Agricultural planting equipment data remote monitoring method and system

Abstract

The invention relates to the technical field of agricultural monitoring, and discloses an agricultural planting equipment data remote monitoring method and system, and the method comprises the steps: carrying out the data collection and normalization of an original data flow from agricultural planting equipment, and obtaining a standardized data queue and equipment metadata; performing rule mapping on a preset rule base and the equipment metadata to obtain a business rule, and performing key assessment on the standardized data queue to obtain a service level identifier; performing multi-dimensional routing strategy synthesis on the network state parameter of the current communication link and the service level identifier to obtain a routing decision table; performing policy routing scheduling on the standardized data queue to obtain a received data packet; performing protocol de-encapsulation verification on the received data packet to obtain effective monitoring data; carrying out transactional persistent storage to obtain a data storage event; performing event-driven rendering on the data storage event to obtain a real-time monitoring interface; according to the invention, the efficiency of remote monitoring of agricultural planting equipment data can be improved.

Description

Technical Field

[0001] This invention relates to the field of agricultural monitoring technology, and in particular to a method and system for remote monitoring of agricultural planting equipment data. Background Technology

[0002] Existing technologies lack standardized collection and organization processes for raw data streams from agricultural planting equipment. They also fail to systematically verify the validity of the data, resulting in the inclusion of invalid and redundant information in the raw data. Furthermore, the lack of targeted protocol deconstruction and paradigmatic processing makes it impossible to form a standardized data queue with a unified format. It is also difficult to accurately extract equipment metadata that reflects the basic information of the equipment, which leads to a lack of reliable data support for subsequent data-based monitoring and analysis, resulting in low data application efficiency.

[0003] Existing technologies have significant shortcomings in data transmission and processing. They fail to develop routing strategies that consider network status parameters of the communication link and the service level requirements of the data, making it easy for important monitoring data to be delayed or lost due to network congestion. Furthermore, the protocol decapsulation and verification of received data packets are not rigorous enough, which may lead to invalid data entering the storage stage. Moreover, the transactional persistent storage mechanism is imperfect, making it difficult to guarantee data security and integrity. The real-time monitoring interface rendering does not associate with data entry events, making it impossible to present the equipment operating status in a timely and accurate manner. The overall monitoring process lacks timeliness and reliability. Therefore, how to improve the efficiency of remote monitoring of agricultural planting equipment data and the generation of data reports has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method and system for remote monitoring of agricultural planting equipment data to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for remote monitoring of agricultural planting equipment data, comprising:

[0006] S1. Collect and organize the raw data stream from agricultural planting equipment to obtain a standardized data queue and equipment metadata of the raw data stream;

[0007] S2. Map the preset rule base to the device metadata to obtain the business rules of the device metadata, and perform a criticality assessment on the standardized data queue according to the business rules to obtain the service level identifier of the standardized data queue.

[0008] S3. Combine the network status parameters of the current communication link with the service level identifier to form a multi-dimensional routing strategy, and obtain the routing decision table of the standardized data queue.

[0009] S4. Based on the routing decision table, perform policy routing scheduling on the standardized data queue to obtain the received data packets of the standardized data queue;

[0010] S5. Perform protocol decapsulation and verification on the received data packet to obtain valid monitoring data of the received data packet; and perform transactional persistent storage on the valid monitoring data to obtain the data entry event of the valid monitoring data;

[0011] S6. Perform event-driven rendering on the data entry event to obtain a real-time monitoring interface for the data entry event.

[0012] In a preferred embodiment, the step of collecting and organizing the raw data stream from agricultural planting equipment to obtain a standardized data queue and equipment metadata of the raw data stream includes:

[0013] The raw data stream from agricultural planting equipment is validated to obtain a purified data stream.

[0014] The purified data stream is deconstructed according to the protocol to obtain the atomic data items of the purified data stream;

[0015] Device metadata is extracted from the atomic data items, and the atomic data items are normalized to obtain a standardized data queue of the original data stream.

[0016] In a preferred embodiment, the step of mapping a pre-set rule base to the device metadata to obtain business rules for the device metadata, and then performing a critical assessment on the standardized data queue based on the business rules to obtain a service level identifier for the standardized data queue, includes:

[0017] The device type identifier in the device metadata is compared with a pre-set rule base to obtain an initial rule set for the device metadata;

[0018] The initial rule set and the running status parameters in the device metadata are injected with rule status to obtain the business rules of the device metadata;

[0019] Based on the key data features in the business rules, feature matching is performed on the standardized data queue to obtain a key data subset of the standardized data queue;

[0020] According to the priority strategy in the business rules, priority mapping is performed on the key data subset to obtain the transmission priority of the key data subset;

[0021] Based on the transmission priority and the service quality standard in the business rules, the service level of the standardized data queue is synthesized to obtain the service level identifier of the standardized data queue.

[0022] In a preferred embodiment, the step of synthesizing the network state parameters of the current communication link with the service level identifier into a multi-dimensional routing policy to obtain the routing decision table of the standardized data queue includes:

[0023] The network status parameters of the current communication link are evaluated in real time to obtain the key network indicators of the network status parameters.

[0024] Based on the service level identifier, generate the routing performance requirements for the standardized data queue;

[0025] By combining the key network metrics with the routing performance requirements, a set of candidate routing strategies for the standardized data queue is obtained.

[0026] The candidate routing strategy set is adaptively filtered against the network load status of the current communication link to obtain the routing decision table for the standardized data queue.

[0027] In a preferred embodiment, the step of performing policy-based routing scheduling on the standardized data queue based on the routing decision table to obtain the received data packets of the standardized data queue includes:

[0028] Parse the routing decision table and extract the transmission priority index and candidate path set from the routing decision table;

[0029] Based on the transmission priority index, the standardized data queue is prioritized and scheduled to obtain a sorted queue of the standardized data queue.

[0030] Based on the candidate path set, the sorting queue is routed and orchestrated to obtain the path allocation scheme for the sorting queue;

[0031] By using the transmission path in the path allocation scheme, the sorted queue is reorganized into message routes to obtain the received data packets of the standardized data queue.

[0032] In a preferred embodiment, the step of performing protocol decapsulation verification on the received data packet to obtain valid monitoring data of the received data packet includes:

[0033] The received data packet is parsed using multiple protocols to obtain the decapsulated data body of the received data packet;

[0034] Perform data integrity verification on the decapsulated data body to obtain the verification data content of the decapsulated data body;

[0035] Extract the data elements from the verification data content to obtain the valid monitoring data of the received data packet.

[0036] In a preferred embodiment, the step of performing transactional persistent storage on the effective monitoring data to obtain the data entry event for the effective monitoring data includes:

[0037] The valid monitoring data is encapsulated into transactions to obtain a transaction operation sequence of the valid monitoring data;

[0038] Perform a transaction persistence commit on the transaction operation sequence to obtain a data persistence record of the transaction operation sequence;

[0039] The data persistence record is instantiated into an event to obtain the data entry event for the valid monitoring data.

[0040] In a preferred embodiment, the step of performing event-driven rendering on the data entry event to obtain a real-time monitoring interface for the data entry event includes:

[0041] The data entry event is deserialized to obtain the monitoring data value of the data entry event;

[0042] Visual attribute synthesis calculation is performed on the monitoring data values ​​to obtain the display attribute values ​​of the monitoring data values;

[0043] Based on the displayed attribute values, the predefined visualization components are rendered to generate a real-time monitoring interface for the data entry event.

