Pig house stall remote monitoring and management method and system based on cloud platform

Abstract

The application discloses a pig house stall remote monitoring and management method and system based on a cloud platform. First, a digital stall model which is one-to-one mapped with a physical stall is constructed, piglet electronic ear tags are written into an association field to complete initialization binding of identity and stall position, and a stall mapping database is formed. Then, piglet ear tag record data and stall real-time behavior data are acquired, a piglet stall binding conflict index is calculated through cross verification of occurrence frequency and active period, and according to the index, abnormal piglets are positioned and the binding relationship is corrected to obtain a corrected binding mapping table. In response to a remote supervision instruction, a target stall is determined based on the dynamic mapping table, a monitoring demand label is analyzed to deconstruct data to obtain characteristics, a data acquisition strategy is constructed accordingly, and precise remote supervision of the piglets is realized. The application effectively solves the problem that the piglet stall binding relationship is inaccurate due to stall transfer and stall jumping, and improves the accuracy of pig house stall monitoring data and the remote management efficiency.

Description

Technical Field

[0001] This invention relates to the field of digital management technology for livestock and poultry farming, and in particular to a method and system for remote monitoring and management of pigsty pens based on a cloud platform. Background Technology

[0002] Currently, large-scale pig farming is rapidly developing towards digitalization and intelligence. Individual pig identification and pen location are the foundation for refined management such as precision feeding, health early warning, and growth assessment. In existing technologies, electronic ear tags are typically linked to pen numbers manually or by scanning when pigs are moved into their pens, relying on fixed readers to continuously collect ear tag signals to monitor the pig's location. However, in actual farming, factors such as pigs moving between pens, switching pens, or blind spots in ear tag reading can easily lead to discrepancies between the system-recorded binding relationship and the pig's actual pen location. When the binding relationship is inaccurate, subsequent data on feed intake, behavior, and environment cannot be accurately attributed to the corresponding individual, directly impacting the accuracy of health analysis and production decisions.

[0003] To address the aforementioned issues, some solutions have attempted to incorporate video surveillance or RFID multi-antenna arrays for location correction, but these generally suffer from high deployment costs and poor real-time performance. Furthermore, existing remote monitoring systems often employ fixed data polling strategies, failing to consider the differences in data timeliness and network link status under varying monitoring needs. In the complex wireless environment of pigsties, data delays or packet loss are prone to occur, making it difficult to meet the practical needs of managers for efficient and accurate remote monitoring of specific pigs. Therefore, there is an urgent need for a remote monitoring and management method and system for pigsties that can automatically detect and correct pen binding anomalies and adaptively adjust data acquisition strategies according to monitoring requirements. Summary of the Invention

[0004] To address at least one of the aforementioned technical problems, this invention proposes a method and system for remote monitoring and management of pigsty pens based on a cloud platform.

[0005] The first aspect of this invention provides a method for remote monitoring and management of pigpen pens based on a cloud platform, comprising: A cloud platform for monitoring pig pen positions is constructed. The cloud platform has a pre-set digital pen model that maps one-to-one with the physical pen positions. The electronic ear tags of each pig are written into the associated fields of the digital pen model to complete the initial binding of pig identity and pen position, and a pen mapping database containing the initial binding relationship is obtained. Based on the pen mapping database, the ear tag record data of the pigs to be monitored and the real-time behavior data of the corresponding pen are obtained. The frequency of occurrence in the ear tag record data is cross-validated with the active time period of the real-time behavior data to obtain the pig pen binding conflict index. Based on the binding conflict index, locate the abnormal pigs in the pen binding, correct the binding relationship of the abnormal pigs in the pen binding, and obtain the corrected binding mapping table; In response to the remote data acquisition instruction from the management personnel, the target pen where the pig to be monitored is currently located is determined according to the binding mapping table, the corresponding target data item to be acquired is determined according to the monitoring requirement tag carried in the remote data acquisition instruction, the target data item to be acquired is deconstructed to determine the data acquisition characteristics of the target data item to be acquired in the cloud platform; Based on the data acquisition characteristics, a data acquisition strategy is constructed for the target data items to be acquired. Based on the data acquisition strategy, the target data items to be acquired are acquired for remote monitoring of the pigs to be supervised.

[0006] In this solution, a cloud platform for monitoring pig pens is constructed. This cloud platform has a pre-set digital pen model that maps one-to-one with physical pens. The electronic ear tags of each pig are written into the association field of the digital pen model, completing the initial binding of pig identity and pen location, resulting in a pen mapping database containing the initial binding relationships. Specifically: Obtain the architectural drawing data of the target pigsty, extract the coordinates of the pen partition lines and the aisle partition lines from the architectural drawing data, and construct the spatial topology of the pigsty based on the coordinates of the pen partition lines and the aisle partition lines. A digital twin model of the pigsty is constructed based on the spatial topology of the pigsty. The physical pens are uniquely coded based on the spatial topology of the pigsty. A digital pen model is constructed according to the unique code, which perfectly matches the spatial location, size, and access channels of the physical pens. All digital pen models are topologically associated with the digital twin model of the pigsty according to the actual spatial arrangement of the pigsty. A data acquisition and transmission channel between the pens and the digital twin model of the pigsty is constructed to obtain a cloud platform for monitoring pig pens. Obtain the electronic ear tag identification information of each pig, establish a one-to-one correspondence between the electronic ear tag identification information and the associated field of the digital stall model, write the electronic ear tag identification information into the associated field of the corresponding digital stall model, and record the timestamp information when the electronic ear tag identification information is written. The spatial location information of the digital stall model, the electronic ear tag identification information, and the timestamp information are structurally integrated according to preset field mapping rules to construct initial binding relationship entries containing pig identification, corresponding stall spatial coordinates, and binding time information. All initial binding relationship entries are stored in the unique coding order of the digital stall model to obtain a stall mapping database containing initial binding relationships.

