Breeding robot operation data traceability and management analysis system and method

Abstract

The application relates to the technical field of data processing, and discloses a breeding robot operation data traceability and management analysis system and method, which comprises the following steps: a state data acquisition module receives operation data and maps the operation data into atomic traceability node data, and extracts a first resident state identification bit of a target business logic unit and a second resident state identification bit of a topological adjacent unit; a survival time sequence analysis module determines a survival time length according to the identification bits, generates a dynamic splitting weight system based on a time length ratio, and a distributed ledger clearing module divides a standard product amount into first share data and multiple second share data according to the dynamic splitting weight system, and writes the first share data and the multiple second share data into corresponding data ledgers respectively. The application breaks the single space coordinate allocation logic, relies on data dynamic segmentation to absorb the time-space deviation caused by material overflow, realizes accurate mapping from underlying data to commercial ledgers, and eliminates the cost mismatch hidden danger.

Description

Technical Field

[0001] This invention relates to a data traceability and management analysis system and method for aquaculture robot operations, belonging to the field of data processing technology. Background Technology

[0002] Currently, in agricultural management data processing systems, management and supervision processes generally deploy automated equipment to output standardized operational actions, simultaneously collecting spatial coordinates and material consumption sequences. This perceived information is then converted and written into a business database to support subsequent cost accounting and product traceability supervision. This one-way data registration mechanism, which directly binds spatial positioning to standard quotas, constitutes a general business analysis infrastructure. However, when this conventional data collection strategy operates in a high-density dynamic environment, its static rights confirmation structural bias gradually becomes apparent. On-site physical construction operations inevitably involve cross-boundary spillover of media, and the target receptor simultaneously maintains dynamic movement. Existing data processing logic absolutely assigns the total consumption captured at a specific coordinate point to a single business accounting unit mapped by that coordinate. This fixed rights confirmation and conversion rule completely strips away the objective physical laws of slippage of commercial value in adjacent topological spaces. The transient spatiotemporal misalignment of action data accumulates over time, leading to deep errors in business analysis and forecasting data.

[0003] To overcome the aforementioned data distortion problem, conventional improvement approaches involve adding high-resolution trajectory tracking devices and global environmental monitoring sensors on-site. Such hardware upgrades increase infrastructure costs and network communication load. Furthermore, due to the lack of a computational mechanism to integrate time-series signals into management and supervision logic, these solutions consistently fail to eliminate the semantic mapping gap between unstructured physical actions and deterministic commercial asset nodes. Hardware perception accuracy and static rights confirmation logic have limitations in complex dynamic environments. If the rights confirmation and conversion rules of the software control layer are not aligned with objective physical laws, they can induce deep data distortion. Chinese invention patent application CN119579197A discloses an intelligent detection and traceability system and method for pig farm breeding data. This system utilizes a sensor network to collect environmental indicators and individual health data and uses blockchain for traceability. In actual breeding operations, physical construction work is accompanied by cross-boundary spills of medicine and feed media, and the target receptors are in a dynamic, cross-boundary migratory state during the transient action.

[0004] Therefore, how to reconstruct the mapping rules from the heterogeneous operation information collected by the sensing nodes to the upper-level supervisory and accounting nodes, and eliminate the data pollution and amortization distortion caused by spatiotemporal mismatch, has become the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A data traceability and management analysis system for livestock farming robots, comprising: The status data acquisition module is used to receive operation data and standard cost equivalent data, map the operation data to atomic traceability node data in the directed acyclic graph structure, encapsulate the standard cost equivalent data in the atomic traceability node data, and extract the first resident status identifier bit of the target business logic unit and the second resident status identifier bits of multiple topologically adjacent units that are topologically adjacent to the target business logic unit. The duration analysis module is connected to the status data acquisition module. It is used to determine the first duration of the target business logic unit based on the first residence status identifier, determine the second duration of each topology adjacent unit based on each second residence status identifier, and generate a dynamic splitting weight system based on the ratio of the first duration to each second duration. The distributed ledger clearing module, connected to the persistence time-series parsing module, is used to divide the standard cost equivalent data encapsulated in the atomic traceability node data into a first share of data and multiple second share data according to the dynamic splitting weight system. The first share of data is written into the first data ledger of the target business logic unit, and the multiple second share data are written into the second data ledger of each corresponding topological adjacency unit along the traceability link edges inside the directed acyclic graph structure.

[0006] Preferably, the state data acquisition module includes: a trajectory parsing unit and a node encapsulation unit; the trajectory parsing unit is used to extract coordinate sequence data contained in the operation data; the node encapsulation unit is connected to the trajectory parsing unit and is used to receive the reference gridded boundary parameters, determine the landing point trajectory parameters in the pre-constructed virtual mapping grid based on the coordinate sequence data, compare the landing point trajectory parameters with the reference gridded boundary parameters, determine the affiliation of the target business logic unit, and encapsulate the metadata containing the affiliation attribute to generate atomic traceability node data.

