceability and horizontal comparative analysis of the entire life cycle of tea, and provides accurate data support for quality management, cost optimization, and process reengineering.

[0057] Example 5, see Figure 1 and Figure 2 This embodiment is based on the above embodiment, and the shared exchange module includes a dynamic mapping unit, a blockchain storage unit, and a permission control unit;

[0058] The dynamic mapping unit constructs a mapping relationship table for the aggregated data, filters the semantics of the data in each link expressed by the core unit, and generates a data view of the entire tea industry chain.

[0059] The blockchain storage unit uses blockchain technology to achieve secure storage and sharing of data views, builds a blockchain network, and allows participants in each link of the tea industry chain to access the blockchain network as nodes.

[0060] The permission control unit controls the permissions of nodes in the blockchain.

[0061] Example 6, see Figure 1 , Figure 2 and Figure 3 This embodiment is based on the above embodiment. The dynamic mapping unit constructs a mapping relationship table for the aggregated data, including the following steps:

[0062] Step S1: Standard definition, predefine the data standards for each link in the tea industry chain, including environmental parameters in the planting link, technological indicators in the processing link, logistics information in the distribution link, and market data in the consumption link;

[0063] Step S2: Unit decomposition, decompose the aggregated data at the unit level, and calculate the importance weight of each unit in the aggregate structure;

[0064] Step S3: View Generation. Each unit is compared to predefined data standards using a similarity matching algorithm. A threshold is set to filter core units that express the semantics of the overall data. The number of core units is set to 50% of the total number of units. A mapping table is constructed, and a data view is generated based on the mapping table. The formula used is as follows:

[0065] ;

[0066] In the formula, The similarity score represents the mapping relationship. Indicates the index of the cell. Indicates the number of units, Represents the actual value of the unit. This represents a predefined data standard. Indicates the first The importance weight of each unit Indicates the first The distance between each unit and the predefined data standard.

[0067] Example 7, see Figure 1 , Figure 2 and Figure 4 This embodiment is based on the above embodiment. The blockchain storage unit realizes secure storage and sharing of data views through blockchain technology, including the following steps:

[0068] Step P1: Initialize the blockchain network, with each participant in the tea industry chain as a node, including tea garden manager nodes, processing plant nodes, logistics and warehousing nodes, and sales terminal nodes, and assign a unique identity to each node;

[0069] Step P2: Construct a distributed ledger. Data views are stored in the ledger in the form of blocks. Each node maintains a complete copy of the ledger. Each block contains a block number, timestamp, data view hash value, previous block hash, and current block hash.

[0070] Step P3: Data sharding. Logically shard the blocks according to node roles. Each shard consists of some nodes. New data is mapped to the corresponding shard according to its stage.

[0071] Step P4: Generate a shared report and periodically calculate the data contribution and access frequency of each node.

[0072] By performing the aforementioned operations, this solution addresses the risks of data tampering, loss, or leakage inherent in traditional data sharing methods. Furthermore, traditional methods suffer from difficulties in tracing sharing paths, response delays, uneven node loads, and a lack of quantitative standards during collaboration. This solution introduces dynamic mapping, blockchain storage, and access control to achieve standardized conversion, secure sharing, and multi-node collaborative management of tea industry chain operational data. It improves data parsability, constructs a distributed ledger based on blockchain technology to ensure data consistency and immutability, and logically shards nodes according to their roles, significantly enhancing the concurrency and operational efficiency of the blockchain network. This also improves the security and compliance of data access, effectively protecting the data rights of all participants in the tea industry chain.

[0073] Example 8, see Figure 1 This embodiment is based on the above embodiment. The permission control unit controls the permissions of nodes in the blockchain. Specifically, it designs smart contracts so that each node can access the required data in the data view through the smart contract. According to the access path and target data requested by the node, it dynamically generates an access token containing user identity information, access time window and data range restrictions, and automatically writes the access record into the blockchain. It introduces zero-knowledge proof so that the node can verify the authenticity of the data without disclosing the original data. It updates the permission rules regularly and adjusts the permissions of the nodes according to user behavior patterns and business needs.

[0074] Example 9, see Figure 1 This embodiment is based on the above embodiment. The operation analysis module uses a time series analysis model to analyze the data view. Specifically, it receives the data view, accesses a pre-trained long short-term memory network to learn from the historical data in the data view, predicts the market demand and sales fluctuation trend of the downstream of the tea industry chain, generates a trend report, calculates the deviation between the trend report and the real-time data in the data view, and judges whether the operation status of each link of the tea industry chain is abnormal. If the deviation exceeds the set threshold, an early warning information is generated.