[0007] In this solution, based on the pen mapping database, the ear tag record data of the pigs to be monitored and the real-time behavior data of the corresponding pens are obtained. The frequency of occurrence in the ear tag record data is cross-validated with the active time period of the real-time behavior data to obtain the pig pen binding conflict index, specifically: The binding pen corresponding to the pig to be supervised is determined according to the pen mapping database, and the electronic ear tag record data of the pig to be supervised is obtained. The electronic ear tag record data includes the electronic ear tag identifier, ear tag recognition time and ear tag recognition location. At the same time, the real-time behavior data corresponding to the binding pen is obtained. The real-time behavior data includes the movement trajectory data, dwell position data, activity status data and feeding activity data of the pig in the pen. The electronic ear tag recording data is arranged chronologically according to the ear tag recognition time to obtain the ear tag appearance time sequence of the pigs to be monitored. The occurrence time, location and duration of the pigs' activities in the bound pen are determined according to the movement trajectory data, dwell position data, activity status data and feeding activity data. The real-time behavior data is arranged chronologically according to the activity occurrence time to obtain the pig behavior time sequence of the corresponding pen. The ear tag identification time in the ear tag appearance time sequence is matched with the activity occurrence time in the pig behavior time sequence. The ear tag identification record is then determined to correspond to the pig behavior record in the bound pen based on the spatial relationship between the ear tag identification position and the bound pen, thus obtaining the behavior matching result corresponding to each ear tag identification record. Based on the behavior matching results, the number of times the ear tag recognition record and the pig behavior record correspond to each other is counted, and the number of conflicts in the ear tag recognition record where no pig behavior record is matched is counted. The ear tag behavior consistency rate is determined based on the number of matching and the number of conflicts. The conflict records are segmented according to the number of consecutive occurrences of each conflict record to determine the continuous conflict period. The degree of continuous conflict is determined according to the time ratio of the continuous conflict period in the total ear tag recognition period. The pig pen binding conflict index is obtained by fusing the ear tag behavior consistency rate with the degree of continuous conflict.

[0008] In this scheme, the step of locating abnormally bound pigs in the pen based on the binding conflict index, correcting the binding relationship of the abnormally bound pigs in the pen, and obtaining a corrected binding mapping table is as follows: Extract the ear tag identification position sequence corresponding to the continuous conflict period in the pig pen binding conflict index, and spatially superimpose the ear tag identification position sequence with the coordinates of the channel dividing line in the pig house spatial topology to identify the position jump point that crosses the channel dividing line in the ear tag identification position sequence. The ear tag identification position sequence is divided into several position segments with the position jump point as the boundary. The number of consecutive occurrences of the ear tag identification record in each position segment is counted. The spatial region corresponding to the position segment whose consecutive occurrences meet the set association conditions is mapped to the digital pen model of the pig house digital twin model to match the corresponding position. A temporary association is established between the electronic ear tag identification information corresponding to the ear tag recognition record and the associated field of the matching digital field model. Real-time behavior data of the temporarily associated digital field model is retrieved synchronously. The movement trajectory data in the real-time behavior data is compared with the ear tag recognition position sequence to calculate the trajectory overlap value. The trajectory overlap value and the ear tag behavior consistency rate are weighted and fused to obtain the binding confidence value. The association weight between the electronic ear tag identification information and each digital field model is sorted according to the binding confidence value, and the digital field model with the highest association weight is selected as the target binding field. Based on the target binding field, the binding relationship of the abnormal pigs in the field is corrected, and the corrected binding relationship entries are generated. After all the abnormal pigs in the field are bound and the binding relationship is corrected, all the corrected binding relationship entries are replaced with the corresponding initial binding relationship entries in the field mapping database according to the unique coding order of the digital field model, so as to obtain the corrected binding mapping table.

[0009] In this solution, in response to the remote data acquisition command from the management personnel, the target pen where the pig to be monitored is currently located is determined according to the binding mapping table, the corresponding target data item to be acquired is determined according to the monitoring requirement tag carried in the remote data acquisition command, and the target data item to be acquired is deconstructed to determine the data acquisition characteristics of the target data item to be acquired in the cloud platform. Specifically: Obtain the monitoring requirement tag from the remote data acquisition instruction, extract the pig identification of the pig to be monitored from the monitoring requirement tag, and match the physical pen space coordinates associated with the pig identification to determine the target pen based on the corrected binding mapping table of the pig identification. The monitoring requirement tags are analyzed to determine the regulatory type. Based on the regulatory type, the target data items to be acquired that match the regulatory type of the pigs to be monitored are retrieved from the cloud platform. The data acquisition devices corresponding to each target data item to be acquired are determined, including pen camera devices, electronic ear tag reading devices, environmental sensing devices and behavior monitoring devices. According to the device identifier of the data acquisition device, query the preset data transmission topology of the cloud platform, obtain the data transmission path of the target data item to be acquired from the data acquisition device through the pen edge acquisition node and the pig house gateway to the cloud platform, count the number of relay nodes, historical data transmission delay and data packet loss in the data transmission path, determine the link transmission load characteristics based on the number of relay nodes and the historical transmission delay of data packets, and determine the link reliability characteristics based on the number of data packet loss and the degree of data transmission delay fluctuation. The actual call time and data validity time in the historical monitoring task corresponding to the target data item to be acquired are obtained. The data timeliness requirement characteristics are determined based on the time difference between the actual call time and the data validity time. The target data item to be acquired is determined to be a high-timeliness data item based on the data timeliness requirement characteristics. The data integrity constraint characteristics are determined based on the data integrity requirements of the target data item to be acquired and the historical data loss situation. The link transmission load characteristics, link reliability characteristics, data timeliness requirement characteristics, and data integrity constraint characteristics are structurally combined to form the data acquisition characteristics of the target data item to be acquired.

[0010] In this solution, the step of constructing a data acquisition strategy for the target data item based on the data acquisition features, and then acquiring the target data item for remote monitoring of the pigs to be monitored according to the data acquisition strategy, specifically involves: Based on the data acquisition characteristics of the target data item to be acquired, the data validity time corresponding to the data timeliness requirement characteristics is compared with the current remote data acquisition time to determine the allowable transmission time of the target data item to be acquired, and the current transmission load status corresponding to each data transmission path is determined based on the link transmission load characteristics. Based on the allowed transmission time and the current transmission load status corresponding to each data transmission path, candidate transmission paths that can complete data transmission within the allowed transmission time are selected, and the candidate transmission paths are sorted according to their corresponding link reliability characteristics to determine the target data transmission path. The data integrity verification range of the target data item to be acquired is determined based on the data integrity constraint characteristics, and the target data item to be acquired is divided into continuously changing data and event-triggered data according to the data formation method of the corresponding data acquisition device. For continuously changing data, obtain the most recently uploaded data record in the target data transmission path to the cloud platform, compare the current data to be obtained with the most recently uploaded data, and determine the changed part as the incremental data to be obtained. For event-triggered data, the event data segment corresponding to the pig to be monitored is determined based on the time correlation in the electronic ear tag record, behavior monitoring record and environmental sensor record corresponding to the target data item to be acquired, and the event data segment is determined as event acquisition data; Based on the data timeliness requirements and the allowed transmission time, the order of acquiring incremental data and event data is determined. The data acquisition devices for the corresponding data items send data acquisition instructions according to the acquisition order. The pen edge acquisition nodes in the target data transmission path time-mark the acquired target data items to be acquired, and the time-marked data is transmitted to the pig house gateway according to the target data transmission path, thus forming a data acquisition strategy for the data items to be acquired. According to the data acquisition strategy, target data items are acquired to remotely monitor the pigs under supervision. A second aspect of the invention also provides a remote monitoring and management system for pig pens built on a cloud platform. This system includes a memory and a processor. The memory includes a program for a remote monitoring and management method for pig pens built on a cloud platform. When executed by the processor, the program implements the steps of the remote monitoring and management method for pig pens built on a cloud platform as described in any of the preceding claims.