[0007] Preferably, the duration timing parsing module includes: a status monitoring unit and a timestamp alignment unit; the status monitoring unit is used to read the flip state of the data fields of the first dwell status identifier bit and the second dwell status identifier bit to determine the trigger start time and the termination departure time; the timestamp alignment unit is connected to the status monitoring unit and is used to calculate the time difference between the termination departure time and the trigger start time to obtain the absolute dwell time, and map the absolute dwell time to the same global clock domain, and output the first dwell time and the second dwell time.

[0008] Preferably, the distributed ledger clearing module is used to generate a directed edge data structure between the target business logic unit and each topological adjacent unit after multiple second share data are written; the directed edge data structure includes a weight attribute field and a timestamp field; the distributed ledger clearing module is used to write the basic values ​​of the dynamically split weight system into the weight attribute field to establish a mapping relationship for data flow.

[0009] Preferably, the system further includes: a risk tracing module; the risk tracing module, connected to the distributed ledger clearing module, is used to receive an abnormal trigger signal containing the trigger source business logic unit identifier and time window parameters; the risk tracing module extracts the time window parameters and starts the reverse graph traversal logic along the traceability link edges inside the directed acyclic graph structure; the risk tracing module extracts the device registration code sequence bound between nodes through the reverse graph traversal logic and outputs a risk unit set containing the device registration code sequence.

[0010] Preferably, the distributed ledger clearing module is also used to receive cross-domain clearing depreciation rate parameters and add compensation tracing link edges between non-directly connected business logic unit nodes based on the cross-domain clearing depreciation rate parameters; when the risk tracing module runs the reverse graph tracing logic, it is used to scan the tracing link edges and compensation tracing link edges to generate a data compensation association set that crosses direct topological adjacency relationships in the global data ledger.

[0011] Preferably, the operational data includes material space consumption characterization data; the status data acquisition module is used to receive the environmental consumption probability distribution model, input the material space consumption characterization data into the environmental consumption probability distribution model for comparison; the status data acquisition module outputs the boundary penetration Boolean value for the topological adjacent unit based on the comparison result, and uses the boundary penetration Boolean value as a filtering condition to determine the validity of the second dwell status identifier bit. The system also includes: an operating indicator accounting module; the operating indicator accounting module is connected to the distributed ledger clearing module, used to receive the benchmark asset and liability conversion template, and read the accumulated first share data and second share data from the first data ledger of the target business logic unit and the second data ledger of the topological adjacent unit; the operating indicator accounting module performs parameter mapping between the first share data and the second share data and the benchmark asset and liability conversion template to generate an operating decision report data stream.

[0012] Preferably, the operational indicator calculation module is also used to receive traceability review instructions; the operational indicator calculation module extracts the atomic traceability node data in the locked state in the directed acyclic graph structure according to the traceability review instructions, and combines the feature hash values ​​in the atomic traceability node data into a supply chain traceability verification code sequence; the operational indicator calculation module transmits the supply chain traceability verification code sequence to the preset data review interface.

[0013] A method for data traceability and operational analysis of livestock farming robots includes the following steps: Step S1: Receive operation data and standard cost equivalent data, map the operation data to atomic traceability node data in a directed acyclic graph structure, encapsulate the standard cost equivalent data in the atomic traceability node data, and extract the first residence status flag of the target business logic unit and the second residence status flag of multiple topologically adjacent units that are topologically adjacent to the target business logic unit. Step S2: Determine the first duration of the target business logic unit based on the first residence status identifier bit, determine the second duration of each topology adjacent unit based on each second residence status identifier bit, and generate a dynamic splitting weight system based on the ratio of the first duration to each second duration. Step S3: According to the dynamic splitting weight system, the standard cost equivalent data encapsulated in the atomic traceability node data is divided into a first share of data and multiple second share data. The first share of data is written into the first data ledger of the target business logic unit, and the multiple second share data are written into the second data ledger of each corresponding topological adjacency unit along the traceability link edges inside the directed acyclic graph structure.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. In the data traceability of livestock robot operations, obtain atomic traceability graph nodes containing the target business logic unit identifier and standard cost consumption equivalent. Set the node status as a pending settlement node and start the system's internally preset delayed verification time window. At the end of the time window, read the farm's electronic fence topology map and extract the identifiers of adjacent logic units that share an edge with the target business logic unit. The system simultaneously extracts the duration of the first target residence status identifier of the target business logic unit and the duration of the second target residence status identifier of each adjacent logic unit. Calculate the cross-domain settlement depreciation rate using the above duration parameters, and based on the cross-domain settlement depreciation rate, reduce the standard cost consumption. The cost equivalent is split into a first share of data and a second share of data. The first share of data is linked to the directed acyclic graph node of the target business logic unit. At the same time, the second share of data is extracted as the compensation cost value and a new traceability link is established to link to the directed acyclic graph node of the corresponding adjacent logic unit. This mechanism breaks the rigid data mapping logic of absolute allocation of single spatial coordinates and transforms the discrete spatiotemporal action sequence into a related data flow that is dynamically constrained by the objective state of adjacent topological nodes. By relying on the proportional division of data shares and the network reconstruction of graph connection edges, the spatiotemporal offset deviation caused by physical medium spillover is absorbed, and the mapping path from the underlying multidimensional heterogeneous data to the deterministic flow topology is established.