[0044] In a preferred embodiment, the formula for calculating the display attribute value is as follows:

[0045] ;

[0046] In the formula, For the display attribute value, This is the preset display scaling factor. The monitored data value, To identify the preset baseline data value based on the device type, For the service level identifier, This is a preset level influence coefficient based on the importance of the service level identifier. A preset range of data variation is defined based on the device type identifier.

[0047] To address the above problems, the present invention also provides a remote monitoring system for agricultural planting equipment data, the system comprising:

[0048] The data acquisition and formatting module is used to collect and format the raw data stream from agricultural planting equipment to obtain a standardized data queue and equipment metadata of the raw data stream.

[0049] The rule mapping evaluation module is used to map the preset rule base with the device metadata to obtain the business rules of the device metadata, and to perform a criticality evaluation on the standardized data queue based on the business rules to obtain the service level identifier of the standardized data queue.

[0050] The routing policy synthesis module is used to synthesize the network status parameters of the current communication link with the service level identifier to obtain the routing decision table of the standardized data queue.

[0051] The policy routing scheduling module is used to perform policy routing scheduling on the standardized data queue based on the routing decision table to obtain the received data packets of the standardized data queue;

[0052] The data verification and storage module is used to perform protocol decapsulation and verification on the received data packets to obtain valid monitoring data of the received data packets; and to perform transactional persistent storage on the valid monitoring data to obtain the data entry event of the valid monitoring data.

[0053] The real-time interface rendering module is used to perform event-driven rendering of the data entry event to obtain a real-time monitoring interface for the data entry event.

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

[0055] 1. This invention performs data validity verification, protocol deconstruction, and data normalization on the raw data stream from agricultural planting equipment. It accurately extracts equipment metadata and forms a standardized data queue, ensuring the integrity and standardization of monitoring data and providing a reliable foundation for subsequent data processing. At the same time, it maps a pre-set rule base with equipment metadata, generates business rules based on equipment operating status parameters, and completes the criticality assessment and service level identification of the standardized data queue according to the business rules. This enables accurate differentiation of data priorities and provides a basis for targeted scheduling of data transmission.

[0056] 2. This invention synthesizes a multi-dimensional routing strategy by combining the network status parameters and service level identifiers of the current communication link, generates a routing decision table, and performs policy-based routing scheduling on standardized data queues to ensure the stability and timeliness of high-priority data transmission. It obtains valid monitoring data by decapsulating and verifying received data packets, generates data entry events through transactional persistent storage, and then performs event-driven rendering on these data entry events to generate a real-time monitoring interface. This achieves efficient processing of monitoring data throughout the entire process from transmission, verification, storage to visualization, effectively improving the overall efficiency of remote monitoring of agricultural planting equipment data. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating a method for remote monitoring of agricultural planting equipment data according to an embodiment of the present invention.

[0058] Figure 2 A functional module diagram of an agricultural planting equipment data remote monitoring system provided in an embodiment of the present invention;

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

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

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

[0062] Reference Figure 1 The diagram shown is a flowchart illustrating a method for remote monitoring of agricultural planting equipment data according to an embodiment of the present invention. In this embodiment, the method for remote monitoring of agricultural planting equipment data includes:

[0063] S1. Collect and organize the raw data stream from agricultural planting equipment to obtain a standardized data queue and equipment metadata of the raw data stream;

[0064] In this embodiment of the invention, the step of collecting and organizing the raw data stream from agricultural planting equipment to obtain a standardized data queue and equipment metadata of the raw data stream includes:

[0065] The raw data stream from agricultural planting equipment is validated to obtain a purified data stream.

[0066] The purified data stream is deconstructed according to the protocol to obtain the atomic data items of the purified data stream;

[0067] Device metadata is extracted from the atomic data items, and the atomic data items are normalized to obtain a standardized data queue of the original data stream.

[0068] Specifically, when validating the raw data stream from agricultural planting equipment, the core dimensions of the validation should be clearly defined first. These dimensions include data format, data range, and data integrity. The data format must conform to the preset transmission format standards of the agricultural planting equipment. For example, numerical data must be integers or decimals with specified precision, and text data must include fixed fields for equipment identification. The data range must match the parameter range when the equipment is working normally. For example, the water pressure data of irrigation equipment must be between the upper and lower limits of the equipment's rated working water pressure, and the fertilizer flow rate data of fertilization equipment must be within the adjustable flow rate range of the equipment. Data integrity must ensure that each data entry includes necessary fields such as collection time, equipment number, parameter name, and parameter value, with no missing or blank fields.

[0069] Furthermore, each piece of data in the original data stream is read one by one and compared with the preset verification dimensions. If the format of a piece of data does not meet the standard, the parameter value exceeds the normal range, or there are missing necessary fields, the data is determined to be invalid and directly removed. If all verification dimensions meet the requirements, the data is determined to be valid and retained. After all data verification is completed, the retained valid data is sorted in order of collection time to obtain the purified data stream of the original data stream.

[0070] Furthermore, when deconstructing the purification data stream according to the protocol, the agricultural equipment data transmission protocol followed by the purification data stream is first determined. This type of protocol usually includes four parts: header, data segment, check bit, and tail label. The header is used to identify the protocol type and data length, the data segment is used to store the actual monitoring parameters, the check bit is used to verify the accuracy of data transmission, and the tail label is used to indicate the end of the data frame.

[0071] Furthermore, the purification data stream is parsed step by step according to the protocol structure. First, the header information is extracted to confirm whether the protocol type matches the preset parsing rules, and the length information of the data segment is obtained. Then, the corresponding data content is extracted according to the length of the data segment. According to the parameter division rules specified in the protocol, the data segment is divided into multiple independent basic data units. Each unit contains only a single type of monitoring parameter or equipment information, such as individual soil moisture data or individual equipment motor operating current data. These independent basic data units are the atomic data items of the purification data stream.

[0072] Furthermore, when extracting device metadata from the atomic data items, the specific content of the device metadata must first be clarified, including the device's unique identifier, device model, manufacturer, installation location, commissioning time, and the planting area to which the device belongs. This information is usually hidden in specific fields of the atomic data items. For example, the first few characters of a certain atomic data item are the device's unique identifier, and the subsequent characters are the device model. This information needs to be extracted one by one according to the field position to form structured device metadata.

[0073] Furthermore, when normalizing the atomic data items, a unified normalization standard is first established, including unified data type, unified data unit, and unified data precision. Then, the data format of each atomic data item is adjusted one by one to conform to the normalization standard. Finally, all normalized atomic data items are arranged in chronological order of acquisition time to form an ordered structured data set, resulting in a standardized data queue of the original data stream.

[0074] In summary, the entire process first removes invalid information from the original data stream through multi-dimensional verification to obtain a purified data stream; then, it decomposes the purified data stream according to the transmission protocol to obtain independent atomic data items; finally, it extracts device metadata from the atomic data items and processes the atomic data items in a unified format to form a standardized data queue. Each step is carried out around the characteristics of agricultural planting equipment data to ensure that the final obtained device metadata and standardized data queue can provide standardized and reliable data support for subsequent remote monitoring.

