Agricultural planting equipment data remote monitoring method and system

Abstract

The application relates to the technical field of agricultural monitoring, and discloses an agricultural planting equipment data remote monitoring method and system, the method comprising the following steps: performing data collection and regularization on original data flow from agricultural planting equipment to obtain a standardized data queue and equipment metadata; performing rule mapping on a preset rule library and the equipment metadata to obtain a business rule, performing key evaluation on the standardized data queue to obtain a service level identifier; performing multi-dimensional routing strategy synthesis on network state parameters of a current communication link and the service level identifier to obtain a routing decision table; performing strategy routing scheduling on the standardized data queue to obtain a received data packet; performing protocol unpacking verification on the received data packet to obtain effective monitoring data; performing transactional persistent storage to obtain a data warehousing event; and performing event-driven rendering on the data warehousing event to obtain a real-time monitoring interface; and the application can improve the efficiency of agricultural planting equipment data remote monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural monitoring, and in particular to an agricultural planting equipment data remote monitoring method and system. BACKGROUND

[0002] The prior art lacks a standardized collection process for the raw data stream of agricultural planting equipment, and does not perform systematic validity verification on the data, resulting in invalid and redundant information mixed in the raw data. In addition, targeted protocol disassembly and normalization processing is not carried out, which cannot form a standardized data queue in a unified format, nor can it accurately extract device metadata reflecting the basic information of the device. This makes subsequent data-based monitoring and analysis lack reliable data support and the data application efficiency is low.

[0003] The prior art has obvious defects in the data transmission and processing link. It does not develop a routing strategy in combination with the network state parameters of the communication link and the service level requirements of the data, which is prone to delay or loss of important monitoring data due to network congestion. At the same time, the protocol unpacking verification of the received data packet is not strict, which may cause invalid data to enter the storage link. In addition, the transactional persistent storage mechanism is not perfect, and the data security and integrity are difficult to guarantee. The real-time monitoring interface rendering is not associated with the data warehouse event, and the device running state cannot be presented in time and accurately. The timeliness and reliability of the overall monitoring process are insufficient. Therefore, how to improve the efficiency of agricultural planting equipment data remote monitoring and data report generation has become a problem to be solved. SUMMARY

[0004] The present application provides an agricultural planting equipment data remote monitoring method and system to solve the problems raised in the background art.

[0005] To achieve the above purpose, the present application provides an agricultural planting equipment data remote monitoring method, comprising:

[0006] S1, collecting and normalizing the raw data stream from the agricultural planting equipment to obtain a standardized data queue and device metadata of the raw data stream;

[0007] S2, mapping a pre-set rule base with the device metadata to obtain a business rule of the device metadata, and performing a criticality evaluation on the standardized data queue according to the business rule to obtain a service level identifier of the standardized data queue;

[0008] S3, synthesizing the network state parameters of the current communication link with the service level identifier to obtain a routing decision table of the standardized data queue;

[0009] S4, performing 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;

[0010] S5, performing protocol unpacking verification on the received data packet, to obtain effective monitoring data of the received data packet; and performing transactional persistent storage on the effective monitoring data, to obtain a data warehousing event of the effective monitoring data;

[0011] S6, performing event-driven rendering on the data warehousing event, to obtain a real-time monitoring interface of the data warehousing event.

[0012] In a preferred embodiment, the data collection and regularization of the original data stream from the agricultural planting equipment to obtain the standardized data queue and the device metadata of the original data stream comprises:

[0013] performing data validity verification on the original data stream from the agricultural planting equipment, to obtain a purified data stream of the original data stream;

[0014] performing protocol deconstruction on the purified data stream, to obtain an atomic data item of the purified data stream;

[0015] extracting device metadata from the atomic data item, and performing data normalization on the atomic data item, to obtain the standardized data queue of the original data stream.

[0016] In a preferred embodiment, the rule mapping of the preset rule library and the device metadata to obtain the business rule of the device metadata, and the key assessment of the standardized data queue according to the business rule to obtain the service level identifier of the standardized data queue comprises:

[0017] performing rule retrieval on the device type identifier in the device metadata and the preset rule library, to obtain an initial rule set of the device metadata;

[0018] performing rule situation injection on the initial rule set and the running state parameter in the device metadata, to obtain the business rule of the device metadata;

[0019] performing feature matching on the standardized data queue according to the key data feature in the business rule, to obtain a key data subset of the standardized data queue;

[0020] performing priority mapping on the key data subset according to the priority strategy in the business rule, to obtain a transmission priority of the key data subset;

[0021] Based on the transmission priority and the quality of service standard in the service rule, a service level synthesis is performed on the standardized data queue to obtain a service level identifier of the standardized data queue.

[0022] In a preferred embodiment, the network state parameter of the current communication link is synthesized with the service level identifier to obtain a routing decision table of the standardized data queue, including:

[0023] The network state parameter of the current communication link is evaluated in real time to obtain a key network indicator of the network state parameter.

[0024] According to the service level identifier, a routing performance requirement of the standardized data queue is generated.

[0025] The key network indicator and the routing performance requirement are deduced to obtain a candidate routing strategy set of the standardized data queue.

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

[0027] In a preferred embodiment, 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:

[0028] The routing decision table is parsed to extract a transmission priority indicator and a candidate path set in the routing decision table.

[0029] According to the transmission priority indicator, a priority scheduling is performed on the standardized data queue to obtain an ordered queue of the standardized data queue.

[0030] Based on the candidate path set, a routing arrangement is performed on the ordered queue to obtain a path allocation scheme of the ordered queue.

[0031] Through a transmission path in the path allocation scheme, a packet routing reorganization is performed on the ordered queue to obtain a received data packet of the standardized data queue.

[0032] In a preferred embodiment, the received data packet is protocol unpacking verified to obtain valid monitoring data of the received data packet, including:

[0033] The received data packet is multi-layer protocol parsed to obtain an unpacking data body of the received data packet.

[0034] The unpacking data body is data integrity verified to obtain verification data content of the unpacking data body.

[0035] extracting data elements of the verification data content, to obtain effective monitoring data of the received data packet.

[0036] In a preferred embodiment, the transactional persistent storage of the effective monitoring data, to obtain a data warehousing event of the effective monitoring data, comprises:

[0037] transactionally encapsulating the effective monitoring data, to obtain a transaction operation sequence of the effective monitoring data;

[0038] transactionally persistently committing the transaction operation sequence, to obtain a data persistent record of the transaction operation sequence;

[0039] event-instantiating the data persistent record, to obtain the data warehousing event of the effective monitoring data.