[0015] 2. This system collects target dwell status identifiers transmitted by environmental infrastructure. The entity duration parameter represented by this identifier is used as a weighting factor to adjust the data splitting ratio. This is integrated with the directed acyclic graph node state update logic of the global business state. The system drives the accounting and writing module to dynamically split the standard cost consumption equivalent encapsulated within the original atomic traceability graph node by calculating the ratio of the dwell time of the identifiers of specific adjacent logical units to that of the target business logical unit. This logic enables the basic environmental sensor status bit data to transcend the single existence verification level and directly evolve into structured mathematical parameters that intervene in the connectivity of the underlying data graph. The system thereby constructs the corresponding law from the physical state bit time characteristics to the connection weight of the upper-level data nodes, so that the writing operation of each piece of underlying traceability data is based on the cross-comparison of the dwell status of adjacent logical units, reducing the cumulative error of data mismatch generated in the unidirectional coordinate information delivery mode. Attached Figure Description

[0016] Figure 1 This is a flowchart of the multi-dimensional traceability of operational data and dynamic ledger clearing process of the present invention; Figure 2 This is a diagram illustrating the evolution of data flow and ledger writing status at the traceability nodes in this invention.

[0017] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] A data traceability and management analysis system for livestock farming robots includes: The status data acquisition module is used to receive operation data and standard cost equivalent data, map the operation data to atomic traceability node data in the directed acyclic graph structure, encapsulate the standard cost equivalent data in the atomic traceability node data, and extract the first resident status identifier bit of the target business logic unit and the second resident status identifier bits of multiple topologically adjacent units that are topologically adjacent to the target business logic unit. The duration analysis module is connected to the status data acquisition module. It is used to determine the first duration of the target business logic unit based on the first residence status identifier, determine the second duration of each topology adjacent unit based on each second residence status identifier, and generate a dynamic splitting weight system based on the ratio of the first duration to each second duration. The distributed ledger clearing module, connected to the persistence time-series parsing module, is used to divide the standard cost equivalent data encapsulated in the atomic traceability node data into a first share of data and multiple second share data according to the dynamic splitting weight system. The first share of data is written into the first data ledger of the target business logic unit, and the multiple second share data are written into the second data ledger of each corresponding topological adjacency unit along the traceability link edges inside the directed acyclic graph structure.

[0020] Preferably, the state data acquisition module includes: a trajectory parsing unit and a node encapsulation unit; the trajectory parsing unit is used to extract coordinate sequence data contained in the operation data; the node encapsulation unit is connected to the trajectory parsing unit and is used to receive the reference gridded boundary parameters, determine the landing point trajectory parameters in the pre-constructed virtual mapping grid based on the coordinate sequence data, compare the landing point trajectory parameters with the reference gridded boundary parameters, determine the affiliation of the target business logic unit, and encapsulate the metadata containing the affiliation attribute to generate atomic traceability node data.

[0021] Preferably, the duration timing parsing module includes: a status monitoring unit and a timestamp alignment unit; the status monitoring unit is used to read the flip state of the data fields of the first dwell status identifier bit and the second dwell status identifier bit to determine the trigger start time and the termination departure time; the timestamp alignment unit is connected to the status monitoring unit and is used to calculate the time difference between the termination departure time and the trigger start time to obtain the absolute dwell time, and map the absolute dwell time to the same global clock domain, and output the first dwell time and the second dwell time.

[0022] Preferably, the duration time series parsing module is used to calculate the duration for the first time series according to the following formula. The liquidation weight coefficient of each topological adjacency cell: ,in, For liquidation weighting coefficients, For the first The second duration corresponding to each topological adjacency unit. The first duration of existence corresponding to the target business logic unit. The duration of each liquidation weight coefficient is the sum of the second durations of all involved topological adjacency units. The duration time sequence parsing module uses each liquidation weight coefficient as the basic value of the dynamic split weight system.

[0023] Preferably, the distributed ledger clearing module is used to generate a directed edge data structure between the target business logic unit and each topological adjacent unit after multiple second share data are written; the directed edge data structure includes a weight attribute field and a timestamp field; the distributed ledger clearing module is used to write the basic values ​​of the dynamically split weight system into the weight attribute field to establish a mapping relationship for data flow.

[0024] Preferably, the system further includes: a risk tracing module; the risk tracing module, connected to the distributed ledger clearing module, is used to receive an abnormal trigger signal containing the trigger source business logic unit identifier and time window parameters; the risk tracing module extracts the time window parameters and starts the reverse graph traversal logic along the traceability link edges inside the directed acyclic graph structure; the risk tracing module extracts the device registration code sequence bound between nodes through the reverse graph traversal logic and outputs a risk unit set containing the device registration code sequence.

[0025] Preferably, the distributed ledger clearing module is also used to receive cross-domain clearing depreciation rate parameters and add compensation tracing link edges between non-directly connected business logic unit nodes based on the cross-domain clearing depreciation rate parameters; when the risk tracing module runs the reverse graph tracing logic, it is used to scan the tracing link edges and compensation tracing link edges to generate a data compensation association set that crosses direct topological adjacency relationships in the global data ledger.