[0075] S2. Map the preset rule base to the device metadata to obtain the business rules of the device metadata, and perform a criticality assessment on the standardized data queue according to the business rules to obtain the service level identifier of the standardized data queue.

[0076] In this embodiment of the invention, the step of mapping a pre-set rule base to the device metadata to obtain business rules for the device metadata, and then performing a critical assessment on the standardized data queue based on the business rules to obtain a service level identifier for the standardized data queue, includes:

[0077] The device type identifier in the device metadata is compared with a pre-set rule base to obtain an initial rule set for the device metadata;

[0078] The initial rule set and the running status parameters in the device metadata are injected with rule status to obtain the business rules of the device metadata;

[0079] Based on the key data features in the business rules, feature matching is performed on the standardized data queue to obtain a key data subset of the standardized data queue;

[0080] According to the priority strategy in the business rules, priority mapping is performed on the key data subset to obtain the transmission priority of the key data subset;

[0081] Based on the transmission priority and the service quality standard in the business rules, the service level of the standardized data queue is synthesized to obtain the service level identifier of the standardized data queue.

[0082] Specifically, when performing rule retrieval between the device type identifier in the device metadata and the preset rule base, the content of the preset rule base is first clarified. This rule base stores the corresponding monitoring rules according to the type of agricultural planting equipment, including the key parameter monitoring requirements, data transmission specifications and anomaly judgment criteria for different types of equipment. Each rule entry is marked with the corresponding device type identifier, which is consistent with the device type identifier format in the device metadata.

[0083] Furthermore, the device type identifier is extracted from the device metadata. This identifier is a unique combination of characters that distinguishes the device type. For example, "irrigation-01" represents a certain type of irrigation equipment, and "fertilization-02" represents a certain type of fertilization equipment. Then, the identifier is compared with the device type identifier of all rule entries in the pre-set rule base one by one. Only the rule entries with completely matching identifiers are selected. These selected rule entries are organized according to rule categories to obtain the initial rule set of the device metadata.

[0084] Furthermore, when injecting rule status into the initial rule set and the operating status parameters in the device metadata, the operating status parameters in the device metadata are first extracted. These parameters include the device's current operating mode, real-time operating voltage, cumulative running time, and the time of the most recent maintenance. These parameters directly reflect the current operating status of the device.

[0085] Furthermore, the operating status parameters are integrated one by one into the corresponding rule entries of the initial rule set. For example, the "water pressure monitoring rule" for irrigation equipment in the initial rule set only specifies the standard water pressure range. After injecting the "continuous water supply" mode parameter, the rule is refined to "under continuous water supply mode, the water pressure needs to be maintained in the middle to high range of the standard range". After injecting the "cumulative running time exceeds the preset value" parameter, the "abnormal judgment rule" is supplemented with "if the cumulative running time exceeds the preset value and the water pressure fluctuation exceeds the threshold, it is judged as a potential fault". By injecting such status to adjust the details of the initial rules, the business rules of the equipment metadata are obtained.

[0086] Furthermore, when performing feature matching on the standardized data queue based on the key data features in the business rules, the key data features are first extracted from the business rules. These features are the core data identifiers for judging the operating status of the equipment, such as "real-time water pressure value" and "water supply flow value" in the irrigation equipment business rules, and "fertilizer concentration value" and "fertilization duration value" in the fertilization equipment business rules. Each key data feature has a clearly defined corresponding data stream field name, which is consistent with the field names in the standardized data queue.

[0087] Furthermore, each piece of data in the standardized data queue is traversed, and each piece of data is checked to see if it contains the fields corresponding to the key data features in the business rules. If a piece of data contains all the key data feature fields, then the data is included in the candidate set. After all the data has been traversed, the data in the candidate set is classified and organized according to the key data feature type to obtain the key data subset of the standardized data queue.

[0088] Furthermore, when performing priority mapping on the key data subset according to the priority strategy in the business rules, the priority strategy in the business rules is first clarified. This strategy divides the priority according to the degree of impact of the data on the safe operation of the equipment. For example, "equipment fault warning related data" is the highest priority, "normal operation parameter data" is the medium priority, and "historical data backtracking data" is the low priority. The strategy also clarifies the priority level identifier corresponding to each data type.

[0089] Furthermore, the type of each data item in the key data subset is analyzed one by one to determine whether it belongs to the category of fault warning, normal operation, or historical backtracking. Then, according to the priority strategy of the business rules, each data item is labeled with a corresponding priority level identifier, such as "high" for fault warning data, "medium" for normal operation data, and "low" for historical backtracking data. The labeled key data subsets are sorted according to priority level to obtain the transmission priority of the key data subsets.

[0090] Furthermore, when synthesizing the service level of the standardized data queue based on the transmission priority and the service quality standard in the business rules, the service quality standard in the business rules is extracted first. This standard specifies the service requirements corresponding to different transmission priorities. For example, high-priority data must meet "low latency and high reliability", medium-priority data must meet "normal latency and medium reliability", and low-priority data must meet "delayable and basic reliability". Each service requirement corresponds to a clear service level identifier.

[0091] Furthermore, the standardized data queue is associated with key data subsets to determine the transmission priority of each data item in the queue. Then, the service quality standards of the business rules are used to match the corresponding service level identifiers for data with different transmission priorities. For example, high transmission priority data is matched with the identifier "High Priority Service-01", medium transmission priority data is matched with the identifier "Medium Priority Service-02", and low transmission priority data is matched with the identifier "Low Priority Service-03". Finally, the service level identifiers of all data are integrated to form a service level label covering the entire standardized data queue, thus obtaining the service level identifier of the standardized data queue.

[0092] In summary, the entire process first retrieves and matches initial rules through device type identifiers, then injects operating status parameters to form business rules that fit the actual equipment, extracts key data based on business rules and assigns transmission priorities, and finally synthesizes service level identifiers by combining service quality standards. Each step revolves around the association between device metadata and business rules, ensuring that the obtained service level identifiers can accurately guide subsequent data transmission scheduling and provide targeted level criteria for remote monitoring of agricultural planting equipment data.

[0093] S3. Combine the network status parameters of the current communication link with the service level identifier to form a multi-dimensional routing strategy, and obtain the routing decision table of the standardized data queue.

[0094] In this embodiment of the invention, the step of synthesizing the network state parameters of the current communication link with the service level identifier into a multi-dimensional routing policy to obtain the routing decision table of the standardized data queue includes:

[0095] The network status parameters of the current communication link are evaluated in real time to obtain the key network indicators of the network status parameters.

[0096] Based on the service level identifier, generate the routing performance requirements for the standardized data queue;

[0097] By combining the key network metrics with the routing performance requirements, a set of candidate routing strategies for the standardized data queue is obtained.

[0098] The candidate routing strategy set is adaptively filtered against the network load status of the current communication link to obtain the routing decision table for the standardized data queue.

[0099] Specifically, when evaluating the network status parameters of the current communication link in real time, it is first determined that the network status parameters of the current communication link include link bandwidth usage, data transmission delay, data packet loss rate, and link connection stability. These parameters are obtained by continuously collecting real-time information on data transmission in the communication link. For example, bandwidth usage is obtained by monitoring the maximum amount of data that the link can transmit per unit time; transmission delay is obtained by recording the time difference between data transmission from the agricultural planting equipment end to the monitoring end; packet loss rate is obtained by statistically analyzing the proportion of data packets that failed to be transmitted per unit time to the total number of data packets sent; and connection stability is obtained by observing whether the link experiences disconnection and reconnection within a certain period of time.