[0075] Example 10, see Figure 1 This embodiment is based on the above embodiment, and the application display module includes a chart display unit, an event-driven unit, a queue monitoring unit, and an interactive feedback unit;

[0076] The chart display unit periodically reads the trend report from the operation analysis module and the data view from the sharing and exchange module, and displays the operation status of each link in the upstream, midstream and downstream of the tea industry chain through visual charts;

[0077] The event-driven unit uses an event-driven mechanism to update the visualization charts in real time, stores the data change events generated in each period in a queue, and calls the data update function to modify the data in the visualization interface.

[0078] The queue monitoring unit monitors the status of the queue through a heartbeat detection mechanism, and automatically triggers a recovery mechanism when the queue is blocked or delayed.

[0079] The interactive feedback unit is equipped with interactive commands. Users can view monitoring parameters through interactive commands, and the user's operation behavior is recorded and an interactive log is generated and fed back to the operation analysis module.

[0080] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0081] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0082] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A tea industry chain operation monitoring system based on big data sharing and exchange, characterized in that: It includes a sensing and acquisition module, a sharing and exchange module, an operation analysis module, and an application display module; The sensing and acquisition module collects basic data from the upstream, midstream, and downstream links of the tea industry chain. It converts the basic data into aggregated data through a unified data format standard and preliminary aggregation, calculates statistical feature values, and sends the aggregated data and statistical feature values ​​to the sharing and exchange module. The shared exchange module dynamically maps the aggregated data to generate a data view of the entire tea industry chain. It uses blockchain technology to achieve secure sharing of the data view and controls the access permissions of each participant in the data view. The operation analysis module uses a time series analysis model to analyze the data view, assess whether the operation status of each link is abnormal, identify deviations and generate early warning information, and at the same time generate a trend report to send to the application display module. The application display module presents the operational status of each link in the tea industry chain through a visual interface. Users can view monitoring parameters through interactive commands and generate interactive logs that are fed back to the operation analysis module. The sensing and acquisition module includes a data acquisition unit, a data processing unit, and a data aggregation unit; The data acquisition unit covers the upstream, midstream, and downstream of the tea industry chain: the upstream involves the tea planting stage, the midstream involves the tea processing and distribution stage, and the downstream involves the consumer service stage; basic data from each stage is collected and sent to the data processing unit. The data processing unit defines a unified data format standard, converts basic data into structured data containing four basic fields: timestamp, geographic location, data type, and numeric field, and performs preliminary cleaning on the structured data to remove outliers and duplicate data. The data aggregation unit aggregates structured data according to timestamps to obtain aggregated data, calculates statistical feature values, uploads the aggregated data and statistical feature values ​​to the sharing and exchange module, and retains a local cache copy at the same time. The shared exchange module includes a dynamic mapping unit, a blockchain storage unit, and a permission control unit; The dynamic mapping unit constructs a mapping relationship table for the aggregated data, filters the semantics of the data in each link expressed by the core units, and generates a data view of the entire tea industry chain, including the following steps: Step S1: Standard definition, predefine the data standards for each link in the tea industry chain; Step S2: Unit decomposition, decompose the aggregated data at the unit level, and calculate the importance weight of each unit in the aggregate structure; Step S3: View generation. Each unit is compared with a predefined data standard using a similarity matching algorithm. A threshold is set to filter core units that express the semantics of the overall data. The number of core units is no less than 30% of the total number of units. A mapping relationship table is constructed, and a data view is generated based on the mapping relationship table. The blockchain storage unit uses blockchain technology to achieve secure storage and sharing of data views, builds a blockchain network, and allows participants in each link of the tea industry chain to access the blockchain network as nodes. The access control unit controls the permissions of nodes in the blockchain; The application display module includes a chart display unit, an event-driven unit, a queue monitoring unit, and an interactive feedback unit; The chart display unit periodically reads the trend report from the operation analysis module and the data view from the sharing and exchange module, and displays the operation status of each link in the upstream, midstream and downstream of the tea industry chain through visual charts; The event-driven unit uses an event-driven mechanism to update the visualization charts in real time, stores the data change events generated in each period in a queue, and calls the data update function to modify the data in the visualization interface. The queue monitoring unit monitors the status of the queue through a heartbeat detection mechanism, and automatically triggers a recovery mechanism when the queue is blocked or delayed. The interactive feedback unit is equipped with interactive commands. Users can view monitoring parameters through interactive commands, and at the same time, the user's operation behavior is recorded and an interactive log is generated and fed back to the operation analysis module.

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