[0026] Preferably, the operational data includes material space consumption characterization data; the status data acquisition module is used to receive the environmental consumption probability distribution model, input the material space consumption characterization data into the environmental consumption probability distribution model for comparison; the status data acquisition module outputs the boundary penetration Boolean value for the topological adjacent unit based on the comparison result, and uses the boundary penetration Boolean value as a filtering condition to determine the validity of the second dwell status identifier bit. The system also includes: an operating indicator accounting module; the operating indicator accounting module is connected to the distributed ledger clearing module, used to receive the benchmark asset and liability conversion template, and read the accumulated first share data and second share data from the first data ledger of the target business logic unit and the second data ledger of the topological adjacent unit; the operating indicator accounting module performs parameter mapping between the first share data and the second share data and the benchmark asset and liability conversion template to generate an operating decision report data stream.

[0027] Preferably, the operational indicator calculation module is also used to receive traceability review instructions; the operational indicator calculation module extracts the atomic traceability node data in the locked state in the directed acyclic graph structure according to the traceability review instructions, and combines the feature hash values ​​in the atomic traceability node data into a supply chain traceability verification code sequence; the operational indicator calculation module transmits the supply chain traceability verification code sequence to the preset data review interface.

[0028] A method for data traceability and operational analysis of livestock farming robots includes the following steps: Step S1: Receive operation data and standard cost equivalent data, map the operation data to atomic traceability node data in a directed acyclic graph structure, encapsulate the standard cost equivalent data in the atomic traceability node data, and extract the first residence status flag of the target business logic unit and the second residence status flag of multiple topologically adjacent units that are topologically adjacent to the target business logic unit. Step S2: Determine the first duration of the target business logic unit based on the first residence status identifier bit, determine the second duration of each topology adjacent unit based on each second residence status identifier bit, and generate a dynamic splitting weight system based on the ratio of the first duration to each second duration. Step S3: According to the dynamic splitting weight system, the standard cost equivalent data encapsulated in the atomic traceability node data is divided into a first share of data and multiple second share data. The first share of data is written into the first data ledger of the target business logic unit, and the multiple second share data are written into the second data ledger of each corresponding topological adjacency unit along the traceability link edges inside the directed acyclic graph structure.

[0029] Example 1: In a high-density, standardized pig farm operating continuously, multiple autonomous robots perform precise feeding and disease prevention spraying tasks within a dynamic physical space. Because the pig pens are separated by non-enclosed fences, when the robots spray medication onto the target pen, the medication drifts and scatters into adjacent pens due to environmental airflow. Furthermore, some active organisms move along the fence boundaries during the robot's transient movements. This results in the robot's end effector hitting the target business logic unit, but the actual material consumption and commercial value absorption are affected. Spatiotemporal slippage can lead to cost mismatch risks between adjacent accounting units. The status data acquisition module in the data traceability and business analysis system for livestock robots receives the spatiotemporal action sequence of the end effector, which includes action codes, physical coordinates, and timestamps. Based on the physical coordinates, it determines the landing point trajectory parameters in a pre-constructed virtual mapping grid. By comparing the landing point trajectory parameters with the baseline gridded boundary parameters, it determines the affiliation of the target business logic unit. At this point, the system marks the generated atomic traceability graph node status as a node to be settled and starts a delayed verification time window to correct the weighting error caused by the spillover of physical media.

[0030] During the delayed verification time window, the persistence time sequence parsing module reads the topology map of the electronic fence in the field area and identifies the adjacent logical units that share an edge with the target business logic unit. The environmental infrastructure synchronously collects the spatial coordinates and occlusion signals of entities through ultra-wideband positioning base stations and infrared beam sensor arrays deployed at the boundaries of each business logic unit. When the ultra-wideband positioning base station resolves that the entity coordinates fall within the effective calibration area of ​​the target business logic unit for three consecutive sampling cycles, and the signal strength at the receiving end of the infrared beam sensor array is lower than the preset blocking threshold, the logic controller outputs a high-level pulse, flipping the target dwell status flag bit from logic zero to logic one. At the same time, it acquires the target dwell status flag bit transmitted by the environmental infrastructure. By locking the persistence time of the first target dwell status flag bit of the entity within the target business logic unit and the persistence time of the second target dwell status flag bit of the entity within each adjacent logical unit, the cross-domain liquidation depreciation rate is calculated. The calculation formula is as follows: ,in, The cross-domain liquidation depreciation rate assigned to a specific adjacent logical unit. The duration of the second target dwell status flag bit for this specific adjacent logical unit. The duration of the first target residency status flag for the target business logic unit. The sum of the duration of the second target residency status identifier bits for all involved adjacent logical units; the distributed ledger clearing module, based on the cross-domain clearing depreciation rate, divides the standard cost consumption equivalent into a first share of data as the actual confirmed consumption value and a second share of data as the compensation cost value, and writes the first share of data into the first data ledger of the target business logical unit, while simultaneously writing the second share of data into the second data ledger of each corresponding adjacent logical unit along the traceability link edge inside the directed acyclic graph structure, thereby completing the status verification of the node to be cleared. At this time, an exclusive causal mapping is established between the underlying physical action and the accounting unit, which is dynamically constrained by the objective status of adjacent topological nodes, eliminating the cost allocation distortion accumulated by unidirectional accounting logic, and enabling the business analysis data flow to have a deterministic basis to support auditing and traceability.