[0100] Furthermore, the collected network status parameters are analyzed to remove abnormal fluctuation data caused by transient interference, and retain stable parameter values ​​that reflect the true state of the link. From these stable parameters, core parameters that directly affect the data transmission quality of agricultural planting equipment are extracted, such as the effective bandwidth actually available for data transmission, the actual data transmission delay, the actual proportion of data packet loss, and the duration of continuous and stable link connection. These extracted core parameters are the key network indicators of the network status parameters.

[0101] Furthermore, when generating the routing performance requirements for the standardized data queues based on the service level identifiers, the specific categories of the service level identifiers are first clarified. Different categories correspond to different data transmission requirements. For example, the standardized data queues corresponding to high-priority service level identifiers are mostly important data such as equipment fault warnings and critical operating parameters. This type of data needs to be transmitted with priority and quality guaranteed. Therefore, the generated routing performance requirements are that the transmission latency must be kept within a short range, the data packet loss rate must be controlled at an extremely low level, and the link must have continuous and stable connectivity to avoid data transmission interruptions. The standardized data queues corresponding to medium-priority service level identifiers are mostly routine equipment operating parameters. The generated routing performance requirements are that the transmission latency is within a normal range, the data packet loss rate is at a moderate level, and the link connection remains basically stable. The standardized data queues corresponding to low-priority service level identifiers are mostly historical operating data and non-critical statistical data. The generated routing performance requirements are that the transmission latency can be within a longer range, the data packet loss rate can be at a higher level, and the link connection only needs to meet basic transmission requirements. These specific requirements generated based on different identifiers constitute the routing performance requirements for the standardized data queues.

[0102] Furthermore, when deriving routing strategies by comparing the key network metrics with the routing performance requirements, the key network metrics are compared one by one with the routing performance requirements corresponding to different service levels. If the effective bandwidth of a potential routing link in its key network metrics meets the bandwidth requirements of a certain service level, the transmission delay meets the delay standard of that requirement, the packet loss rate is lower than the packet loss limit specified in the requirement, and the link stability meets the stability requirements of the requirement, then "prioritize the transmission of the standardized data queue corresponding to the service level of this link" is selected as a candidate routing strategy. For the routing performance requirements of all service levels, routing strategies that meet the conditions are selected in this way, and these strategies are classified and organized according to service level to ensure that there is a corresponding candidate strategy under each service level. The resulting strategy set is the candidate routing strategy set for the standardized data queue.

[0103] Furthermore, when adaptively filtering the candidate routing strategy set with the network load status of the current communication link, the network load status of the current communication link is first collected. This status includes the current data transmission volume, the link resource occupancy ratio, and the remaining available resources of each link involved in each strategy in the candidate routing strategy set. For example, the ratio of the current data transmission volume of a link to its maximum transmission capacity is the link occupancy ratio, and the maximum transmission capacity minus the current transmission volume is the remaining available resources.

[0104] Furthermore, each policy in the candidate routing policy set is analyzed one by one. If the current load of the link corresponding to the policy is too high, that is, the link occupancy rate exceeds a high level, and the remaining available resources cannot support the transmission requirements of the corresponding service level data, which may lead to increased data transmission latency or packet loss rate, then the policy is directly eliminated. If the link load is at a moderate level, that is, the link occupancy rate is within a reasonable range, and the remaining available resources can fully support the transmission requirements of the corresponding data and maintain transmission quality, then the policy is retained. All retained policies are sorted according to service level priority and link load rationality, and organized into a structured table containing policy content, corresponding link information, applicable service level, and execution priority. This table is the routing decision table of the standardized data queue.

[0105] In summary, the entire process first obtains key network indicators by real-time collection and analysis of communication link parameters, then clarifies the corresponding data transmission performance requirements based on service level identifiers, derives candidate routing strategies that meet the conditions by comparing indicators with requirements, and finally selects feasible strategies based on network load status and compiles them into a routing decision table. Each step revolves around data transmission quality and the actual status of the link, ensuring that the obtained routing decision table can provide accurate guidance for the efficient transmission of standardized data queues and adapt to the actual needs of remote monitoring of agricultural planting equipment data.

[0106] S4. Based on the routing decision table, perform policy routing scheduling on the standardized data queue to obtain the received data packets of the standardized data queue;

[0107] In this embodiment of the invention, the step of performing policy-based routing scheduling on the standardized data queue based on the routing decision table to obtain the received data packets of the standardized data queue includes:

[0108] Parse the routing decision table and extract the transmission priority index and candidate path set from the routing decision table;

[0109] Based on the transmission priority index, the standardized data queue is prioritized and scheduled to obtain a sorted queue of the standardized data queue.

[0110] Based on the candidate path set, the sorting queue is routed and orchestrated to obtain the path allocation scheme for the sorting queue;

[0111] By using the transmission path in the path allocation scheme, the sorted queue is reorganized into message routes to obtain the received data packets of the standardized data queue.

[0112] Specifically, when parsing the routing decision table, the structured format of the routing decision table is first clarified. This table records information in rows and columns, including a service level identifier column, a transmission priority index column, a candidate path set column, a link basic parameter column, and a link real-time status column. The service level identifier column is filled with the service level corresponding to the standardized data queue, such as high priority service-01, medium priority service-02, and low priority service-03. The transmission priority index column marks the priority level corresponding to each service level, such as high priority marked "P1", medium priority marked "P2", and low priority marked "P3". The candidate path set column records all transmission paths adapted to the corresponding service level in the format of "link identifier-maximum link bandwidth-transmission delay range", such as "link A-100Mbps-within 10ms" and "link B-80Mbps-within 15ms". The link basic parameter column supplements the physical connection method of the path, such as wireless cellular and fiber optic. The link real-time status column records the current occupied bandwidth and load rate of the path.

[0113] Further, each data entry in the routing decision table is read row by row. First, the transmission priority index column is focused on, and the priority level corresponding to each entry, such as P1, P2, and P3, is extracted and bound to the service level identifier of that row to form a one-to-one correspondence list of "service level identifier - priority level". The list clearly marks the priority that a certain service level must match. This list is the transmission priority index in the routing decision table. Then, the candidate path set column is focused on, and the "link identifier - maximum bandwidth - delay range" information of all transmission paths under each entry is extracted and sorted by link identifier. At the same time, the service level identifier of that row is associated to mark the service level range that each path is adapted to. For example, link A is marked "adapted to high priority service - 01" and link B is marked "adapted to medium priority service - 02", forming a complete set containing path physical parameters and adaptation range. This set is the candidate path set in the routing decision table.

[0114] Furthermore, when prioritizing the standardized data queue according to the transmission priority index, each piece of data in the standardized data queue is first traversed, and a service level identifier is extracted from the metadata field of each piece of data. This identifier has been bound to the data in the early data collection and standardization stage. Then, the extracted service level identifier is compared with the "service level identifier - priority level" list in the transmission priority index to determine the priority level corresponding to each piece of data. For example, high priority service-01 corresponds to P1, medium priority service-02 corresponds to P2, and a priority level label is added to the header of each piece of data to facilitate subsequent sorting and identification.