[0040] In a preferred embodiment, the event-driven rendering of the data warehousing event, to obtain a real-time monitoring interface of the data warehousing event, comprises:

[0041] event data deserializing the data warehousing event, to obtain a monitoring data value of the data warehousing event;

[0042] visual property synthesis calculation of the monitoring data value, to obtain a display property value of the monitoring data value;

[0043] According to the display property value, attribute rendering of a predefined visual component is performed, to generate the real-time monitoring interface of the data warehousing event.

[0044] In a preferred embodiment, the calculation formula of the display property value is as follows:

[0045] ;

[0046] In the formula, is the display property 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 data variation range preset based on the device type identifier.

[0047] To solve the above problems, the application further provides an agricultural planting equipment data remote monitoring system, which comprises:

[0048] The data collection and normalization module is configured to perform data collection and normalization on the raw data stream from the agricultural planting equipment to obtain a standardized data queue and equipment metadata of the raw data stream.

[0049] The rule mapping and evaluation module is configured to perform rule mapping between a preset rule library and the equipment metadata to obtain a business rule of the equipment metadata, and perform criticality evaluation on the standardized data queue according to the business rule to obtain a service level identifier of the standardized data queue.

[0050] The routing strategy synthesis module is configured to perform multi-dimensional routing strategy synthesis between a network state parameter of a current communication link and the service level identifier to obtain a routing decision table of the standardized data queue.

[0051] The policy routing and scheduling module is configured to perform policy routing and scheduling on the standardized data queue based on the routing decision table to obtain a received data packet of the standardized data queue.

[0052] The data verification and 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.

[0053] 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.

[0054] Compared with the prior art, the present application has the following advantages:

[0055] 1. The present application performs data validity verification, protocol deconstruction and data paradigm processing on the raw data stream from the agricultural planting equipment, accurately extracts equipment metadata and forms a standardized data queue, ensures the integrity and standardization of the monitoring data, and provides a reliable basis for subsequent data processing. Meanwhile, the preset rule library is mapped with the equipment metadata, the business rule is generated in combination with the equipment running state parameter, the criticality evaluation and service level identifier determination of the standardized data queue are completed according to the business rule, the data priority is accurately distinguished, and the basis for targeted scheduling of data transmission is provided.

[0056] 2.The application combines network state parameters of the current communication link with service level identifiers to synthesize multi-dimensional routing strategies, generates a routing decision table, and performs policy routing scheduling on standardized data queues to ensure the stability and timeliness of high-priority data transmission; effective monitoring data is obtained by verifying the protocol encapsulation of received data packets, and data warehouse events are formed through transactional persistent storage, and real-time monitoring interfaces are generated by event-driven rendering of data warehouse events, realizing efficient processing of the whole process from transmission, verification, storage to visualization of monitoring data, and effectively improving the overall efficiency of remote monitoring of agricultural planting equipment data. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 A flowchart of an agricultural planting equipment data remote monitoring method provided by an embodiment of the application is shown in the figure.

[0058] Figure 2 A functional module diagram of an agricultural planting equipment data remote monitoring system provided by an embodiment of the application is shown in the figure.

[0059] The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0060] It should be understood that the specific embodiments described herein are only used to explain the application and not to limit the application.

[0061] An agricultural planting equipment data remote monitoring method is provided in the embodiments of the application. The execution subject of the agricultural planting equipment data remote monitoring method includes but is not limited to at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiments of the application. In other words, the agricultural planting equipment data remote monitoring method can be executed by software or hardware installed in 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, etc. The server can be a stand-alone server, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and big data and artificial intelligence platforms, etc.

[0062] Referring to Figure 1 A flowchart of an agricultural planting equipment data remote monitoring method provided by an embodiment of the application is shown in the figure. In this embodiment, the agricultural planting equipment data remote monitoring method includes:

[0063] S1, data acquisition and regularization are performed on an original data stream from an agricultural planting device to obtain a standardized data queue and device metadata of the original data stream;

[0064] In the embodiment of the application, the data acquisition and regularization performed on the original data stream from the agricultural planting device to obtain the standardized data queue and the device metadata of the original data stream comprises:

[0065] Data validity checking is performed on the original data stream from the agricultural planting device to obtain a purified data stream of the original data stream;

[0066] Protocol deconstruction is performed on the purified data stream to obtain atomic data items of the purified data stream;

[0067] Device metadata is extracted from the atomic data items, and data normalization is performed on the atomic data items to obtain the standardized data queue of the original data stream.

[0068] Specifically, when the data validity checking is performed on the original data stream from the agricultural planting device, the core dimensions of the checking are first determined, which include data format, data range and data integrity, wherein the data format needs to conform to the preset transmission format standard of the agricultural planting device, for example, the numerical data needs to be an integer or a decimal number with a specified precision, and the text data needs to contain a fixed field of device identification; the data range needs to match the parameter interval during normal operation of the device, for example, the water pressure data of the irrigation device needs to be within the upper and lower limits of the rated working water pressure of the device, and the fertilizer flow data of the fertilization device needs to be within the adjustable flow range of the device; the data integrity needs to ensure that each data contains necessary fields such as collection time, device number, parameter name and parameter value, without missing or blank fields.

[0069] Further, each data in the original data stream is read one by one and compared with the preset checking dimensions, if the format of a certain data does not conform to the standard, the parameter value exceeds the normal range or there is a missing necessary field, it is determined that the data is invalid data and is directly excluded; if all the checking dimensions meet the requirements, it is determined that the data is valid data and is retained, and after all the data checking is completed, the retained valid data is arranged in order of collection time to obtain the purified data stream of the original data stream.

[0070] Further, when the protocol deconstruction is performed on the purified data stream, the agricultural device data transmission protocol followed by the purified data stream is first determined, which usually includes four parts of a header, a data segment, a check bit and a tail mark, the header is used to identify the protocol type and the 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 mark is used to identify the end of the data frame.

[0071] Further, the purification data stream is parsed from front to back according to the protocol structure, the table header information is extracted first, it is confirmed whether the protocol type matches the preset parsing rule, and the length information of the data segment is obtained; then the corresponding data content is extracted according to the data segment length, the data segment is split into a plurality of independent basic data units according to the parameter division rule stipulated by the protocol, each unit contains only a single type of monitoring parameter or device information, such as single soil humidity data, single device motor working current data, and these independent basic data units are the atomic data items of the purification data stream.