[0011] This invention discloses a method and system for remote monitoring and management of pigpen locations based on a cloud platform. First, a digital pen model is constructed, mapping each pen to a physical location. Electronic ear tags of pigs are written into an associated field to initialize the binding between their identity and pen location, forming a pen mapping database. Then, pig ear tag records and real-time pen behavior data are acquired. By cross-validating occurrence frequency and active periods, a pig pen binding conflict index is calculated. Based on this index, abnormal pigs are located and their binding relationships are corrected, resulting in a corrected binding mapping table. In response to remote monitoring commands, the target pen is determined based on the dynamic mapping table. Monitoring requirement tags are parsed to deconstruct data acquisition characteristics, and a data acquisition strategy is constructed accordingly, enabling precise remote monitoring of pigs. This invention effectively solves the problem of inaccurate pig pen binding relationships due to pen transfers and cross-pening, improving the accuracy of pigpen monitoring data and the efficiency of remote management. Attached Figure Description

[0012] Figure 1 A flowchart of a remote monitoring and management method for pig pens based on a cloud platform is shown below. Figure 2 The flowchart illustrating the initial binding of pig identity and pen location in this invention is shown. Figure 3 The flowchart illustrating the present invention for determining the pig pen binding conflict index is shown. Figure 4A block diagram of a remote monitoring and management system for pig pens based on a cloud platform, according to the present invention, is shown. Detailed Implementation

[0013] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0014] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0015] Figure 1 The flowchart illustrates a remote monitoring and management method for pig pens based on a cloud platform, according to the present invention.

[0016] like Figure 1 As shown, the first aspect of the present invention provides a method for remote monitoring and management of pig pens based on a cloud platform, comprising: A cloud platform for monitoring pig pen positions is constructed. The cloud platform has a pre-set digital pen model that maps one-to-one with the physical pen positions. The electronic ear tags of each pig are written into the associated fields of the digital pen model to complete the initial binding of pig identity and pen position, and a pen mapping database containing the initial binding relationship is obtained. Based on the pen mapping database, the ear tag record data of the pigs to be monitored and the real-time behavior data of the corresponding pen are obtained. The frequency of occurrence in the ear tag record data is cross-validated with the active time period of the real-time behavior data to obtain the pig pen binding conflict index. Based on the binding conflict index, locate the abnormal pigs in the pen binding, correct the binding relationship of the abnormal pigs in the pen binding, and obtain the corrected binding mapping table; In response to the remote data acquisition instruction from the management personnel, the target pen where the pig to be monitored is currently located is determined according to the binding mapping table, the corresponding target data item to be acquired is determined according to the monitoring requirement tag carried in the remote data acquisition instruction, the target data item to be acquired is deconstructed to determine the data acquisition characteristics of the target data item to be acquired in the cloud platform; Based on the data acquisition characteristics, a data acquisition strategy is constructed for the target data items to be acquired. Based on the data acquisition strategy, the target data items to be acquired are acquired for remote monitoring of the pigs to be supervised.

[0017] It should be noted that this invention constructs a digital pen model that maps one-to-one with physical pens and writes electronic ear tags into the associated fields, enabling precise initial binding of pig identity with pen location. By acquiring ear tag record data and real-time pen behavior data and performing cross-validation, the matching degree between ear tag frequency and active behavior periods is effectively quantified, thereby accurately identifying binding conflicts caused by pen transfers, cross-penning, or abnormal readings, significantly improving the sensitivity of anomaly detection. Abnormal pigs are located based on the binding conflict index, and the binding relationship is corrected, ensuring the real-time nature of identity and location information in the pen mapping database. Target pens are dynamically determined in response to remote commands, and monitoring requirement tags are parsed. Feature deconstruction is performed on the target data items to be acquired, adaptively extracting acquisition features such as data timeliness, link load, and integrity. Finally, based on the above features, a targeted data acquisition strategy is constructed, achieving efficient and low-latency collection and transmission of target data. This enables managers to conduct precise remote monitoring of pigs based on accurate and timely data, significantly improving the automation level and decision reliability of pigpen management.

[0018] Figure 2 The flowchart illustrating the initial binding of pig identity and pen location according to the present invention is shown.

[0019] According to an embodiment of the present invention, the construction of a pigpen monitoring cloud platform includes a pre-set digital pen model that maps one-to-one with physical pens. The electronic ear tags of each pig are written into the association field of the digital pen model to complete the initial binding of pig identity with pen location, resulting in a pen mapping database containing the initial binding relationships. Specifically: Obtain the architectural drawing data of the target pigsty, extract the coordinates of the pen partition lines and the aisle partition lines from the architectural drawing data, and construct the spatial topology of the pigsty based on the coordinates of the pen partition lines and the aisle partition lines. A digital twin model of the pigsty is constructed based on the spatial topology of the pigsty. The physical pens are uniquely coded based on the spatial topology of the pigsty. A digital pen model is constructed according to the unique code, which perfectly matches the spatial location, size, and access channels of the physical pens. All digital pen models are topologically associated with the digital twin model of the pigsty according to the actual spatial arrangement of the pigsty. A data acquisition and transmission channel between the pens and the digital twin model of the pigsty is constructed to obtain a cloud platform for monitoring pig pens. Obtain the electronic ear tag identification information of each pig, establish a one-to-one correspondence between the electronic ear tag identification information and the associated field of the digital stall model, write the electronic ear tag identification information into the associated field of the corresponding digital stall model, and record the timestamp information when the electronic ear tag identification information is written. The spatial location information of the digital stall model, the electronic ear tag identification information, and the timestamp information are structurally integrated according to preset field mapping rules to construct initial binding relationship entries containing pig identification, corresponding stall spatial coordinates, and binding time information. All initial binding relationship entries are stored in the unique coding order of the digital stall model to obtain a stall mapping database containing initial binding relationships.