[0115] Furthermore, the standardized data queue with annotations is sorted in descending order of priority. Data at priority level P1 is placed at the front of the queue, data at priority level P2 is placed after P1, and data at priority level P3 is placed at the back. If multiple data entries belong to the same priority level, the collection timestamp field of each data entry is extracted. This field records the specific time the data was collected from the agricultural planting equipment. The data entries are arranged in ascending order of collection timestamps to ensure that data of the same priority is transmitted in the order of collection, avoiding the timeliness reduction caused by the delayed sorting of data collected earlier. After sorting, an ordered data stream is formed according to the dual rules of "priority-collection time", resulting in the sorted queue of the standardized data queue.

[0116] Furthermore, when routing the sorting queue based on the candidate path set, the parameters of each transmission path in the candidate path set are first verified, and the adaptation priority range, currently occupied bandwidth and maximum bandwidth of each path are extracted. The remaining available bandwidth of each path is calculated by subtracting the occupied bandwidth from the maximum bandwidth. At the same time, it is confirmed whether the transmission delay of the path is still within the marked delay range. For example, if link A is marked "within 10ms", it is necessary to confirm that the current actual delay does not exceed this range, and the path with excessive delay is eliminated. The path with sufficient remaining available bandwidth and the delay meets the standard is retained as a valid path.

[0117] Furthermore, starting from the head of the sorting queue, data is processed in segments according to priority. First, P1 level data segments are processed: the total data volume of the segment is calculated by the number of bytes, and it is allocated to the effective path in the candidate path set that is adapted to P1 level and has the largest remaining available bandwidth. During allocation, it is ensured that the total data volume of the segment does not exceed the remaining available bandwidth of the path. If the total data volume of a P1 data segment exceeds the remaining available bandwidth of a single path, the segment is split into multiple sub-segments and allocated to multiple effective paths adapted to P1 level in descending order of remaining available bandwidth, ensuring that the total data volume of the sub-segments matches the remaining available bandwidth of the path.

[0118] Furthermore, P2 and P3 level data segments are processed in the same way. P2 data segments are allocated to valid paths adapted to P2 level, and P3 data segments are allocated to valid paths adapted to P3 level, to avoid high-priority data transmission being blocked due to different priority data occupying the same path. Each data segment, along with its allocated path identifier, remaining bandwidth usage, and transmission order, is recorded in a table. The table includes data segment number, data priority, allocated path identifier, remaining bandwidth, and transmission start order, thus obtaining the path allocation scheme for the sorted queue.

[0119] Furthermore, when reorganizing the message routing of the sorting queue through the transmission path in the path allocation scheme, the data segments in the sorting queue are first grouped according to the correspondence between "data segment number - allocation path identifier" in the path allocation scheme. Each path identifier corresponds to a group of data, forming multiple "path-data group" correspondences.

[0120] Furthermore, the data in each "path-data group" is encapsulated: a path identifier field is added to the header of each data item, which is completely consistent with the link identifier of the allocated path to ensure that the receiver can identify the path source; a data segment number field records the sequence number of the data in the original data segment to facilitate reassembly by the receiver; a data length field records the number of bytes of the data to facilitate the receiver to confirm the data integrity; and a checksum field calculates and generates fixed-length checksum information based on the data content to verify whether the data is corrupted by the receiver; a timestamp field is added to the tail of the data to record the data encapsulation time, which corresponds to the previous collection timestamp to facilitate the tracking of data time sequence. After encapsulation, an independent message adapted to the corresponding transmission path is formed.

[0121] Furthermore, the encapsulated message is sent to the receiving module at the monitoring end through the corresponding transmission path. The receiving module receives messages according to the path identifier: first, it reads the checksum field in the header of each message, recalculates the checksum information based on the message data content, and compares it with the header checksum. If the two are consistent, it is determined that the data is not corrupted and the message is retained; if they are inconsistent, it is determined that the data is corrupted, the message is discarded and a discard log is recorded.

[0122] Furthermore, for the retained valid messages, the order of each data in the original sorting queue is restored according to the data segment number in the message header and the collection timestamp at the tail: first, the data is integrated from high to low priority, then within the same priority, it is arranged from early to late according to the collection timestamp, and finally the split sub-data segments are merged to restore the complete data segments, forming a set of data packets with the same order as the original sorting queue and complete data, thus obtaining the received data packets of the standardized data queue.

[0123] In summary, the entire process refines the parsing dimensions of the routing decision table to accurately extract priority and core path information; it achieves orderly data scheduling through a dual "priority-collection time" rule; it ensures path adaptability through remaining bandwidth calculation and segmented allocation; and it completes packet reassembly through multi-field encapsulation and verification, and time-series recovery. Each step revolves around the accuracy, orderliness, and integrity of data transmission, detailing the entire process from decision table parsing to receiving data packet generation, ensuring that the final received data packet accurately matches the subsequent verification and storage needs of remote monitoring of agricultural planting equipment data.

[0124] S5. Perform protocol decapsulation and verification on the received data packet to obtain valid monitoring data of the received data packet; and perform transactional persistent storage on the valid monitoring data to obtain the data entry event of the valid monitoring data;

[0125] In this embodiment of the invention, the step of performing protocol decapsulation verification on the received data packet to obtain valid monitoring data of the received data packet includes:

[0126] The received data packet is parsed using multiple protocols to obtain the decapsulated data body of the received data packet;

[0127] Perform data integrity verification on the decapsulated data body to obtain the verification data content of the decapsulated data body;

[0128] Extract the data elements from the verification data content to obtain the valid monitoring data of the received data packet.

[0129] The process of persistently storing the valid monitoring data through transactions to obtain the data entry event for the valid monitoring data includes:

[0130] The valid monitoring data is encapsulated into transactions to obtain a transaction operation sequence of the valid monitoring data;

[0131] Perform a transaction persistence commit on the transaction operation sequence to obtain a data persistence record of the transaction operation sequence;

[0132] The data persistence record is instantiated into an event to obtain the data entry event for the valid monitoring data.

[0133] Specifically, when performing multi-layer protocol parsing on the received data packet, the multi-layer protocol structure carried by the received data packet is first identified, including the physical layer, data link layer, network layer and application layer, and each layer has a corresponding protocol format and identification field.

[0134] Furthermore, starting from the physical layer, the parsing process reads the original signal of the received data packet, filters out noise interference generated during signal transmission, extracts electrical signal data that conforms to the physical layer protocol standard, converts the electrical signal into a binary data string, and completes the physical layer parsing.

[0135] Further, the data link layer is parsed to identify the frame start identifier and frame end identifier in the binary data string, extract the frame data between the two identifiers, parse the MAC address field and frame check field in the frame data, remove the control fields at the frame header and trailer, and obtain the data link layer parsing.

[0136] Further, network layer resolution is performed, reading the IP address field and routing control field from the link layer resolution data, verifying whether the IP address matches the preset device IP list, and removing the network layer control field after a successful match to obtain the network layer resolution data.

[0137] Further, application layer parsing is performed. The application layer adopts a dedicated monitoring protocol for agricultural planting equipment. This protocol includes a device identification field, a monitoring parameter field, a data acquisition time field, and a data description field. During parsing, the device identification field is extracted to confirm the device to which it belongs. The monitoring parameter field is read to obtain raw monitoring data such as soil moisture and equipment operating voltage. The data acquisition time field is extracted to record the time when the data was generated. The header instruction field and the tail check field of the application layer protocol are removed. The core data retained after parsing each layer is integrated into a continuous data stream to obtain the decapsulated data body of the received data packet.