[0031] Example 2: A verification experiment was conducted in a high-density simulated pig farming environment with 12 standardized pens. The experimental platform used the Monte Carlo simulation method to construct the working environment. Its core control equations were based on the physical particle trajectory transmission model and the biological individual random walk model. In order to simulate the measurement non-idealities in real industrial scenarios, Gaussian white noise with a variance of 0.05 was injected into the collected end effector physical coordinate data, and a fluctuating lateral airflow of 0.5 m / s to 2.5 m / s was simulated as a physical disturbance source for material drift. The key parameter sampling period was set to 100 ms. The technical considerations for setting this parameter balance the real-time reporting of sensor data and the processing load of the distributed ledger clearing module. When the ambient airflow rate is in the high range, in order to capture the continuity of the material drift trajectory and avoid signal aliasing under the Nyquist sampling theorem, the sampling period tends to the lower limit of its value range and is set to a reasonable engineering example value of 50 ms.

[0032] The experimental design consisted of four comparison groups. The control group used the traditional rigid spatial coordinate weighting logic. The group with missing features retained timestamp tracking but removed the cross-domain liquidation depreciation rate calculation step. The out-of-range control group set the delay verification time window to 1 second, which is lower than the extreme value of the system's physical inertia period. The experimental group of this invention used the above-mentioned complete liquidation procedure. The experiment formed a problem intensity gradient by changing the lateral airflow rate. The observation index was the deviation rate between the ledger record value of each business logic unit and the preset actual material consumption. The original input data records showed that when the airflow rate was 0.5 m / s, the material physical drift rate was less than 5%. At this time, the deviation rate of the control group was 4.8%, and the deviation of the sum of the first share data and the second share data of the experimental group of this invention from the actual value was 0.2%. When the airflow rate regularly increased to a strong disturbance state of 2.0 m / s, the deviation rate of the control group climbed to 22.4% because it could not capture the material equivalent spilled to the adjacent column. However, the cross-domain liquidation depreciation rate determined by the survival time series analysis module of the experimental group of this invention was higher. The values ​​are 0.15 and 0.12 respectively, ensuring that the compensation cost value is accurately reallocated to the second data ledger of the corresponding adjacent logical units, with the deviation rate maintained below 1.5%. The cross-domain liquidation depreciation rate assigned to a specific adjacent logical unit.

[0033] When the lateral airflow velocity exceeds the performance inflection point of 3.0 m / s, the deviation rate of the sample group of this invention rises nonlinearly to 8.6% due to the excessive diffusion of the physical medium into non-adjacent logical units. Furthermore, the excessively narrow delay verification time window leads to incomplete updates of the weight attribute fields of the compensation traceability link edges. However, when the delay verification time window is set within the range of 2 to 5 seconds, the success rate of state verification of each node reaches 99.2%. The data trend objectively confirms that the solution of this invention, by establishing an exclusive causal mapping that is dynamically constrained by the objective state of adjacent topological nodes, demonstrates error suppression capability as the material drift intensity gradient increases. In particular, the dynamic segmentation of the standard cost consumption equivalent by the distributed ledger clearing module absorbs the original data distortion caused by spatiotemporal slippage and maps the underlying physical actions to commercial ledger records, confirming the stability of the solution in dealing with interference from aquaculture operations.

[0034] Example 3: In a fattening pig house operation scenario with an integrated environmental ventilation control system, multiple autonomous robots complete pneumatic feed spraying operations along a preset track. Due to the nonlinear fluctuations in the local airflow field caused by the change in the rotation speed of the indoor ventilator, the spatial dispersion range of feed particles after spraying is dynamically shifted. At this time, the system completes online calibration of the environmental loss probability distribution model to establish the benchmark boundary of cost distribution. The environmental loss probability distribution model is stored in the local register of the state data acquisition module. Its initial state is defined as a multivariate feature vector containing particle mass, initial spray velocity, and environmental drag coefficient. The functional specifications of the environment require that the sampling frequency of the field wind speed sensor is not less than 2Hz and the measurement accuracy error is within 0.1m / s, thereby supporting Boolean decision logic.