[0072] Further, when the device metadata is extracted from the atomic data item, the specific content of the device metadata is first determined, including device unique identification, device model, manufacturer, installation location, commissioning time and device belonging to planting area, and these information is usually hidden in the specific field of the atomic data item, such as the first several characters of a certain atomic data item being device unique identification and the subsequent characters being device model, so these information needs to be extracted one by one according to the field position to form structured device metadata.

[0073] Further, when the atomic data item is subjected to data normalization, a unified normalization standard is first formulated, including data type unification, data unit unification and data precision unification, then the data format of each atomic data item is adjusted one by one to make it conform to the normalization standard, finally all the normalized atomic data items are arranged in order according to the collection time to form an ordered structured data set, and a standardized data queue of the original data stream is obtained.

[0074] In summary, the whole process first removes invalid information in the original data stream through multi-dimensional verification to obtain a purification data stream, then the purification data stream is disassembled according to the transmission protocol to obtain independent atomic data items, and finally device metadata is extracted from the atomic data items and the atomic data items are subjected to unified format processing to form a standardized data queue, each step is carried out around the characteristics of agricultural planting device data to ensure that the device metadata and the standardized data queue obtained finally can provide standardized and reliable data support for the subsequent remote monitoring link.

[0075] S2, the preset rule base is mapped with the device metadata to obtain the business rule of the device metadata, and the key evaluation is carried out on the standardized data queue according to the business rule to obtain the service level identification of the standardized data queue;

[0076] In the embodiment of the application, the preset rule base is mapped with the device metadata to obtain the business rule of the device metadata, and the key evaluation is carried out on the standardized data queue according to the business rule to obtain the service level identification of the standardized data queue, including:

[0077] The device type identifier in the device metadata is subjected to rule retrieval with a preset rule library, to obtain an initial rule set of the device metadata;

[0078] The initial rule set is subjected to rule situation injection with a running state parameter in the device metadata, to obtain a business rule of the device metadata;

[0079] According to a key data feature in the business rule, a key data subset of the standardized data queue is obtained by feature matching;

[0080] According to a priority strategy in the business rule, a transmission priority of the key data subset is obtained by priority mapping;

[0081] Based on the transmission priority and a service quality standard in the business rule, a service level of the standardized data queue is obtained by service level synthesis.

[0082] Specifically, when the device type identifier in the device metadata is subjected to rule retrieval with a preset rule library, the content of the preset rule library is first determined. The rule library stores corresponding monitoring rules according to the classification of agricultural planting device types, including key parameter monitoring requirements, data transmission specifications and abnormality determination basis of different types of devices, and each rule item is marked with a corresponding device type identifier, which is consistent with the device type identifier format in the device metadata.

[0083] Further, the device type identifier in the device metadata is extracted, which is a unique character combination for distinguishing device types, such as "irrigation-01" representing a certain type of irrigation device and "fertilization-02" representing a certain type of fertilization device. The identifier is compared with the device type identifiers of all rule items in the preset rule library one by one, and only rule items with completely matched identifiers are selected. These selected rule items are sorted according to rule categories to obtain the initial rule set of the device metadata.

[0084] Further, when the initial rule set is subjected to rule situation injection with a running state parameter in the device metadata, the running state parameter in the device metadata is first extracted, including the current working mode of the device, the real-time working voltage, the cumulative running time and the last maintenance time. These parameters directly reflect the current running situation of the device.

[0085] Further, the running state parameters are integrated into the corresponding rule entries of the initial rule set one by one, for example, the "water pressure monitoring rule" of the irrigation equipment in the initial rule set only stipulates the standard water pressure range, after injecting the "continuous water supply" mode parameter, the rule is refined to "in the continuous water supply mode, the water pressure needs to be maintained in the high-middle interval 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 determined as a potential fault", and the details of the initial rule are adjusted by such situation injection to obtain the business rule of the device metadata.

[0086] Further, when performing feature matching on the standardized data queue according to the key data features in the business rule, the key data features are first extracted from the business rule, which are the core data identifiers for judging the running state of the equipment, such as "real-time water pressure value" and "water supply flow value" in the irrigation equipment business rule, and "fertilizer concentration value" and "fertilization duration value" in the fertilization equipment business rule, and each key data feature corresponds to a data stream field name consistent with the field name in the standardized data queue.

[0087] Further, each data in the standardized data queue is traversed to check whether it contains the fields corresponding to the key data features in the business rule, if a certain data contains all the key data feature fields, the data is included in the selected set, after all data is traversed, the data in the selected set is classified and arranged according to the type of key data feature, to obtain the key data subset of the standardized data queue.

[0088] Further, when performing priority mapping on the key data subset according to the priority strategy in the business rule, the priority strategy in the business rule is first determined, which divides the priority according to the influence degree of data on the safe operation of the equipment, such as "device fault warning related data" with the highest priority, "normal operation parameter data" with medium priority, and "historical data backtracking data" with low priority, and the strategy clearly indicates the priority level identifier corresponding to each data type.

[0089] Further, the type of each data in the key data subset is analyzed one by one to determine whether it belongs to the fault warning, normal operation or historical backtracking category, and then the priority level identifier corresponding to each data is marked according to the priority strategy of the business rule, such as marking "high" for fault warning data, "medium" for normal operation data, and "low" for historical backtracking data, and the key data subset after marking is sorted according to the priority level to obtain the transmission priority of the key data subset.

[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] Adaptively screen the candidate routing strategy set with the network load state of the current communication link to obtain a routing decision table of the standardized data queue.

[0099] Specifically, when the network state parameters of the current communication link are evaluated in real time, it is first determined that the network state 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 of data transmission in the communication link, such as obtaining the bandwidth usage by monitoring the maximum amount of data that can be transmitted per unit time, obtaining the transmission delay by recording the time difference of data sent from the agricultural planting device end to the monitoring end, obtaining the packet loss rate by counting the proportion of the number of unsuccessfully transmitted data packets per unit time to the total number of sent data packets, and obtaining the connection stability by observing whether the link is disconnected and reconnected within a period of time.

[0100] Further, the collected network state parameters are analyzed to eliminate abnormal fluctuation data caused by instantaneous interference and retain stable parameter values that can reflect the real state of the link. Core parameters that directly affect the data transmission quality of the agricultural planting device are extracted from these stable parameters, such as the effective bandwidth available for data transmission, the actual delay duration of data transmission, the actual proportion of data packet loss, and the duration of continuous stable connection of the link. These extracted core parameters are the key network indicators of the network state parameters.