[0138] Furthermore, when performing data integrity verification on the decapsulated data body, the original verification code carried in the decapsulated data body is first extracted. This verification code is calculated and generated at the data sending end based on the complete data before decapsulation and is bound to the data content.

[0139] Furthermore, based on the same verification rules as the sending end, all fields of the current decapsulated data body are calculated to generate a new verification code, ensuring that the calculation process covers every byte of the decapsulated data body and does not omit any field.

[0140] Furthermore, the newly generated check code is compared with the extracted original check code. If the two are completely consistent, it is determined that no data loss or tampering occurred during the transmission and parsing of the decapsulated data body, and the complete decapsulated data body is retained. If the two are inconsistent, it is determined that the data is corrupted, the decapsulated data body is directly discarded, and a data corruption log is recorded. After the verification is completed, the decapsulated data body that is determined to be complete is used as the verification data content of the decapsulated data body.

[0141] Furthermore, when extracting data elements from the verification data content, the types of data elements required for monitoring agricultural planting equipment are first identified, including unique equipment identifiers, monitoring parameter types, monitoring parameter values, data acquisition time, and equipment working status identifiers. These elements each have independent fields corresponding to them in the verification data content.

[0142] Furthermore, each element field in the data content is located and verified one by one. The character content of the device unique identifier field is read to confirm the agricultural planting equipment to which the data belongs; the monitoring parameter type field is read to distinguish the data into specific parameter categories such as soil moisture, irrigation flow, and fertilizer concentration; the monitoring parameter value field is read to obtain the actual monitoring results of the corresponding parameters; the data acquisition time field is read to record the specific time when the data was acquired from the device; the device working status identifier field is read to confirm whether the device is currently in normal operation, standby, or warning state. All extracted elements are organized into a structured data set in the order of "device identifier-parameter type-parameter value-acquisition time-working status" to obtain the effective monitoring data of the received data packet.

[0143] Specifically, when encapsulating the effective monitoring data into transactions, the composition structure of the transaction operation sequence is first clarified, including data insertion instructions, field mapping relationships, data validity verification rules, and transaction rollback triggering conditions, to ensure that the transaction operation can fully cover the entire process of data entry.

[0144] Specifically, the elements in the effective monitoring data are matched with the fields of the database table. For example, the unique identifier of the device corresponds to the "Device ID" field in the database table, the value of the monitoring parameter corresponds to the "Parameter Value" field, and the collection time corresponds to the "Collection Timestamp" field, thus generating a field mapping relationship table.

[0145] Furthermore, data insertion instructions are written based on the field mapping table. The instructions include the database table name, the list of fields to be inserted, and the corresponding data values. At the same time, data validity verification rules are set, such as the monitoring parameter values ​​must be within the rated monitoring range of the device. If they exceed the range, a transaction rollback is triggered. Rollback trigger conditions are set, including scenarios such as database connection interruption, instruction execution timeout, and duplicate data insertion. The data insertion instructions, field mapping table, verification rules, and rollback conditions are integrated in the execution order to form an ordered set of operation instructions, resulting in the transaction operation sequence of the effective monitoring data.

[0146] Furthermore, when performing transaction persistence commit on the transaction operation sequence, a stable connection with the monitoring database is first established, and the account permissions and network stability of the database connection are verified to ensure that the connection can support the complete execution of the transaction operation.

[0147] Furthermore, data insertion instructions are sent to the database one by one according to the execution order of the transaction operation sequence, and the execution status of each instruction is recorded in real time, including "execution successful", "execution failed" and "waiting". If an instruction fails to execute, the preset rollback condition is immediately triggered to undo the modifications made to the database by the executed instructions, thus ensuring the consistency of database data.

[0148] Furthermore, if all instructions are executed successfully, the execution results returned by the database are read, including the record ID of the inserted data, the execution time, and the number of rows affected. The content, execution status, execution results, and database connection information of the transaction operation sequence are organized into a structured document. Each record in the document corresponds to the complete information of one transaction operation, thus obtaining the data persistence record of the transaction operation sequence.

[0149] Furthermore, when instantiating the data persistence record into an event, a standard structure for the data entry event is first defined, including event ID, event type, data entry identifier, entry completion time, transaction execution result, and associated database table name. Each field has a clear information source and format requirements.

[0150] Furthermore, the necessary information for the event is extracted from the persistent data record to generate a unique event ID to distinguish different events; the event type is fixed as "data entry", and the event attributes are clearly defined; the entry data identifier corresponds to the inserted data record ID in the persistent record, associating it with the specific entry data; the entry completion time is read from the transaction end timestamp in the persistent record; the transaction execution result directly uses the "execution successful" status in the persistent record; the associated database table name is extracted from the database table name in the transaction operation sequence, and this information is filled according to the event structure to form an event instance containing complete entry information, thus obtaining the data entry event of the effective monitoring data.

[0151] In summary, the entire process revolves around parsing the received data packets and extracting valid data. First, the protocol structure from the physical layer to the application layer is parsed layer by layer to gradually strip away the control fields and obtain the decapsulated data body. Then, the data integrity is ensured by comparing the checksum. Finally, core elements such as device identification and parameter values ​​are accurately extracted to form valid monitoring data. Each step focuses on data reliability to ensure that accurate information that meets the monitoring needs of agricultural planting equipment is selected from the transmitted data, laying the foundation for subsequent data storage and application.

[0152] In summary, the entire process focuses on the transactional storage and event generation of effective monitoring data. First, the effective monitoring data is encapsulated into a sequence of transactional operations containing instructions, mapping relationships, and rollback mechanisms. Persistent commit is completed through a stable database connection, and the execution results are recorded. Finally, standardized data storage events are generated based on the persistent records. Each step aims at the security and traceability of data storage, ensuring that agricultural planting equipment monitoring data can be stored in the database in a standardized and reliable manner. At the same time, it forms event evidence that can be used for subsequent interface rendering, adapting to the full-process requirements of remote monitoring.

[0153] S6. Perform event-driven rendering on the data entry event to obtain a real-time monitoring interface for the data entry event.

[0154] In this embodiment of the invention, the step of performing event-driven rendering on the data entry event to obtain a real-time monitoring interface for the data entry event includes:

[0155] The data entry event is deserialized to obtain the monitoring data value of the data entry event;

[0156] Visual attribute synthesis calculation is performed on the monitoring data values ​​to obtain the display attribute values ​​of the monitoring data values;

[0157] Based on the displayed attribute values, the predefined visualization components are rendered to generate a real-time monitoring interface for the data entry event.

[0158] The formula for calculating the display attribute value is as follows:

[0159] ;

[0160] In the formula, For the display attribute value, This is the preset display scaling factor. The monitored data value, To identify the preset baseline data value based on the device type, For the service level identifier, This is a preset level influence coefficient based on the importance of the service level identifier. A preset range of data variation is defined based on the device type identifier.