[0020] It's important to note that establishing a cloud platform for monitoring pig pens is to uniformly map the physical pens, pig identities, and pen locations within the pigsty to a cloud-based digital management environment. This is achieved by constructing the pigsty's spatial topology based on architectural drawings and creating corresponding digital pen models based on the actual locations, dimensions, and access routes of the physical pens. This allows the cloud platform to accurately reflect the spatial distribution and interconnections of each pen within the pigsty. Binding pigs to their pens is crucial because remote pigsty monitoring requires not only identifying the pigs but also determining their specific spatial location. Only by establishing a stable correspondence between identity and pen can subsequent data collection—such as ear tag information, movement trajectories, dwelling behavior, feeding behavior, and pen environment data—be accurately attributed to specific pigs, avoiding the problem of being unable to determine the data's location when summarizing data solely based on pig identity. When pigs are moved to different pens, switched pens, or manually repositioned, the original correspondence between their identity and pen location can still serve as a benchmark for subsequent binding conflict detection and dynamic correction. This allows the cloud platform to identify the deviation between the pig's actual activity location and its initial bound pen location, thereby continuously maintaining the consistency between the pig's identity, pen location, and monitoring data.

[0021] The initial binding of pig identity and pen location includes dynamic selection of multiple binding modes: QR code binding mode: By scanning the QR code on the pig's file card with a mobile terminal, the pig's identity information is obtained. The pig's identity information is then bound to the currently selected digital stall model. The selection focus of the stall is automatically moved according to the scanning order to achieve batch binding. When the ear number of the corresponding stall is scanned, a stall binding prompt is triggered and the process is redirected to the stall binding process. When a placeholder stall or a deleted stall is encountered, the user is prompted to reselect the starting stall and continue scanning. Electronic ear tag reading and binding mode: The pig's identity information is obtained by recognizing the pig's electronic ear tag with the reading stick. The pig's identity information is then bound to the currently selected digital stall model. The Bluetooth connection adopts a global connection mode. The stall selection focus moves automatically. After reading is completed, the binding is submitted for confirmation. Manual selection of binding mode: The target field is selected manually from the list of digital field models, and the corresponding pig identity information is searched and selected from the list of pig ear tags. The binding relationship is manually established and then submitted for confirmation. Clear Pen Mode: For pens that need to be emptied of pigs, it supports single selection, selection by column, selection by row, or full permission selection of pens, and removes the binding relationship between the identities of all pigs under the target pen and the pen, thus releasing the pen resources.

[0022] The pigpen monitoring cloud platform also connects to various smart devices and enables data collaboration between these devices, including: Precision feeder: After being bound to the digital pen model, it automatically matches the feeding strategy according to the pig's identity and pen binding relationship, realizing precise feed control on an individual pig basis; Bluetooth thermometer: Connects to mobile terminals via Bluetooth protocol, collects pig body temperature data and automatically associates it with the pig's identification tag in the current pen. The body temperature data is directly connected to the cloud platform and presented in real time in the form of a pen map. Electronic ear tags: serving as the unique identifier for pigs, they are wirelessly read by a reading stick to complete identification and pen binding; Reading wand: As an electronic ear tag reading device, it communicates with mobile terminals via Bluetooth global connection to read the electronic ear tag identification information of pigs in batches within the pen; Inspection robot: Autonomously inspects the pigsty aisles, scanning the ear tags of pigs in each pen with its onboard recognition device and comparing the information with a pen mapping database to achieve remote inventory and pen verification, including: The patrol robot autonomously travels along the pre-set channels in the pigsty and scans and identifies the electronic ear tags of the pigs in the pens it passes through using the ear tag recognition device on board, thereby obtaining ear tag recognition records and the corresponding pen location information; The ear tag identification records collected by the patrol robot are compared with the binding relationship in the pen mapping database to identify abnormal pigs whose binding relationship is inconsistent with their actual location. Abnormal pigs include pigs that have not updated their binding after being transferred to another pen, pigs that have wandered into another pen, and pigs whose ear tags have fallen off. The comparison results are fed back to the cloud platform, triggering the binding relationship correction process or generating abnormal warning information. At the same time, the difference between the actual number of pigs in each pen and the number recorded by the system is counted, and a pen inventory report is generated.

[0023] The pigpen monitoring cloud platform also supports the linkage management of business documents and pen data. The business documents include mating records, farrowing records, weaning records, non-pregnant return to estrus records, abortion records, breeding pig transfer records, breeding pig death records, abnormal pig registration, hatching inventory list, breeding pig allocation records, breeding pig sales records, and pig body temperature recording records. The method for linking business documents and field data is as follows: In the business document entry interface, after the operator selects a production unit, the system automatically presents the pig information of each column under that production unit according to the corrected binding mapping table. The operator can directly locate and retrieve the corresponding pig's identity, production batch and age information by selecting the column, without having to manually enter the pig number. For mating record forms, the system limits the selection of pigs to the range of mating pen spaces. After selecting a pen space, the corresponding pig's ear tag is automatically populated and the mating information is entered. For farrowing record documents, the system automatically retrieves farrowing unit information based on the pen where the pig is located, and enters farrowing data by selecting the pen and retrieving the ear number. For weaning record forms, the system supports selecting the production line, pig house, and production batch, and then selecting farrowing pigs by pen to enter weaning entries. It also supports continuous entry into multiple pens and submission for review. For breeding pig transfer records, the system supports selecting multiple pigs by pen and then entering the transfer information in one go; For hatching inventory records, the system supports batch reading of electronic ear tags to obtain pig ear numbers using a reading stick, and also supports the function of importing inventory records to complete the inventory of transferred and sold pigs in advance. The system verifies the pigpen locations in business documents to avoid file confusion and reduce data error rates.

[0024] Figure 3 The flowchart illustrating the present invention for determining the pig pen binding conflict index is shown.