[0101] Further, when generating the routing performance requirements of the standardized data queue according to the service level identifier, it is first determined that the specific category of the service level identifier corresponds to different data transmission requirements. Among them, the standardized data queue corresponding to the high-priority service identifier is mainly important data such as device fault warning and key operating parameters. This type of data needs to be transmitted first and the quality needs to be guaranteed, so the generated routing performance requirements are that the transmission delay needs to be maintained within a short range, the data packet loss rate needs to be controlled at a very low level, and the link needs to have continuous and stable connection capability to avoid data transmission interruption. The standardized data queue corresponding to the medium-priority service identifier is mainly device routine operating parameters, and the generated routing performance requirements are that the transmission delay is within a normal range, the data packet loss rate is at a medium level, and the link connection remains basically stable. The standardized data queue corresponding to the low-priority service identifier is mainly historical operating data and non-critical statistical data, and the generated routing performance requirements are that the transmission delay can be within a long range, the data packet loss rate can be at a high level, and the link connection can meet the basic transmission requirements. These specific requirements generated according to different identifiers are the routing performance requirements of the standardized data queue.

[0102] Further, when the key network indicators are compared with the routing performance requirements for routing strategy derivation, the effective bandwidth in the key network indicators of a potential routing link is compared with the bandwidth requirement of a service level requirement, the transmission delay is compared with the delay standard of the requirement, the packet loss rate is compared with the upper limit of the packet loss requirement, and the link stability is compared with the stability requirement of the requirement. If the effective bandwidth meets the bandwidth requirement, the transmission delay meets the delay standard, the packet loss rate is lower than the upper limit of the packet loss requirement, and the link stability meets the stability requirement, the routing strategy of "preferentially selecting the link to transmit the standardized data queue of the corresponding service level" is selected as a candidate routing strategy. For the routing performance requirements of all service levels, the routing strategies that meet the conditions are selected in this way, and the strategies are classified and arranged according to the service levels to ensure that there is a corresponding candidate strategy under each service level, and the strategy set formed is the candidate routing strategy set of the standardized data queue.

[0103] Further, when the candidate routing strategy set is adaptively screened according to the network load state of the current communication link, the network load state of the current communication link is first collected, including the current data transmission amount, the occupation proportion of link resources, and the remaining available resource amount of each strategy in the candidate routing strategy set. The occupation proportion of the link is the proportion of the current data transmission amount to the maximum transmission capacity of the link, and the remaining available resource amount is the maximum transmission capacity minus the current transmission amount.

[0104] Further, each strategy in the candidate routing strategy set is analyzed one by one. If the current load of the link corresponding to the strategy is too high, that is, the occupation proportion of the link exceeds a high level, and the remaining available resource amount cannot support the transmission requirement of the corresponding service level data, which may cause an increase in data transmission delay or an increase in packet loss rate, the strategy is directly excluded. If the load of the link is at a moderate level, that is, the occupation proportion of the link is within a reasonable range, and the remaining available resource amount can fully support the transmission requirement of the corresponding data and maintain the transmission quality, the strategy is retained. All retained strategies are sorted according to the service level priority and the rationality of the link load to form a structured table containing the strategy content, the corresponding link information, the applicable service level, and the execution priority. The table is the routing decision table of the standardized data queue.

[0105] In summary, the whole process first obtains the key network indicators by real-time collection and analysis of communication link parameters, then clearly identifies the corresponding data transmission performance requirement according to the service level identifier, derives the candidate routing strategies that meet the conditions through comparison of the indicators and the requirements, and finally screens out practical strategies and arranges them into a routing decision table in combination with the network load state. Each step is carried out around the data transmission quality and the actual state of the link to ensure that the routing decision table obtained can provide accurate guidance for the efficient transmission of the standardized data queue and adapt to the actual needs of the remote monitoring of agricultural planting equipment data.

[0106] S4, performing 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;

[0107] In the embodiment of the application, the performing 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 includes:

[0108] analyzing the routing decision table to extract a transmission priority index and a candidate path set in the routing decision table;

[0109] performing priority scheduling on the standardized data queue according to the transmission priority index to obtain a sorted queue of the standardized data queue;

[0110] performing routing arrangement on the sorted queue based on the candidate path set to obtain a path allocation scheme of the sorted queue;

[0111] performing packet routing reorganization on the sorted queue through a transmission path in the path allocation scheme to obtain the received data packet of the standardized data queue.

[0112] Specifically, when analyzing the routing decision table, the structured format of the routing decision table is first determined, and the table records information in units of rows and columns, including a service level identification column, a transmission priority index column, a candidate path set column, a link basic parameter column and a link real-time state column. The service level identification column fills in 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 marking “P1” for high priority, marking “P2” for medium priority and marking “P3” for low priority. The candidate path set column records all transmission paths suitable for the corresponding service level in the format of “link identification-link maximum bandwidth-transmission delay range”, such as “link A-100Mbps-10ms within” and “link B-80Mbps-15ms within”. The link basic parameter column supplements the physical connection mode of the path, such as wireless cellular and optical fiber. The link real-time state column records the current occupied bandwidth and load rate of the path.

[0113] Further, each data entry of the row-by-row read routing decision table is focused on the transmission priority indicator column, the priority level corresponding to each entry is extracted, such as P1, P2, P3, and is bound with the service level identifier of the row to form a one-to-one correspondence list of “service level identifier-priority level”, which clearly marks the priority level that must be matched for a certain service level. This list is the transmission priority indicator 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, sorted by link identifier, and associated with the service level identifier of the row to mark the service level range that each path adapts, such as link A marked “adapt high priority service-01” and link B marked “adapt 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] Further, when performing priority scheduling on the standardized data queue according to the transmission priority indicator, the service level identifier is extracted from the metadata field of each data in the standardized data queue. This identifier is bound with the data in the early data collection and regularization stage. Then, the extracted service level identifier is compared with the “service level identifier-priority level” list in the transmission priority indicator to determine the priority level corresponding to each data, such as high priority service-01 corresponding to P1 and medium priority service-02 corresponding to P2. A priority level label is added to the head of each data to facilitate subsequent sorting and identification.

[0115] Further, the sorted standardized data queue is sorted in order of priority level from high to low. The data of P1 level is arranged at the front end of the queue, the data of P2 level is arranged after P1, and the data of P3 level is arranged at the tail. If multiple data belong to the same priority level, the collection timestamp field of each data is extracted, which records the specific time when the data is collected from the agricultural planting equipment. The data is arranged in order of collection timestamp from early to late to ensure that data of the same priority is transmitted in order of collection time, avoiding the decrease of timeliness of early collected data due to lagging sorting. After sorting, an ordered data stream arranged according to the dual rules of “priority-collection time” is formed, and the sorting queue of the standardized data queue is obtained.