[0161] Specifically, when performing event data deserialization on the data entry event, the storage format of the data entry event is first determined. After the event is generated, it is saved in the form of a serialized string. The string contains the event ID, the data entry identifier, the set of monitoring data elements, and the event generation time. The set of monitoring data elements covers the unique identifier of the device, the type of monitoring parameter, the value of the monitoring parameter, the collection time, and the working status of the device. Each field is distinguished by a structured tag to ensure that the field boundaries are clear and identifiable.

[0162] Furthermore, the serialized string of the data entry event is read, and the tag area corresponding to the "monitoring data element set" is located according to the preset format rules. All content in the area is extracted, and the fields are split in a fixed order of "device unique identifier - monitoring parameter type - monitoring parameter value - acquisition time - device working status". The specific content of each element is obtained from the corresponding tag. For example, the device unique identifier is obtained from the "device unique identifier" tag, and the monitoring parameter value is obtained from the "monitoring parameter value" tag. The split element content is organized into directly readable structured data. This data contains the core values ​​and related information required for monitoring, and the monitoring data value of the data entry event is obtained.

[0163] Furthermore, when performing visualization attribute synthesis calculations on the monitoring data values, the sources of each key information involved in the calculation are first clarified. The display scaling factor is a fixed value configured during system initialization, used to adjust the scaling ratio of the display attribute values ​​as a whole, and is unrelated to specific monitoring data or device types. The baseline data value is preset based on the device type identifier associated with the monitoring data value. Different device type identifiers correspond to different baseline data values, such as the soil moisture baseline value for a certain type of irrigation equipment and the fertilizer concentration baseline value for a certain type of fertilization equipment, which are set according to the equipment design standards and normal operation requirements. The data variation range is preset based on the device type identifier, reflecting the numerical fluctuation range that may occur when the corresponding equipment monitoring parameters are operating normally. The variation range is different for different device type identifiers.

[0164] Furthermore, the service level identifier is obtained by synthesizing service levels from the standardized data queue in the early stage, including high-quality service identifier, medium-quality service identifier, and low-quality service identifier, which can reflect the importance of the data. The level influence coefficient is preset according to the importance of the service level identifier. The more important the service level identifier, the larger the corresponding level influence coefficient. For example, the level influence coefficient corresponding to the high-quality service identifier is greater than that of the medium-quality service identifier, and the level influence coefficient corresponding to the medium-quality service identifier is greater than that of the low-quality service identifier, ensuring that the importance of service level is reflected through the coefficient.

[0165] Furthermore, by combining the aforementioned key information, a visualization attribute synthesis calculation is performed. First, the difference between the monitored data value and the benchmark data value is calculated. This difference reflects the degree to which the monitored data deviates from the normal benchmark. Then, this difference is divided by the data variation range to obtain the deviation ratio of the monitored data value relative to the benchmark value. The deviation ratio eliminates the impact of differences in data ranges between different devices. At the same time, the service level influence factor is calculated, which is 1 plus the product of the level influence coefficient and the service level identifier. The service level influence factor can be quantified. The quantified object is the impact of the importance of the service level on the displayed attributes. The higher the importance, the larger the influence factor.

[0166] Furthermore, the deviation ratio is multiplied by the service level impact factor to obtain an intermediate result that comprehensively reflects the data deviation and service importance. Then, the intermediate result is multiplied by a display scaling factor to adjust the overall size of the result to fit the interface display requirements. The final result is the display attribute value, which determines the display attribute configuration of the monitoring data in the visualization interface, such as color depth, font size, dynamic effect intensity, etc., thus obtaining the display attribute value of the monitoring data value.

[0167] Furthermore, when rendering the predefined visualization components according to the display attribute values, the contents of the predefined visualization component library are first defined, including device status cards, parameter value components, trend chart components, and warning prompt components. Each component reserves a configurable interface corresponding to the dimension of the display attribute value. The interface can receive configuration information such as font color, layout position, and presentation type from the display attribute values.

[0168] Furthermore, the configuration of each dimension in the display attribute value is matched with the interface of the corresponding component. The "font color - warning color" is passed to the color interface of the device status card so that the warning status data is displayed in a warning color; the "layout position - right warning area" is passed to the position interface of the warning prompt component to ensure that the warning component is concentrated in a conspicuous area; and the "data presentation type - trend chart" is passed to the type interface of the trend chart component so that the data that needs to display the changing trend is presented in the form of a chart, ensuring that each component loads the corresponding attribute configuration.

[0169] Furthermore, after the components are loaded and configured, they are arranged within the interface framework according to their layout positions. Device status cards are concentrated in the device area on the left for easy and quick device location; parameter components and chart components are distributed in the middle parameter area for easy viewing of core monitoring data; and early warning components are summarized in the early warning area on the right to highlight abnormal information. When arranging components, it is ensured that there is no overlap and the spacing is uniform to avoid visual clutter. At the same time, dynamic prompt rules are triggered, such as displaying the parameter standard range when the mouse hovers over the device and displaying the reason for the floating early warning when an early warning is issued. After all components are loaded, a complete visual interface is formed, generating a real-time monitoring interface for the data entry event.

[0170] In summary, the entire process first extracts monitoring data values ​​by parsing the serialized structure of data entry events, then clearly identifies the sources of key information required for attribute value calculation, and completes attribute value calculation by combining the degree of data deviation and the importance of service level. Each step revolves around matching data characteristics with visualization requirements, ensuring that the transformation from event data to attribute configuration accurately meets the intuitive presentation requirements of agricultural planting equipment monitoring, and provides clear and standardized attribute support for interface rendering.

[0171] In summary, the entire process focuses on the end-to-end implementation from data to interface. First, core monitoring data is obtained through event deserialization. Then, comprehensive display attribute values ​​are calculated through multi-dimensional key information. Finally, configuration loading and interface arrangement are achieved through the interface of predefined components. Each step is centered on the readability and usability of the interface. By calculating the display attribute values, data with high importance or large deviations from the baseline are presented more clearly and visually, ensuring that the operating data of agricultural planting equipment can be presented clearly and intuitively, making it convenient for maintenance personnel to quickly grasp the status of the equipment and meet the actual application needs of remote monitoring.

[0172] like Figure 2 The diagram shown is a functional block diagram of an agricultural planting equipment data remote monitoring system provided in an embodiment of the present invention.

[0173] The agricultural planting equipment data remote monitoring system 100 of the present invention can be installed in an electronic device. Depending on the functions implemented, the agricultural planting equipment data remote monitoring system 100 may include a data acquisition and organization module 101, a rule mapping and evaluation module 102, a routing strategy synthesis module 103, a strategy routing scheduling module 104, a data verification and storage module 105, and a real-time interface rendering module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0174] In this embodiment, the functions of each module / unit are as follows:

[0175] The data acquisition and shaping module 101 is used to collect and shape the raw data stream from agricultural planting equipment to obtain a standardized data queue and equipment metadata of the raw data stream.

[0176] The rule mapping evaluation module 102 is used to map the preset rule base with the device metadata to obtain the business rules of the device metadata, and to perform a criticality evaluation on the standardized data queue according to the business rules to obtain the service level identifier of the standardized data queue.

[0177] The routing policy synthesis module 103 is used to synthesize the network status parameters of the current communication link with the service level identifier in a multi-dimensional routing policy to obtain the routing decision table of the standardized data queue.

[0178] The policy routing scheduling module 104 is used to perform policy routing scheduling on the standardized data queue based on the routing decision table to obtain the received data packets of the standardized data queue.