[0025] According to an embodiment of the present invention, based on the pen mapping database, the ear tag record data of the pigs to be monitored and the real-time behavior data of the corresponding pens are obtained. The frequency of occurrence in the ear tag record data is cross-validated with the active time period of the real-time behavior data to obtain the pig pen binding conflict index, specifically as follows: The binding pen corresponding to the pig to be supervised is determined according to the pen mapping database, and the electronic ear tag record data of the pig to be supervised is obtained. The electronic ear tag record data includes the electronic ear tag identifier, ear tag recognition time and ear tag recognition location. At the same time, the real-time behavior data corresponding to the binding pen is obtained. The real-time behavior data includes the movement trajectory data, dwell position data, activity status data and feeding activity data of the pig in the pen. The electronic ear tag recording data is arranged chronologically according to the ear tag recognition time to obtain the ear tag appearance time sequence of the pigs to be monitored. The occurrence time, location and duration of the pigs' activities in the bound pen are determined according to the movement trajectory data, dwell position data, activity status data and feeding activity data. The real-time behavior data is arranged chronologically according to the activity occurrence time to obtain the pig behavior time sequence of the corresponding pen. The ear tag identification time in the ear tag appearance time sequence is matched with the activity occurrence time in the pig behavior time sequence. The ear tag identification record is then determined to correspond to the pig behavior record in the bound pen based on the spatial relationship between the ear tag identification position and the bound pen, thus obtaining the behavior matching result corresponding to each ear tag identification record. Based on the behavior matching results, the number of times the ear tag recognition record and the pig behavior record correspond to each other is counted, and the number of conflicts in the ear tag recognition record where no pig behavior record is matched is counted. The ear tag behavior consistency rate is determined based on the number of matching and the number of conflicts. The conflict records are segmented according to the number of consecutive occurrences of each conflict record to determine the continuous conflict period. The degree of continuous conflict is determined according to the time ratio of the continuous conflict period in the total ear tag recognition period. The pig pen binding conflict index is obtained by fusing the ear tag behavior consistency rate with the degree of continuous conflict.

[0026] It should be noted that since the binding relationship between the electronic ear tag and the digital pen model is established when the pig is initially put into the pen, the pig may move between pens, cross pens, or the binding relationship of the electronic ear tag may not be updated in time after the pig is transferred to another pen. Alternatively, the electronic ear tag may be misidentified or missed. This may cause the "pig identity - pen" relationship recorded by the cloud platform to deviate from the actual pen where the pig is located. Therefore, this invention uses electronic ear tag records as a representation of the pig's identity and location, and real-time behavioral data of the corresponding pen as a representation of the pig's actual activity status. Cross-validation is performed through two dimensions: time and space. First, the ear tag recognition time is matched with the time of pig activity within the pen to determine if pig activity was present in that pen at the time the electronic ear tag appeared. Second, the ear tag recognition location is matched with the pig's activity location within the pen to determine if the pig's identity corresponding to the ear tag corresponds to the actual activity in that pen. When the ear tag recognition record can match the pen's pig behavior record within the corresponding time period, it indicates that the electronic ear tag binding relationship is consistent with the actual pen activity. When ear tags appear frequently but there is no synchronous pig behavior in the corresponding pen, or when pig activity continues in the pen but the corresponding ear tag does not appear synchronously, a behavior mismatch record is formed. Furthermore, based on the quantitative relationship between matching and conflicting records, and the continuity of conflicting records, a pig pen binding conflict index is determined, thereby identifying whether the pen currently bound to the electronic ear tag is consistent with the pig's actual pen location.

[0027] According to an embodiment of the present invention, the step of locating abnormally bound pigs in a pen based on the binding conflict index, correcting the binding relationship of the abnormally bound pigs in the pen, and obtaining a corrected binding mapping table specifically involves: Extract the ear tag identification position sequence corresponding to the continuous conflict period in the pig pen binding conflict index, and spatially superimpose the ear tag identification position sequence with the coordinates of the channel dividing line in the pig house spatial topology to identify the position jump point that crosses the channel dividing line in the ear tag identification position sequence. The ear tag identification position sequence is divided into several position segments with the position jump point as the boundary. The number of consecutive occurrences of the ear tag identification record in each position segment is counted. The spatial region corresponding to the position segment whose consecutive occurrences meet the set association conditions is mapped to the digital pen model of the pig house digital twin model to match the corresponding position. A temporary association is established between the electronic ear tag identification information corresponding to the ear tag recognition record and the associated field of the matching digital field model. Real-time behavior data of the temporarily associated digital field model is retrieved synchronously. The movement trajectory data in the real-time behavior data is compared with the ear tag recognition position sequence to calculate the trajectory overlap value. The trajectory overlap value and the ear tag behavior consistency rate are weighted and fused to obtain the binding confidence value. The association weight between the electronic ear tag identification information and each digital field model is sorted according to the binding confidence value, and the digital field model with the highest association weight is selected as the target binding field. Based on the target binding field, the binding relationship of the abnormal pigs in the field is corrected, and the corrected binding relationship entries are generated. After all the abnormal pigs in the field are bound and the binding relationship is corrected, all the corrected binding relationship entries are replaced with the corresponding initial binding relationship entries in the field mapping database according to the unique coding order of the digital field model, so as to obtain the corrected binding mapping table.

[0028] It should be noted that by spatially overlaying the ear tag identification location sequence with the pigpen aisle dividing lines during continuous conflict periods, the location jump points where pigs actually change positions between different pens can be identified. These location jump points are then used to divide continuous location segments, thus avoiding the direct attribution of occasional ear tag misreads to pen changes. Furthermore, based on the number of consecutive occurrences of ear tag records within each location segment, location areas with stable spatial associations are filtered. The trajectory overlap is then calculated by combining the real-time movement trajectory of the corresponding digital pen model with the ear tag identification location sequence, enabling mutual verification between pig identity information and actual activity space. The binding confidence is obtained by fusing the trajectory overlap and ear tag behavior consistency rate, ensuring that binding relationship correction no longer relies solely on a single ear tag reading result, but comprehensively considers the consistency of the pig's continuous location and actual behavior. This allows for accurate differentiation of different situations such as pig transfers, pen skipping, and momentary ear tag misreads, reducing interference from abnormal location data on binding relationship correction. Finally, the target binding pen is determined based on the binding confidence, replacing the original binding relationship, ensuring that the corrected binding mapping table promptly reflects the actual pen location of the pig.