[0116] Further, when routing and scheduling the sorting queue based on the candidate path set, first verify the parameters of each transmission path in the candidate path set, extract the adaptation priority range, the current occupied bandwidth and the maximum bandwidth of each path, calculate the remaining available bandwidth of each path by subtracting the occupied bandwidth from the maximum bandwidth, and confirm whether the transmission delay of the path is still within the annotated delay range, such as link A annotated "within 10ms", the current actual delay must be confirmed not to exceed the range, remove the path with excessive delay, and keep the path with sufficient remaining available bandwidth and up-to-standard delay as an effective path.

[0117] Further, starting from the head of the sorting queue, process the data by priority segment, first process the P1 level data segment: count the total data amount of the data segment, calculate the data byte number, and allocate it to the effective path in the candidate path set that adapts to the P1 level and has the maximum remaining available bandwidth, and ensure that the total data amount of the data segment does not exceed the remaining available bandwidth of the path when allocating; if the total data amount of a P1 data segment exceeds the remaining available bandwidth of a single path, the data segment is split into multiple sub-data segments, which are allocated to multiple effective paths that adapt to the P1 level in order of decreasing remaining available bandwidth, ensuring that the total data amount of the sub-data segments matches the remaining available bandwidth of the path.

[0118] Further, process the P2 and P3 level data segments in the same way, allocate the P2 data segment to the effective path that adapts to the P2 level, and allocate the P3 data segment to the effective path that adapts to the P3 level, to avoid high-priority data transmission being blocked due to different priority data occupying the same path; record each data segment and the allocated path identifier, path remaining bandwidth usage, transmission order and other information in a table, which contains data segment number, data priority, allocated path identifier, path remaining bandwidth, transmission start order and other content, to obtain the path allocation scheme of the sorting queue.

[0119] Further, when reorganizing the packet routing of the sorting queue through the transmission path in the path allocation scheme, first group the data segments in the sorting queue according to the "data segment number-allocated path identifier" correspondence in the path allocation scheme, each path identifier corresponds to a group of data, forming multiple "path-data group" correspondence relationships.

[0120] Further, the data in each "path-data group" is packet encapsulated: a path identification field is added to the head of each piece of data, which is completely consistent with the link identification of the assigned path, ensuring that the receiving end identifies the path source, a data segment number field, which records the serial number of the data in the original data segment, facilitating the receiving end to recombine, a data length field, which records the byte number of the data, facilitating the receiving end to confirm data integrity and a check code field, which is generated based on the data content to calculate fixed-length check information for the receiving end to verify whether the data is damaged; a timestamp field is added to the tail of the data, which records the data encapsulation time corresponding to the previous collection timestamp, facilitating the tracing of data timing, and after encapsulation, an independent packet adapted to the corresponding transmission path is formed.

[0121] Further, the encapsulated packet is sent to the monitoring end receiving module through the corresponding transmission path, and the receiving module receives the packet according to the path identification: first, the check code field in the head of each packet is read, the check information is recalculated based on the packet data content, and compared with the head check code; if they are consistent, it is determined that the data is not damaged, and the packet is retained; if they are not consistent, it is determined that the data is damaged, and the packet is discarded and a discard log is recorded.

[0122] Further, for the retained valid packet, the order of each data in the original ordering queue is restored according to the data segment number in the head of the packet and the collection timestamp in the tail: first, the data is integrated from high to low according to the data priority, then arranged from early to late according to the collection timestamp within the same priority, and finally combined and split into complete data segments to form a data packet set consistent with the original ordering queue order and complete data, obtaining the received data packet of the standardized data queue.

[0123] In summary, the whole process refines the analysis dimension of the routing decision table, accurately extracts the priority and path core information; realizes the ordered scheduling of data through the "priority-collection time" double rules; ensures the path adaptability through the remaining bandwidth calculation and segmented allocation; completes the packet recombination through multi-field encapsulation and verification, timing recovery, each step focuses on the accuracy, orderliness and integrity of data transmission, refines the whole process operation from decision table analysis to received data packet generation, and ensures that the final received data packet can accurately match the subsequent verification and storage needs of the agricultural planting equipment data remote monitoring.

[0124] S5, protocol de-encapsulation verification is performed on the received data packet to obtain valid monitoring data of the received data packet; and the valid monitoring data is transactionally and persistently stored to obtain a data warehousing event of the valid monitoring data;

[0125] In the embodiment of the application, the protocol de-encapsulation verification on the received data packet to obtain the valid monitoring data of the received data packet comprises:

[0126] performing multi-layer protocol analysis on the received data packet to obtain unpacked data of the received data packet;

[0127] performing data integrity check on the unpacked data to obtain verification data content of the unpacked data;

[0128] extracting data elements of the verification data content to obtain effective monitoring data of the received data packet.

[0129] the transactional persistent storage of the effective monitoring data to obtain data warehousing events of the effective monitoring data, comprising:

[0130] transactional encapsulation of the effective monitoring data to obtain a transaction operation sequence of the effective monitoring data;

[0131] transactional persistent submission of the transaction operation sequence to obtain a data persistent record of the transaction operation sequence;

[0132] event instantiation of the data persistent record to obtain data warehousing events of the effective monitoring data.

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

[0134] Further, starting from the physical layer, the original signal of the received data packet is read, noise interference generated in the signal transmission process is filtered, and telecommunication signal data conforming to the physical layer protocol standard is extracted, the telecommunication signal is converted into a binary data string, and the physical layer analysis is completed.

[0135] Further, the data link layer analysis is entered, the frame start identifier and the frame end identifier in the binary data string are identified, the frame data between the two identifiers is extracted, the MAC address field and the frame check field in the frame data are analyzed, the control fields at the head and tail of the frame are removed, and the link layer analysis data is obtained.

[0136] Further, the network layer analysis is carried out, the IP address field and the routing control field in the link layer analysis data are read, the IP address is verified with the preset device IP list, the network layer control field is removed after the verification is passed, and the network layer analysis data is obtained.

[0137] Further, application layer analysis is performed, the application layer adopts an agricultural planting device special monitoring protocol, the protocol includes device identification field, monitoring parameter field, acquisition time field and data description field, when analyzing, the device identification field is extracted to confirm the belonging device, the monitoring parameter field is read to obtain the original monitoring data such as soil humidity and device working voltage, the acquisition time field is extracted to record the time when the data is generated, the head instruction field and tail check field of the application layer protocol are removed, the core data reserved after analyzing each layer is integrated into continuous data stream, and the unpacked data body of the received data packet is obtained.