[0035] The system obtains the current wind speed variable through the monitoring unit. and robot movement speed Based on Stokes' law of drag in fluid dynamics, the spatial offset distance of suspended particles in a transverse airflow field is nonlinearly positively correlated with the airflow velocity, and the length of the delay verification time window is determined based on physical quantities. The calculation formula is as follows: ,in, To extend the duration of the delayed verification time window, The physical distance between the center points of adjacent business logic units. For the robot's movement speed, For real-time wind speed variables, The time compensation coefficient is pre-calibrated based on the material dispersion characteristics, and its dimensions are: In this application example, Set to 3.0m, when It is 0.5 m / s and When the speed is 1.2 m / s, we get The time is 6.6s. This value ensures that the system only starts the ledger status verification procedure after the material airflow generated by the end effector falls into the target or adjacent column. Although the high-velocity lateral wind disturbance manifests as a high Reynolds number turbulent state in the macroscopic environment, considering that the feed or medicine particles released by the system's end nozzle are generally distributed in the micrometer range, these fine particles, after leaving the nozzle and undergoing initial high-speed inertial gliding and rapid momentum dissipation, will quickly enter a relatively static suspended state with the surrounding local fluid. At this stage, the microscopic boundary layer airflow slip characteristics wrapped around the particles undergo physical degradation, and the microscale local airflow environment returns to the category of creeping flow with extremely low Reynolds number. The system thus introduces an apparent Reynolds number reduction mapping mechanism into the local boundary flow field, enabling the Stokes drag model, which has been equivalently modified at the microscale, to be legally extended and accurately evaluate the nonlinear viscous frictional resistance experienced by the suspension and drift of particles in the turbulent disturbance field. This establishes a self-consistent physical deduction path between cross-scale fluid conditions at the underlying logic level.

[0036] While determining the delayed verification time window, the state data acquisition module inputs the collected material space loss characterization data into the environmental loss probability distribution model. It then analyzes the mapping operation containing the Gaussian kernel function to evaluate the probability of material penetrating the physical fence and entering the topological adjacent unit. The algorithm conversion steps include: acquiring the three-dimensional wind speed vector and the initial velocity of particle ejection as model input conditions, and outputting continuous probability density spatial distribution characteristics; performing numerical integration and summation on the probability density spatial distribution characteristics within the spatial definite integral domain with the target topological adjacent unit's physical fence as the geometric boundary, calculating the quantitative value of the region's cumulative spillover probability distribution; the logic comparator reads the quantitative value and performs a hard threshold truncation comparison operation. If the obtained spillover probability exceeds a preset significance threshold of 15%, the environmental loss probability distribution model outputs a boundary penetration Boolean value of true for that topological adjacent unit. This Boolean value triggers the persistence time series parsing module to scan the second resident state identifier bit and incorporates the corresponding adjacent logical unit identifier into the current dynamic splitting weight system; the distributed ledger clearing module writes the split second share data and its corresponding cross-domain clearing depreciation rate into the atomic traceability graph. The node weight attribute field completes the incremental update of the directed acyclic graph structure. At this point, the system realizes the automatic correction from the perception of environmental physical variables to the weight of business ledgers, correcting the backtracking deviation of business analysis data caused by inaccurate parameter settings under dynamic and complex working conditions. The control limit of 15% as the preset significance judgment threshold is not set arbitrarily, but is based on the calculation and interception of the statistical noise limit of the equipment in the field. Before formal operation, the system performed multiple cycles of windless benchmark operation tests in an isolation chamber with zero airflow interference. Statistics show that due to the high frequency of the robot's mobile chassis... The system maps the material background physical ejection rate data induced by the oscillation transmission and water hammer effect of pipeline pump valve start-up and shutdown to a Gaussian curve. Then, it extracts the upper limit of the statistical confidence interval with a one-sided confidence level of 95% as the comparison baseline. Only when the instantaneous spillover probability obtained by actual monitoring and analysis exceeds the tolerance limit of this baseline (i.e., the 15% critical value calculated in the embodiment of this article), the system will logically attribute the phenomenon solely to the substantial cross-boundary migration caused by the external airflow field, thereby shielding the hidden danger of misjudgment triggered by mechanical background error.

[0037] Example 4: This example combines Figures 1 to 2 This document describes the data traceability and operational analysis system and methods for livestock farming robots, such as... Figure 1As shown, the multi-dimensional traceability and dynamic ledger clearing process for operational data is initiated by the system input data stage. At this stage, the system receives operational data and standard cost equivalent data. Subsequently, through data mapping and encapsulation operations, the operational data is mapped to atomic traceability node data in a directed acyclic graph structure, and the standard cost equivalent is encapsulated. Simultaneously, the system synchronously performs residency status flag extraction to extract the first residency status flag of the target business logic unit and the second residency status flags of multiple topologically adjacent units. After transmitting the first / second residency status flags, the system enters the duration determination stage, determining the first duration based on the first residency status flag and the second residency status flags based on each second residency status flag. The residence status flag determines the second duration, which in turn triggers the dynamic splitting weight system generation logic. Based on the ratio of the first duration to each second duration, the dynamic splitting weight system is generated and output. Then, it enters the standard cost equivalent segmentation stage. According to the dynamic splitting weight system, the standard cost equivalent data is divided into first share data and multiple second share data. Finally, through data ledger writing and mapping, the multiple share data are written to the corresponding relevant ledgers along the traceability link edges inside the directed acyclic graph. This allows the first share data to be written to the first data ledger of the target business logic unit, and the multiple second share data to be written to the second data ledger of each topological adjacency unit.