[0029] According to an embodiment of the present invention, in response to a remote data acquisition instruction from a manager, the target pen where the pig to be monitored is currently located is determined according to the binding mapping table, the corresponding target data item to be acquired is determined according to the monitoring requirement tag carried in the remote data acquisition instruction, and the target data item to be acquired is deconstructed to determine the data acquisition characteristics of the target data item to be acquired in the cloud platform, specifically as follows: Obtain the monitoring requirement tag from the remote data acquisition instruction, extract the pig identification identifier of the pig to be monitored from the monitoring requirement tag, and match the physical pen space coordinates associated with the pig identification identifier to determine the target pen according to the corrected binding mapping table. The monitoring requirement tags are analyzed to determine the regulatory type. Based on the regulatory type, the target data items to be acquired that match the regulatory type of the pigs to be monitored are retrieved from the cloud platform. The data acquisition devices corresponding to each target data item to be acquired are determined, including pen camera devices, electronic ear tag reading devices, environmental sensing devices and behavior monitoring devices. According to the device identifier of the data acquisition device, query the preset data transmission topology of the cloud platform, obtain the data transmission path of the target data item to be acquired from the data acquisition device through the pen edge acquisition node and the pig house gateway to the cloud platform, count the number of relay nodes, historical data transmission delay and data packet loss in the data transmission path, determine the link transmission load characteristics based on the number of relay nodes and the historical transmission delay of data packets, and determine the link reliability characteristics based on the number of data packet loss and the degree of data transmission delay fluctuation. The actual call time and data validity time in the historical monitoring task corresponding to the target data item to be acquired are obtained. The data timeliness requirement characteristics are determined based on the time difference between the actual call time and the data validity time. The target data item to be acquired is determined to be a high-timeliness data item based on the data timeliness requirement characteristics. The data integrity constraint characteristics are determined based on the data integrity requirements of the target data item to be acquired and the historical data loss situation. The link transmission load characteristics, link reliability characteristics, data timeliness requirement characteristics, and data integrity constraint characteristics are structurally combined to form the data acquisition characteristics of the target data item to be acquired.

[0030] It should be noted that due to significant differences in data types, collection devices, and transmission conditions corresponding to different regulatory needs, adopting a uniform data acquisition method for all regulatory data can easily lead to problems such as low-timeliness data consuming transmission resources, high-timeliness data failing to be uploaded in a timely manner, data delays caused by link congestion, and critical data loss due to transmission anomalies. By determining the target pen based on the identity of the pig to be regulated and the corrected binding mapping table, and by determining the specific data items and corresponding collection devices in conjunction with the regulatory type, the data acquisition process can be limited to the data range directly related to the target pig and its pen. Further analysis of the data transmission path corresponding to the target data items, by determining the link transmission load characteristics and link reliability characteristics through the number of relay nodes, historical transmission latency, and packet loss, can reflect the degree of congestion and transmission stability that may occur during the transmission of different data items from the collection end to the cloud platform. At the same time, by combining the actual call time, data validity time, and data loss in historical regulatory tasks, the data timeliness requirement characteristics and data integrity constraint characteristics can be determined, which can determine the actual requirements of different data items for transmission timeliness and data integrity. Therefore, by structuring and combining the above features, targeted basis can be provided for transmission path selection, data acquisition order and data transmission method in the subsequent data acquisition process. This enables the cloud platform to implement differentiated data acquisition based on the timeliness, completeness and current transmission conditions of the target data item, avoiding resource waste caused by indiscriminate collection and transmission, and improving the timeliness, reliability and targeting of remote monitoring data acquisition for pigs.

[0031] According to an embodiment of the present invention, the step of constructing a data acquisition strategy for the target data item to be acquired based on the data acquisition features, and acquiring the target data item to be acquired according to the data acquisition strategy for remote monitoring of the pigs to be supervised, specifically includes: Based on the data acquisition characteristics of the target data item to be acquired, the data validity time corresponding to the data timeliness requirement characteristics is compared with the current remote data acquisition time to determine the allowable transmission time of the target data item to be acquired, and the current transmission load status corresponding to each data transmission path is determined based on the link transmission load characteristics. Based on the allowed transmission time and the current transmission load status corresponding to each data transmission path, candidate transmission paths that can complete data transmission within the allowed transmission time are selected, and the candidate transmission paths are sorted according to their corresponding link reliability characteristics to determine the target data transmission path. The data integrity verification range of the target data item to be acquired is determined based on the data integrity constraint characteristics, and the target data item to be acquired is divided into continuously changing data and event-triggered data according to the data formation method of the corresponding data acquisition device. For continuously changing data, obtain the most recently uploaded data record in the target data transmission path to the cloud platform, compare the current data to be obtained with the most recently uploaded data, and determine the changed part as the incremental data to be obtained. For event-triggered data, the event data segment corresponding to the pig to be monitored is determined based on the time correlation in the electronic ear tag record, behavior monitoring record and environmental sensor record corresponding to the target data item to be acquired, and the event data segment is determined as event acquisition data; Based on the data timeliness requirements and the allowed transmission time, the order of acquiring incremental data and event data is determined. The data acquisition devices for the corresponding data items send data acquisition instructions according to the acquisition order. The pen edge acquisition nodes in the target data transmission path time-mark the acquired target data items to be acquired, and the time-marked data is transmitted to the pig house gateway according to the target data transmission path, thus forming a data acquisition strategy for the data items to be acquired. According to the data acquisition strategy, the target data items to be acquired are obtained for remote monitoring of the pigs to be supervised.

[0032] It should be noted that by determining the allowed transmission time based on the data validity time of the target data item to be acquired and the current remote data acquisition time, and by combining the current transmission load status and link reliability characteristics of each data transmission path to filter the target data transmission path, the target monitoring data can be preferentially uploaded through paths with lower transmission load and higher reliability while meeting timeliness requirements, reducing the impact of link congestion and data transmission failure on remote monitoring. Furthermore, the target data item to be acquired is divided into continuously changing data and event-triggered data according to the data formation method of the corresponding data acquisition device because different acquisition devices have different data generation mechanisms. The data formation method is... This refers to the triggering mechanism and time organization method for data acquisition equipment to generate and output monitoring data. Among them, continuously changing data refers to data continuously generated by the acquisition equipment according to continuous sampling or fixed sampling period. Its value will be continuously updated as the pig or pen environment changes. For example, temperature and humidity data continuously collected by environmental sensing equipment and continuous activity status data formed by behavior monitoring equipment. Event-triggered data refers to data generated by the acquisition equipment only when it detects a specific event or meets preset trigger conditions. For example, the ear tag recognition record formed by the electronic ear tag reading device when it detects a pig entering or leaving the pen, and the event record formed by the behavior monitoring equipment when it detects specific behaviors such as feeding or abnormal activities. Because adjacent moments in continuously changing data typically exhibit strong continuity and correlation, transmitting all data completely for each remote monitoring session would result in a large amount of duplicate data. Therefore, by comparing the current data with the most recently uploaded data and only capturing the changed portions, the amount of duplicate data transmission can be reduced. Event-triggered data, on the other hand, has clear event occurrence times and boundaries, and its monitoring value is primarily concentrated in the data before and after a specific event. Therefore, by extracting event data segments corresponding to the pigs to be monitored based on the temporal correlation between electronic ear tag records, behavioral monitoring records, and environmental sensor records, the transmission of historical data irrelevant to the current monitoring objective can be avoided. By employing different data acquisition methods for these two types of data, determining the acquisition order based on data timeliness requirements, and uploading the data to the pig farm gateway via a unified time stamp by the collection nodes at the pen edge along the target transmission path, the cloud platform can prioritize acquiring data of practical value for current remote monitoring under limited network resources. This reduces data transmission volume and link occupancy while improving the timeliness, completeness, and effectiveness of acquiring key monitoring data.