[0138] Further, when performing data integrity check on the unpacked data body, the original check code carried in the unpacked data body is first extracted, the check code is generated based on the complete data before unpacking at the data sending end, and is bound with the data content.

[0139] Further, based on the same check rule as the sending end, the contents of all fields of the current unpacked data body are calculated to generate a new check code, and it is ensured that the calculation process covers each byte of the unpacked data body without missing any field.

[0140] Further, the newly generated check code is compared with the extracted original check code, if the two are completely consistent, it is determined that the unpacked data body has not occurred data loss or tampering in the transmission and analysis process, and the complete unpacked data body is retained; if the two are inconsistent, it is determined that the data is damaged, the unpacked data body is directly discarded, and a data damage log is recorded, after the check is completed, the unpacked data body that is determined to be complete is taken as the verification data content of the unpacked data body.

[0141] Further, when extracting the data elements of the verification data content, the types of data elements required by the agricultural planting device monitoring are first determined, including device unique identification, monitoring parameter type, monitoring parameter value, data acquisition time and device working state identification, and these elements have independent fields corresponding in the verification data content.

[0142] Further, each element field in the verification data content is located, the character content of the device unique identification field is read to confirm the agricultural planting device to which the data belongs, the monitoring parameter type field is read to distinguish the specific parameter categories such as soil humidity, irrigation flow and fertilizer concentration, the monitoring parameter value field is read to obtain the actual monitoring result of the corresponding parameter, the data acquisition time field is read to record the specific time when the data is collected from the device, and the device working state identification field is read to confirm that the device is currently in normal operation, standby or warning state, all the extracted elements are arranged into a structured data set in the order of “device identification-parameter type-parameter value-acquisition time-working state”, and the effective monitoring data of the received data packet is obtained.

[0143] Specifically, when transaction encapsulating the effective monitoring data, the composition structure of the transaction operation sequence is first determined, including data insertion instruction, field mapping relationship, data validity verification rule and transaction rollback trigger condition, to ensure that the transaction operation can completely cover the whole process of data warehousing.

[0144] Specifically, each element in the effective monitoring data is matched with the field of the database table, such as the device unique identifier corresponding to the "device ID" field of the database table, the monitoring parameter value corresponding to the "parameter value" field, and the collection time corresponding to the "collection timestamp" field, to generate a field mapping relationship table.

[0145] Further, based on the field mapping relationship table, data insertion instructions are written, which include the database table name, the list of fields to be inserted and the corresponding data values, and the data validity verification rules are set, such as the monitoring parameter value being within the rated monitoring range of the device, and if it exceeds the range, the transaction rollback is triggered. The rollback trigger conditions are set, including database connection interruption, instruction execution timeout, data repeated insertion and the like. The data insertion instructions, the field mapping relationship table, the verification rules and the rollback conditions are integrated in the execution order to form an ordered operation instruction set, and the transaction operation sequence of the effective monitoring data is obtained.

[0146] Further, when transaction persisting the transaction operation sequence, a stable connection with the monitoring end database is first established, and the account authority and network stability of the database connection are verified to ensure that the connection can support the complete execution of the transaction operation.

[0147] Further, according to the execution order of the transaction operation sequence, the data insertion instructions are sent to the database one by one, and the execution status of each instruction is recorded in real time, including "execution success", "execution failure" and "waiting", and if a certain instruction fails to execute, the pre-set rollback condition is triggered immediately to undo the modification of the database by the executed instructions, ensuring the consistency of the database data.

[0148] Further, 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 consumption and the affected row number. The content of the transaction operation sequence, the execution status, the execution result and the database connection information are arranged into a structured document, and each record in the document corresponds to the complete information of a transaction operation, and the data persistence record of the transaction operation sequence is obtained.

[0149] Further, when event instantiation is performed on the data persistence record, the standard structure of the data warehousing event is first defined, including event ID, event type, warehousing data identifier, warehousing completion time, transaction execution result and associated database table name, each field has a clear information source and format requirement.

[0150] Further, the information required for extracting the event from the data persistence record is extracted, a unique event ID is generated for distinguishing different events, the event type is fixed as "data warehousing" to explicitly indicate the event attribute, the warehousing data identifier is the inserted data record ID in the persistence record to associate with the specific warehousing data, the warehousing completion time is the transaction end timestamp in the persistence record, the transaction execution result directly uses the "execution success" state in the persistence record, and the associated database table name is extracted from the database table name in the transaction operation sequence.

[0151] In summary, the whole process is focused on the analysis of the received data packet and the extraction of valid data. First, the protocol structure from the physical layer to the application layer is parsed layer by layer, and the control field is gradually stripped to obtain the unpacked data body. Then, the data integrity is ensured through the comparison of the check code. Finally, the core elements such as device identifier and parameter value are accurately extracted to form valid monitoring data. Each step is centered on data reliability to ensure that the accurate information meeting the monitoring needs of agricultural planting equipment is selected from the transmission data, laying a foundation for subsequent data storage and application.

[0152] In summary, the whole process is focused on the transactional warehousing and event generation of valid monitoring data. First, the valid monitoring data is encapsulated as a transaction operation sequence containing instructions, mapping relationship and rollback mechanism. Then, the stable database connection is used to complete the persistence submission and record the execution result. Finally, the standardized data warehousing event is generated based on the persistence record. Each step aims to ensure the safety and traceability of data warehousing, so that the agricultural planting equipment monitoring data can be stored in the database in a standardized and reliable manner, and the event basis for subsequent interface rendering is formed to meet the whole process needs of remote monitoring.

[0153] S6, event-driven rendering is performed on the data warehousing event to obtain a real-time monitoring interface of the data warehousing event.

[0154] In the embodiment of the present application, the event-driven rendering of the data warehousing event to obtain the real-time monitoring interface of the data warehousing event includes:

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

[0156] The monitoring data value is subjected to visual attribute synthesis calculation to obtain the display attribute value of the monitoring data value;

[0157] According to the display attribute value, the pre-defined visual component is subjected to attribute rendering to generate the real-time monitoring interface of the data warehousing event.

[0158] The calculation formula of the display attribute value is as follows:

[0159] ;

[0160] 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 data variation range preset based on the device type identifier.