[0179] The data verification and storage module 105 is used to perform protocol decapsulation and verification on the received data packet to obtain valid monitoring data of the received data packet; and to perform transactional persistent storage on the valid monitoring data to obtain the data entry event of the valid monitoring data.

[0180] The real-time interface rendering module 106 is used to perform event-driven rendering of the data entry event to obtain a real-time monitoring interface for the data entry event.

[0181] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0182] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0183] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0184] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0185] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An agricultural planting equipment data remote monitoring method, characterized in that, The method comprises: S1, data acquisition and regularization of the original data stream from the agricultural planting equipment, to obtain the standardized data queue and equipment metadata of the original data stream; S2, rule mapping of the preset rule base and the equipment metadata, to obtain the business rules of the equipment metadata, and key evaluation of the standardized data queue according to the business rules, to obtain the service level identifier of the standardized data queue; S3, multi-dimensional routing strategy synthesis of the network state parameters of the current communication link and the service level identifier, to obtain the routing decision table of the standardized data queue; S4, strategy routing scheduling of the standardized data queue based on the routing decision table, to obtain the received data packet of the standardized data queue; S5, protocol unpacking verification of the received data packet, to obtain the effective monitoring data of the received data packet; and transactional persistent storage of the effective monitoring data, to obtain the data warehousing event of the effective monitoring data; S6, event-driven rendering of the data warehousing event, to obtain the real-time monitoring interface of the data warehousing event.

2. The method of claim 1, wherein the agricultural planting equipment data is transmitted to the remote server through the communication network. The data acquisition and regularization of the original data stream from the agricultural planting equipment to obtain the standardized data queue and equipment metadata of the original data stream comprises: Data validity check of the original data stream from the agricultural planting equipment to obtain the purified data stream of the original data stream; Protocol deconstruction of the purified data stream to obtain the atomic data item of the purified data stream; Extracting equipment metadata from the atomic data item and data normalizing the atomic data item to obtain the standardized data queue of the original data stream.

3. The method of claim 1, wherein the agricultural planting equipment data is transmitted to the remote server through the communication network. The rule mapping of the preset rule base and the equipment metadata to obtain the business rules of the equipment metadata, and the key evaluation of the standardized data queue according to the business rules to obtain the service level identifier of the standardized data queue comprises: Rule retrieval of the device type identifier in the equipment metadata and the preset rule base to obtain the initial rule set of the equipment metadata; Rule situation injection of the initial rule set and the running state parameter in the equipment metadata to obtain the business rules of the equipment metadata; Feature matching of the standardized data queue according to the key data features in the business rules to obtain the key data subset of the standardized data queue; Priority mapping of the key data subset according to the priority strategy in the business rules to obtain the transmission priority of the key data subset; Service level synthesis of the standardized data queue based on the transmission priority and the service quality standard in the business rules to obtain the service level identifier of the standardized data queue.

4. The method for remote monitoring of agricultural planting equipment data as described in claim 1, characterized in that, The multi-dimensional routing strategy synthesis of the network state parameters of the current communication link and the service level identifier to obtain the routing decision table of the standardized data queue comprises: Real-time evaluation of the network state parameters of the current communication link to obtain the key network indicators of the network state parameters; According to the service level identifier, a routing performance requirement of the standardized data queue is generated; The key network indicators are subjected to routing strategy derivation with the routing performance requirement to obtain a candidate routing strategy set of the standardized data queue; The candidate routing strategy set is subjected to adaptive screening with a network load state of the current communication link to obtain a routing decision table of the standardized data queue.

5. The method for remote monitoring of agricultural planting equipment data as described in claim 1, characterized in that, The routing decision table is used to perform policy routing scheduling on the standardized data queue to obtain a received data packet of the standardized data queue, including: The routing decision table is parsed to extract a transmission priority indicator and a candidate path set in the routing decision table; The standardized data queue is subjected to priority scheduling according to the transmission priority indicator to obtain a sorted queue of the standardized data queue; The sorted queue is subjected to routing arrangement based on the candidate path set to obtain a path allocation scheme of the sorted queue; The sorted queue is subjected to packet routing reorganization through a transmission path in the path allocation scheme to obtain the received data packet of the standardized data queue.

6. The method of claim 1, wherein the agricultural planting equipment data is transmitted to the remote server through a wireless communication network. 5 The received data packet is subjected to protocol unpacking verification to obtain valid monitoring data of the received data packet, including: The received data packet is subjected to multi-layer protocol analysis to obtain unpacked data of the received data packet; The unpacked data is subjected to data integrity verification to obtain verification data content of the unpacked data; Data elements of the verification data content are extracted to obtain valid monitoring data of the received data packet.

7. The method for remote monitoring of agricultural planting equipment data as described in claim 1, characterized in that, The valid monitoring data is subjected to transactional persistent storage to obtain a data warehousing event of the valid monitoring data, including: The valid monitoring data is subjected to transactional packaging to obtain a transaction operation sequence of the valid monitoring data; The transaction operation sequence is subjected to transactional persistent storage submission to obtain a data persistent storage record of the transaction operation sequence; The data persistent storage record is subjected to event instantiation to obtain the data warehousing event of the valid monitoring data.

8. The method for remote monitoring of agricultural planting equipment data as described in claim 1, characterized in that, The data warehousing event is subjected to event-driven rendering to obtain a real-time monitoring interface of the data warehousing event, including: The data warehousing event is subjected to event data deserialization to obtain monitoring data values of the data warehousing event; The monitoring data values are subjected to visual property synthesis calculation to obtain display attribute values of the monitoring data values; According to the display attribute values, a pre-defined visual component is subjected to attribute rendering to generate the real-time monitoring interface of the data warehousing event.

9. A method for remote monitoring of agricultural planting equipment data as described in claim 8, characterized in that, The calculation formula of the display attribute values is as follows: ； In the formula, is the display attribute value, is a preset display scaling coefficient, is the monitoring data value, is a preset reference data value based on the device type identifier, is the service level identifier, is a level influence coefficient preset according to the importance degree of the service level identifier, is a preset data variation range based on the device type identifier.

10. An agricultural planting equipment data remote monitoring system, characterized by, The system includes: A data collection and regularization module is configured to collect and regularize raw data streams from agricultural planting equipment to obtain a standardized data queue and equipment metadata of the raw data streams; A rule mapping and evaluation module is configured to map a pre-configured rule library with the equipment metadata to obtain a business rule of the equipment metadata, and evaluate the standardized data queue based on the business rule to obtain a service level identifier of the standardized data queue; A service level identifier of the standardized data queue is obtained by performing key evaluation on the standardized data queue based on the business rule of the equipment metadata. The routing policy synthesis module is configured to perform multi-dimensional routing policy synthesis on the network state parameter of the current communication link and the service level identifier, to obtain a routing decision table of the standardized data queue; The policy routing scheduling module is configured to perform policy routing scheduling on the standardized data queue based on the routing decision table, to obtain a received data packet of the standardized data queue; The data verification storage module is configured to perform protocol unpacking verification on the received data packet, to obtain valid monitoring data of the received data packet; and perform transactional persistent storage on the valid monitoring data, to obtain a data warehousing event of the valid monitoring data; The interface real-time rendering module is configured to perform event-driven rendering on the data warehousing event, to obtain a real-time monitoring interface of the data warehousing event.

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