[0033] Figure 4 A block diagram of a remote monitoring and management system for pig pens based on a cloud platform, according to the present invention, is shown.

[0034] A second aspect of the present invention also provides a remote monitoring and management system for pig pens based on a cloud platform. The system includes: a memory 401, a processor 402, and a communication interface 403. The memory includes a program for remote monitoring and management of pig pens based on a cloud platform. The communication interface is used for data connection and communication between the memory and the processor. When the program for remote monitoring and management of pig pens based on a cloud platform is executed by the processor, it implements the steps of the remote monitoring and management method for pig pens based on a cloud platform as described in any of the above claims.

[0035] This invention discloses a method and system for remote monitoring and management of pigpen locations based on a cloud platform. First, a digital pen model is constructed, mapping each pen to a physical location. Electronic ear tags of pigs are written into an associated field to initialize the binding between their identity and pen location, forming a pen mapping database. Then, pig ear tag records and real-time pen behavior data are acquired. By cross-validating occurrence frequency and active periods, a pig pen binding conflict index is calculated. Based on this index, abnormal pigs are located and their binding relationships are corrected, resulting in a corrected binding mapping table. In response to remote monitoring commands, the target pen is determined based on the dynamic mapping table. Monitoring requirement tags are parsed to deconstruct data acquisition characteristics, and a data acquisition strategy is constructed accordingly, enabling precise remote monitoring of pigs. This invention effectively solves the problem of inaccurate pig pen binding relationships due to pen transfers and cross-pening, improving the accuracy of pigpen monitoring data and the efficiency of remote management.

[0036] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0037] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0038] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for remote monitoring and management of pigpen pens based on a cloud platform, characterized in that, Includes the following steps: A cloud platform for monitoring pig pen positions is constructed. The cloud platform has a pre-set digital pen model that maps one-to-one with the physical pen positions. The electronic ear tags of each pig are written into the associated fields of the digital pen model to complete the initial binding of pig identity and pen position, and a pen mapping database containing the initial binding relationship is obtained. Based on the pen mapping database, ear tag record data of the pigs to be monitored and real-time behavior data of the corresponding pens are obtained. The frequency of occurrence in the ear tag record data is cross-validated with the active time periods of the real-time behavior data to obtain the pig pen binding conflict index, specifically: The binding pen corresponding to the pig to be supervised is determined according to the pen mapping database, and the electronic ear tag record data of the pig to be supervised is obtained. The electronic ear tag record data includes the electronic ear tag identifier, ear tag recognition time and ear tag recognition location. At the same time, the real-time behavior data corresponding to the binding pen is obtained. The real-time behavior data includes the movement trajectory data, dwell position data, activity status data and feeding activity data of the pig in the pen. The electronic ear tag recording data is arranged chronologically according to the ear tag recognition time to obtain the ear tag appearance time sequence of the pigs to be monitored. The occurrence time, location and duration of the pigs' activities in the bound pen are determined according to the movement trajectory data, dwell position data, activity status data and feeding activity data. The real-time behavior data is arranged chronologically according to the activity occurrence time to obtain the pig behavior time sequence of the corresponding pen. The ear tag identification time in the ear tag appearance time sequence is matched with the activity occurrence time in the pig behavior time sequence. The ear tag identification record is then determined to correspond to the pig behavior record in the bound pen based on the spatial relationship between the ear tag identification position and the bound pen, thus obtaining the behavior matching result corresponding to each ear tag identification record. Based on the behavior matching results, the number of times the ear tag recognition record and the pig behavior record correspond to each other is counted, and the number of conflicts in the ear tag recognition record where no pig behavior record is matched is counted. The ear tag behavior consistency rate is determined based on the number of matching and the number of conflicts. The conflict records are segmented according to the number of consecutive occurrences of each conflict record to determine the continuous conflict period. The degree of continuous conflict is determined according to the time ratio of the continuous conflict period in the total ear tag recognition period. The pig pen binding conflict index is obtained by fusing the ear tag behavior consistency rate with the degree of continuous conflict. Based on the binding conflict index, locate the abnormal pigs in the pen binding, correct the binding relationship of the abnormal pigs in the pen binding, and obtain the corrected binding mapping table; In response to the remote data acquisition instruction from the management personnel, the target pen where the pig to be monitored is currently located is determined according to the binding mapping table, the corresponding target data item to be acquired is determined according to the monitoring requirement tag carried in the remote data acquisition instruction, the target data item to be acquired is deconstructed to determine the data acquisition characteristics of the target data item to be acquired in the cloud platform; Based on the data acquisition characteristics, a data acquisition strategy is constructed for the target data items to be acquired. Based on the data acquisition strategy, the target data items to be acquired are acquired for remote monitoring of the pigs to be supervised.

2. The method for remote monitoring and management of pigpens based on a cloud platform as described in claim 1, characterized in that, The construction of a pigpen monitoring cloud platform includes a pre-set digital pen model that maps one-to-one with physical pens. The electronic ear tags of each pig are written into the association field of the digital pen model, completing the initial binding of pig identity and pen location, resulting in a pen mapping database containing the initial binding relationships. Specifically: Obtain the architectural drawing data of the target pigsty, extract the coordinates of the pen partition lines and the aisle partition lines from the architectural drawing data, and construct the spatial topology of the pigsty based on the coordinates of the pen partition lines and the aisle partition lines. A digital twin model of the pigsty is constructed based on the spatial topology of the pigsty. The physical pens are uniquely coded based on the spatial topology of the pigsty. A digital pen model is constructed according to the unique code, which perfectly matches the spatial location, size, and access channels of the physical pens. All digital pen models are topologically associated with the digital twin model of the pigsty according to the actual spatial arrangement of the pigsty. A data acquisition and transmission channel between the pens and the digital twin model of the pigsty is constructed to obtain a cloud platform for monitoring pig pens. Obtain the electronic ear tag identification information of each pig, establish a one-to-one correspondence between the electronic ear tag identification information and the associated field of the digital stall model, write the electronic ear tag identification information into the associated field of the corresponding digital stall model, and record the timestamp information when the electronic ear tag identification information is written. The spatial location information of the digital stall model, the electronic ear tag identification information, and the timestamp information are structurally integrated according to preset field mapping rules to construct initial binding relationship entries containing pig identification, corresponding stall spatial coordinates, and binding time information. All initial binding relationship entries are stored in the unique coding order of the digital stall model to obtain a stall mapping database containing initial binding relationships.