[0038] like Figure 2 As shown, the data stream to be processed is mapped and encapsulated as standard cost equivalent data. It is then mapped to atomic traceability node data and encapsulated as standard cost equivalent data, thus transforming it into an atomic traceability node ready state with encapsulated equivalent data. Subsequently, by extracting the first and second identifier bits and determining their duration, the system enters the duration determination state, i.e., extracting the identifier bits and determining the duration. After that, the system uses the ratio to generate a dynamic splitting weight system and divide the equivalent data, so that the data state is migrated to the weight system and splitting ready state (weights are generated and equivalents are divided at this stage). Finally, the data is written to each unit ledger along the traceability link edge for full-process traceability analysis, thereby achieving ledger clearing and traceability ready state, realizing writing to each unit ledger stream separately.

[0039] Example 5: When deploying an operational data traceability system in an automated pig farming workshop, due to the hardware processing tolerances of the end effector and the material flow characteristics, the system determines a benchmark index table for standard cost equivalent data through an offline calibration program. This offline calibration program is applied to atomizing nozzles with specific orifice diameters and liquid medications with specified viscosities. The flowmeter accuracy of the test bench is not less than 0.01 L / min, and the sensitivity of the electronic scale is not less than 0.1 g. The operation process includes driving the robot's end effector to complete a simulated spraying action at constant pressure for 60 seconds in a windless environment and recording the actual mass of discharged material. The pulse count value recorded in the work instruction Calculate the standard cost equivalent of a single work instruction according to the formula. The calculation formula is as follows: ,in, This provides standard cost equivalent data for a single work instruction. The actual material discharge mass calibrated offline. The cumulative pulse count value recorded during the offline calibration process is used. The system repeats the above steps under a pressure gradient of 0.2MPa to 0.6MPa, maps the generated offline calibration result set to the instruction dictionary of the state data acquisition module, completes the initial filling of the cost attribute field in the atomic traceability node data, and provides physical quantity input for dynamic rights confirmation.

[0040] When the system faces a switch to a cross-regional aquaculture mode, the on-site pre-calibration procedure reconstructs the initial adjacency matrix of the directed acyclic graph structure by reading the topology map of the electronic fence in the area. This calibration procedure defines the boundary coordinate system of the business logic units within the controlled physical space and obtains the physical barrier rate of the fence as the initial environmental disturbance attenuation factor. Within the delay verification time window, the mobile monitoring terminal simulates the migration state of biological individuals and records the change of the dwell status flag bit at 50 consecutive sampling points. If the signal overlap between cells is... If the interference exceeds the preset baseline, the system will adjust the length of the delay verification time window to compensate for the error. Its decision logic is described as when For every 5% increase, An additional 0.5s is added to correct the error in determining the second dwelling state flag bit caused by the difference in the physical fence aperture. The calibrated initial adjacency matrix is ​​written into the register of the distributed ledger clearing module, and the underlying topology parameters are physically aligned with the layout of the aquaculture units.

[0041] Example 6: In a large-scale, multi-unit coupled layered egg-laying hen farm, the system deploys a pre-calibration procedure applied to the three-dimensional cage space separated by wire mesh. During the calibration process, RFID reference sources are placed at the geometric center points of each business logic unit, and the status monitoring unit records the signal gain difference of the robot's end effector between different cage levels. Based on the topology map of the electronic fence in the field, the initial spatial loss operator in the three-dimensional coordinate system is determined. The functional specifications of the enabling environment require that the depth sensor on the robot has a sampling frequency of not less than 10Hz and a ranging resolution better than 5mm. The system calculates the roughness coefficient of the physical boundary by acquiring 200 consecutive ranging sample sequences. Using this as the structural input variable of the environmental loss probability distribution model, the spatial occlusion effect of the physical field is transformed into a discrete attenuation matrix with a unique index value, providing a physical benchmark for the allocation of weight attribute fields in the directed acyclic graph structure. The above data matrix construction process consists of two mesh mapping steps: the system divides the space of the field metal wire mesh and support column in the three-dimensional physical coordinate system into hundreds of three-dimensional meshes of equal volume according to the diameter step of the sensor detection spot, and assigns a unique coded index containing the level and horizontal and vertical coordinates to each mesh; the system extracts the roughness coefficient as the obstacle diffuse reflection reduction feature within the scope of each independent mesh, multiplies it with the initial spatial loss operator through the preloaded exponential hindrance equation, and solves the output attenuation rate quantification value that can quantitatively reflect the actual pore penetration ability of each mesh space. Finally, the attenuation rate values ​​generated by all three-dimensional meshes in the field area are filled into the multidimensional tensor structure one by one according to their physical geometric arrangement, thereby generating a weighted attenuation feature matrix that directly supports dynamic allocation.