[0161] Specifically, when the data warehousing event is event data deserialized, the storage format of the data warehousing event is first determined. The event is saved in the form of a serialized string after being generated, and the string contains an event ID, a warehousing data identifier, a monitoring data element set, and an event generation time. The monitoring data element set covers a device unique identifier, a monitoring parameter type, a monitoring parameter value, a collection time, and a device working state. Each field is distinguished by a structured tag to ensure that the field boundary is clear and identifiable.

[0162] Further, the serialized string of the data warehousing event is read, the tag area corresponding to the "monitoring data element set" is located according to a preset format rule, all contents in the area are extracted, and the fields are split in a fixed order of "device unique identifier-monitoring parameter type-monitoring parameter value-collection time-device working state". The specific content of each element is obtained from under the corresponding tag, for example, the device unique identifier is obtained from under the "device unique identifier" tag, and the monitoring parameter value is obtained from under the "monitoring parameter value" tag. The element contents after splitting are arranged as structured data that can be directly read. The data contains core values and associated information required for monitoring, and the monitoring data value of the data warehousing event is obtained.

[0163] Further, when the monitoring data value is visually synthesized and calculated, the sources of each key information participating in the calculation are first determined. The display scaling coefficient is a fixed value configured during system initialization and is used to adjust the scaling ratio of the display attribute value as a whole, and is irrelevant to specific monitoring data or device type. The reference data value is preset based on the device type identifier associated in the monitoring data value, and different device type identifiers correspond to different reference data values, for example, the soil humidity reference value of a certain type of irrigation device, the fertilizer concentration reference value of a certain type of fertilization device, and the like, which are set according to the device design standard and normal operation requirements. The data variation range is preset based on the device type identifier, and reflects the numerical fluctuation interval that may occur during normal operation of the corresponding device. The variation ranges of different device type identifiers are different.

[0164] Further, the service level identifier is obtained by synthesizing the service levels of the standardized data queue in advance, and contains high-priority service identifier, medium-priority service identifier, and low-priority 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, and the more important the service level identifier is, the greater the corresponding level influence coefficient is, for example, the level influence coefficient corresponding to the high-priority service identifier is greater than that corresponding to the medium-priority service identifier, and the level influence coefficient corresponding to the medium-priority service identifier is greater than that corresponding to the low-priority service identifier, so as to ensure that the importance of the service level can be reflected by the coefficient.

[0165] Further, the difference between the monitoring data value and the reference data value is calculated first in the visual attribute synthesis calculation based on the above-mentioned key information, which can reflect the degree of deviation of the monitoring data from the normal reference; then the difference is divided by the data variation range to obtain the deviation proportion of the monitoring data value with respect to the reference value, which eliminates the influence of the range difference of 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, and the object of quantification is the influence of the importance of the service level on the display attribute, and the higher the importance is, the greater the influence factor is.

[0166] Further, the deviation proportion and the service level influence factor are multiplied to obtain an intermediate result that comprehensively reflects the data deviation and the service importance; then the intermediate result is multiplied by the display scaling coefficient to adjust the overall size of the result, so as to adapt to the interface display requirements, and finally the result obtained is the display attribute value, which determines the display attribute configuration of the monitoring data in the visual interface, such as color depth, font size, dynamic effect intensity, etc., to obtain the display attribute value of the monitoring data value.

[0167] Further, when rendering the attribute of the pre-defined visual component according to the display attribute value, the content of the pre-defined visual component library is first determined, including device state card, parameter value component, trend chart component, and early warning prompt component, each component reserving a configurable interface corresponding to the dimension of the display attribute value, which can receive configuration information such as font color, layout position, and presentation type in the display attribute value.

[0168] Further, the dimensions in the display attribute value are matched with the interfaces of the corresponding components, the "font color-alarm color" is transmitted to the color interface of the device state card, so that the early warning state data is displayed in alarm color; the "layout position-right side warning area" is transmitted to the position interface of the early warning prompt component, so as to ensure that the early warning components are concentrated in the eye-catching area; and the "data presentation type-trend chart" is transmitted to the type interface of the trend chart component, so that the data that needs to show the change trend is presented in the form of a chart, and it is ensured that each component loads the corresponding attribute configuration.

[0169] Further, the components loaded after configuration are arranged in the interface framework according to layout positions, the device state cards are concentrated in the left device area, the parameter components and the chart components are distributed in the middle parameter area, and the warning components are summarized in the right warning area, so that abnormal information is highlighted, the components are arranged without overlapping and with uniform spacing, visual confusion is avoided, dynamic prompt rules are triggered at the same time, such as displaying parameter standard range when the mouse hovers, displaying the hovering warning reason when warning, and forming a complete visual interface after all components are loaded, thereby generating the real-time monitoring interface of the data warehousing event.

[0170] In summary, the whole process first extracts the monitoring data value by analyzing the serialized structure of the data warehousing event, then clearly displays the source of each key information required for attribute value calculation, and completes attribute value calculation combined with the data deviation degree and the importance of service level, each step is around the matching of data characteristics and visualization needs, ensuring that the conversion from event data to attribute configuration is accurate and meets the intuitive presentation requirements of agricultural planting equipment monitoring, providing clear and standardized attribute support for interface rendering.

[0171] In summary, the whole process focuses on the whole process from data to interface, first relies on event deserialization to obtain core monitoring data, then calculates comprehensive display attribute values through multi-dimensional key information, and finally realizes configuration loading and interface arrangement with the help of the interface of the pre-defined component, each step takes the readability and practicality of the interface as the core, and through the calculation of the display attribute value, the data with high importance or large deviation from the benchmark presents more obvious visual effect, ensuring that the agricultural planting equipment operation data can be clearly and intuitively presented, facilitating the operation and maintenance personnel to quickly grasp the device state, and meeting the actual application requirements of remote monitoring.

[0172] As shown in Figure 2 FIG. 1 is a functional module diagram of an agricultural planting equipment data remote monitoring system provided by an embodiment of the present application.

[0173] The agricultural planting equipment data remote monitoring system 100 can be installed in an electronic device. According to the functions to be implemented, the agricultural planting equipment data remote monitoring system 100 can include a data collection and regularization 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 an interface real-time rendering module 106. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

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

[0175] The data acquisition and regularization module 101 is used for data acquisition and regularization of an original data stream from an agricultural planting device, to obtain a standardized data queue and device metadata of the original data stream;

[0176] The rule mapping and evaluation module 102 is used for rule mapping of a preset rule library and the device metadata, to obtain a business rule of the device metadata, and according to the business rule, key evaluation is performed on the standardized data queue, to obtain a service level identifier of the standardized data queue;

[0177] The routing strategy synthesis module 103 is used for multi-dimensional routing strategy synthesis of a network state parameter of a current communication link and the service level identifier, to obtain a routing decision table of the standardized data queue;

[0178] The strategy routing scheduling module 104 is used for strategy routing scheduling of the standardized data queue based on the routing decision table, to obtain a received data packet of the standardized data queue;

[0179] The data verification and storage module 105 is used for protocol unpacking verification of the received data packet, to obtain valid monitoring data of the received data packet, and transactional persistent storage is performed on the valid monitoring data, to obtain a data warehousing event of the valid monitoring data;

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

[0181] In several embodiments provided in the application, it should be understood that the disclosed method and system can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and another division mode can be used in actual implementation.