3. The method for remote monitoring and management of pigpens based on a cloud platform as described in claim 1, characterized in that, The step of locating abnormally bound pigs in a pen based on the binding conflict index, correcting the binding relationships of these abnormally bound pigs, and obtaining a corrected binding mapping table is specifically as follows: Extract the ear tag identification position sequence corresponding to the continuous conflict period in the pig pen binding conflict index, and spatially superimpose the ear tag identification position sequence with the coordinates of the channel dividing line in the pig house spatial topology to identify the position jump point that crosses the channel dividing line in the ear tag identification position sequence. The ear tag identification position sequence is divided into several position segments with the position jump point as the boundary. The number of consecutive occurrences of the ear tag identification record in each position segment is counted. The spatial region corresponding to the position segment whose consecutive occurrences meet the set association conditions is mapped to the digital pen model of the pig house digital twin model to match the corresponding position. A temporary association is established between the electronic ear tag identification information corresponding to the ear tag recognition record and the associated field of the matching digital field model. Real-time behavior data of the temporarily associated digital field model is retrieved synchronously. The movement trajectory data in the real-time behavior data is compared with the ear tag recognition position sequence to calculate the trajectory overlap value. The trajectory overlap value and the ear tag behavior consistency rate are weighted and fused to obtain the binding confidence value. The association weight between the electronic ear tag identification information and each digital field model is sorted according to the binding confidence value, and the digital field model with the highest association weight is selected as the target binding field. Based on the target binding field, the binding relationship of the abnormal pigs in the field is corrected, and the corrected binding relationship entries are generated. After all the abnormal pigs in the field are bound and the binding relationship is corrected, all the corrected binding relationship entries are replaced with the corresponding initial binding relationship entries in the field mapping database according to the unique coding order of the digital field model, so as to obtain the corrected binding mapping table.

4. The method for remote monitoring and management of pigpens based on a cloud platform as described in claim 1, characterized in that, In response to the remote data acquisition command from the management personnel, the system determines the target pen where the pig to be monitored is currently located based on the binding mapping table, determines the corresponding target data item to be acquired based on the monitoring requirement tag carried in the remote data acquisition command, and performs feature deconstruction on the target data item to be acquired to determine the data acquisition characteristics of the target data item in the cloud platform, specifically as follows: Obtain the monitoring requirement tag from the remote data acquisition instruction, extract the pig identification of the pig to be monitored from the monitoring requirement tag, traverse the corrected binding mapping table according to the pig identification, match the physical pen space coordinates associated with the pig identification and determine it as the target pen; The monitoring requirement tags are analyzed to determine the regulatory type. Based on the regulatory type, the target data items to be acquired that match the regulatory type of the pigs to be monitored are retrieved from the cloud platform. The data acquisition devices corresponding to each target data item to be acquired are determined, including pen camera devices, electronic ear tag reading devices, environmental sensing devices and behavior monitoring devices. According to the device identifier of the data acquisition device, query the preset data transmission topology of the cloud platform, obtain the data transmission path of the target data item to be acquired from the data acquisition device through the pen edge acquisition node and the pig house gateway to the cloud platform, count the number of relay nodes, historical data transmission delay and data packet loss in the data transmission path, determine the link transmission load characteristics based on the number of relay nodes and the historical transmission delay of data packets, and determine the link reliability characteristics based on the number of data packet loss and the degree of data transmission delay fluctuation. The actual call time and data validity time in the historical monitoring task corresponding to the target data item to be acquired are obtained. The data timeliness requirement characteristics are determined based on the time difference between the actual call time and the data validity time. The target data item to be acquired is determined to be a high-timeliness data item based on the data timeliness requirement characteristics. The data integrity constraint characteristics are determined based on the data integrity requirements of the target data item to be acquired and the historical data loss situation. The link transmission load characteristics, link reliability characteristics, data timeliness requirements characteristics, and data integrity constraints are structurally combined to form the data acquisition characteristics of the target data item to be acquired.

5. The method for remote monitoring and management of pigpens based on a cloud platform as described in claim 1, characterized in that, The step of constructing a data acquisition strategy for the target data item based on the data acquisition features, and acquiring the target data item for remote monitoring of the pigs to be monitored according to the data acquisition strategy, specifically involves: Based on the data acquisition characteristics of the target data item to be acquired, the data validity time corresponding to the data timeliness requirement characteristics is compared with the current remote data acquisition time to determine the allowable transmission time of the target data item to be acquired, and the current transmission load status corresponding to each data transmission path is determined based on the link transmission load characteristics. Based on the allowed transmission time and the current transmission load status corresponding to each data transmission path, candidate transmission paths that can complete data transmission within the allowed transmission time are selected, and the candidate transmission paths are sorted according to their corresponding link reliability characteristics to determine the target data transmission path. The data integrity verification range of the target data item to be acquired is determined based on the data integrity constraint characteristics, and the target data item to be acquired is divided into continuously changing data and event-triggered data according to the data formation method of the corresponding data acquisition device. For continuously changing data, obtain the most recently uploaded data record in the target data transmission path to the cloud platform, compare the current data to be obtained with the most recently uploaded data, and determine the changed part as the incremental data to be obtained. For event-triggered data, the event data segment corresponding to the pig to be monitored is determined based on the time correlation in the electronic ear tag record, behavior monitoring record and environmental sensor record corresponding to the target data item to be acquired, and the event data segment is determined as event acquisition data; Based on the data timeliness requirements and the allowed transmission time, the order of acquiring incremental data and event data is determined. The data acquisition devices for the corresponding data items send data acquisition instructions according to the acquisition order. The pen edge acquisition nodes in the target data transmission path time-mark the acquired target data items to be acquired, and the time-marked data is transmitted to the pig house gateway according to the target data transmission path, thus forming a data acquisition strategy for the data items to be acquired. According to the data acquisition strategy, the target data items to be acquired are obtained for remote monitoring of the pigs to be supervised.

6. A remote monitoring and management system for pig pens built on a cloud platform, characterized in that, The cloud-based remote monitoring and management system for pig pens includes a memory and a processor. The memory includes a cloud-based remote monitoring and management method program for pig pens. When the processor executes the cloud-based remote monitoring and management method program for pig pens, it implements the steps of the cloud-based remote monitoring and management method for pig pens as described in any one of claims 1 to 5.

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