[0182] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the modules can be selected to achieve the purpose of the embodiment.

[0183] In addition, each functional module in each embodiment of the application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

[0184] It is apparent for a person skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, but that the present application can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application.

[0185] Embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology and application system that use digital computers or computer-controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

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, obtaining the standardized data queue and equipment metadata of the original data stream; S2, mapping the preset rule base with the equipment metadata to obtain the business rules of the equipment metadata, and evaluating the key of the standardized data queue according to the business rules to obtain the service level identification of the standardized data queue, including: Retrieving the device type identification in the equipment metadata from the preset rule base to obtain the initial rule set of the equipment metadata; Injecting the initial rule set and the running state parameter in the equipment metadata with the rule situation to obtain the business rules of the equipment metadata; According to the key data characteristics in the business rules, the characteristic matching of the standardized data queue is carried out to obtain the key data subset of the standardized data queue; According to the priority strategy in the business rules, the priority mapping of the key data subset is carried out to obtain the transmission priority of the key data subset; Based on the transmission priority and the service quality standard in the business rules, the service level synthesis of the standardized data queue is carried out to obtain the service level identification of the standardized data queue; S3, the network state parameter of the current communication link and the service level identification are synthesized with multi-dimensional routing strategy to obtain the routing decision table of the standardized data queue; S4, based on the routing decision table, the strategy routing scheduling of the standardized data queue is carried out to obtain the received data packet of the standardized data queue; S5, the protocol unpacking verification of the received data packet is carried out to obtain the effective monitoring data of the received data packet; and the transactional persistent storage of the effective monitoring data is carried out to obtain the data warehousing event of the effective monitoring data; S6, the event driven rendering of the data warehousing event is carried out 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, obtaining 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 normalizing the atomic data item to obtain the standardized data queue of the original data stream.

3. The method for remote monitoring of agricultural planting equipment data as described in claim 1, characterized in that, The network state parameter of the current communication link and the service level identification are synthesized with multi-dimensional routing strategy to obtain the routing decision table of the standardized data queue, comprising: Real-time evaluation of the network state parameter of the current communication link to obtain the key network indicators of the network state parameter; According to the service level identification, the routing performance requirement of the standardized data queue is generated; The key network indicators and the routing performance requirement are deduced with the routing strategy to obtain the candidate routing strategy set of the standardized data queue; Adaptively screen the candidate routing strategy set and a network load state of the current communication link to obtain a routing decision table of the standardized data queue.

4. The method of claim 1, wherein the agricultural planting equipment data is transmitted to the remote server through the communication network. The strategy routing scheduling based on the routing decision table is performed on the standardized data queue to obtain a received data packet of the standardized data queue, including: Analyzing the routing decision table to extract a transmission priority index and a candidate path set in the routing decision table; According to the transmission priority index, priority scheduling is performed on the standardized data queue to obtain a sorting queue of the standardized data queue; Based on the candidate path set, routing arrangement is performed on the sorting queue to obtain a path allocation scheme of the sorting queue; Through a transmission path in the path allocation scheme, packet routing reorganization is performed on the sorting queue to obtain a received data packet 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 protocol unpacking verification of the received data packet includes: Performing multi-layer protocol analysis on the received data packet to obtain an unpacked data body of the received data packet; Performing data integrity verification on the unpacked data body to obtain verification data content of the unpacked data body; Extracting data elements of the verification data content to obtain effective monitoring data of the received data packet.

6. The method for remote monitoring of agricultural planting equipment data as described in claim 1, characterized in that, The transactional persistent storage of the effective monitoring data includes: Transaction encapsulation is performed on the effective monitoring data to obtain a transaction operation sequence of the effective monitoring data; The transactional persistent storage is performed on the transaction operation sequence to obtain a data persistent storage record of the transaction operation sequence; Event instantiation is performed on the data persistent storage record to obtain a data warehouse event of the effective monitoring data.

7. The method of claim 1, wherein the agricultural planting equipment data is transmitted to the remote server through a wireless communication network. The event-driven rendering of the data warehouse event includes: ​ Event data deserialization is performed on the data warehouse event to obtain monitoring data values of the data warehouse event; Visual property synthesis calculation is performed on the monitoring data values to obtain display attribute values of the monitoring data values; According to the display attribute values, attribute rendering is performed on a pre-defined visual component to generate a real-time monitoring interface of the data warehouse event.

8. The method of claim 7, wherein the data is transmitted to the server through the communication network. 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.

9. 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 device metadata of the raw data streams; A rule mapping and evaluation module is configured to map a pre-configured rule library with the device metadata to obtain business rules of the device metadata, and perform criticality evaluation on the standardized data queue according to the business rules to obtain a service level identifier of the standardized data queue, including: Retrieving a device type identifier in the device metadata from the pre-configured rule library to obtain an initial rule set of the device metadata; The initial rule set is subjected to rule situation injection with a running state parameter in the device metadata to obtain a service rule of the device metadata; According to a key data feature in the service rule, a feature matching is performed on the standardized data queue to obtain a key data subset of the standardized data queue; According to a priority policy in the service rule, a priority mapping is performed on the key data subset to obtain a transmission priority of the key data subset; Based on the transmission priority and a service quality standard in the service rule, a service level synthesis is performed on the standardized data queue to obtain a service level identifier of the standardized data queue; A routing strategy synthesis module is configured to perform a multi-dimensional routing strategy synthesis on a network state parameter of a current communication link and the service level identifier to obtain a routing decision table of the standardized data queue; A policy routing scheduling module is configured to perform a 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; A data verification storage module is configured to perform a protocol unpacking verification on the received data packet to obtain valid monitoring data of the received data packet, and to perform a transactional persistent storage on the valid monitoring data to obtain a data warehousing event of the valid monitoring data; An interface real-time rendering module is configured to perform an event-driven rendering on the data warehousing event to obtain a real-time monitoring interface of the data warehousing event.

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