[0042] When facing a turbulent environment with high humidity, the environmental loss probability distribution model determines the bandwidth parameter of the Gaussian kernel function. To improve the accuracy of boundary penetration Boolean value determination, this bandwidth parameter The determination method depends on the sample distribution characteristics of the material space loss characterization data, and its calculation formula is as follows: ,in, The bandwidth parameter of the Gaussian kernel function. The standard deviation of the spatially distributed material sample. This refers to the number of valid ranging points captured by the status data acquisition module within the delay verification time window; the system completes periodic data density scanning at each cage level, and the obtained bandwidth parameters... If the instantaneous peak probability of material penetrating the physical fence exceeds the preset safety envelope, the distributed ledger clearing module increases the number of compensation traceability links. At this time, the system completes the quantitative compensation correction from the trajectory of microscopic physical particles to the weight of the business ledger, improving the physical consistency of cost confirmation in a multi-layered three-dimensional space environment. In the high-density discrete material dispersion analysis model, in order to establish an equivalent conversion relationship on the statistical dimensions between the macroscopic beam point cloud data captured by the ranging sensor and the microscopic particle group, the system, before commissioning, considers the spatial resolution of the depth sensor and the material distribution. The initial injection volume density of the feed line was calibrated using voxel equivalence. This calibration mechanism defines a unit grid in physical space to ensure that each ranging reflection point that falls into the scanning cone angle and is certified as valid corresponds to a cluster of microparticles with a constant mass expectation on its geometric projection section. Through this macro-micro scale data binding substitution, the number of discrete ranging points that originally only represented the sampling frequency of the equipment is legally transformed into the total number of scattered particle swarm samples with thermodynamic statistical significance, thereby satisfying the input self-consistency requirement for estimating the size of independent and identically distributed random variables in the subsequent bandwidth Gaussian kernel density evaluation formula.

[0043] In risk management scenarios involving feed batch quality anomaly audits, the risk traceability module receives an anomaly trigger signal containing the trigger source business logic unit identifier and time window parameters. Based on the time window parameters, it locks the corresponding timestamp interval and reads the directed acyclic graph (DAG) structure stored in the distributed ledger clearing module. Using this DAG structure, it locates the atomic traceability node data within the target time interval and initiates a reverse graph traversal logic along the traceability link edges within the DAG structure and the compensation traceability link edges added for non-directly connected nodes. The reverse graph traversal logic extracts the corresponding robot attributes based on the device registration code sequence bound between nodes and... The cross-domain data flow path caused by physical material spillage is restored to a set of risk units containing specific robot numbers and original feeding batches, thereby establishing a reverse ownership confirmation link from business ledger anomalies to the underlying physical operation responsibility entities. In the analysis phase of the farm's annual business forecast and budget planning, the business analysis module obtains the first share data and multiple second share data of each business logic unit within the distributed ledger within a preset accounting period. By performing a summation operation on the first share data and multiple second share data, the actual total ownership confirmation consumption of each business logic unit is determined, and the regional resource utilization efficiency index reflecting the deviation of operation execution is calculated. The operational analysis module uses the material space consumption data recorded in each atom traceability graph node to characterize the regional resource utilization efficiency index. The weighted correction is performed, and the corrected resource utilization efficiency index is input into the preset linear regression model. The output is an operational decision report containing the feed purchase volume recommendation for the next accounting cycle and the correction value of the epidemic prevention cost quota. This realizes the automated quantitative mapping from the underlying physical loss dynamic data to the system-level administrative management decision-making basis.

[0044] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A data traceability and operational analysis system for livestock farming robots, characterized in that, include: The status data acquisition module is used to receive operation data and standard cost equivalent data, map the operation data to atomic traceability node data in the directed acyclic graph structure, encapsulate the standard cost equivalent data in the atomic traceability node data, and extract the first resident status identifier bit of the target business logic unit and the second resident status identifier bits of multiple topologically adjacent units that are topologically adjacent to the target business logic unit. The duration analysis module is connected to the status data acquisition module. It is used to determine the first duration of the target business logic unit based on the first residence status identifier, determine the second duration of each topology adjacent unit based on each second residence status identifier, and generate a dynamic splitting weight system based on the ratio of the first duration to each second duration. The distributed ledger clearing module, connected to the persistence time-series parsing module, is used to divide the standard cost equivalent data encapsulated in the atomic traceability node data into a first share of data and multiple second share data according to the dynamic splitting weight system. The first share of data is written into the first data ledger of the target business logic unit, and the multiple second share data are written into the second data ledger of each corresponding topological adjacency unit along the traceability link edges inside the directed acyclic graph structure.

2. The system for tracing and analyzing operational data of a livestock robot according to claim 1, characterized in that, The status data acquisition module includes a trajectory parsing unit and a node encapsulation unit. The trajectory parsing unit is used to extract coordinate sequence data contained in the operation data. The node encapsulation unit is connected to the trajectory parsing unit and is used to receive the baseline gridded boundary parameters, determine the landing point trajectory parameters in the pre-constructed virtual mapping grid based on the coordinate sequence data, compare the landing point trajectory parameters with the baseline gridded boundary parameters, determine the affiliation of the target business logic unit, and encapsulate the metadata containing the affiliation attribute to generate atomic traceability node data.

3. The system for tracing and analyzing operational data of a livestock robot according to claim 1, characterized in that, The duration timing parsing module includes: a status monitoring unit and a timestamp alignment unit; the status monitoring unit is used to read the flip state of the data fields of the first dwell status flag bit and the second dwell status flag bit to determine the trigger start time and the termination departure time; the timestamp alignment unit is connected to the status monitoring unit to calculate the time difference between the termination departure time and the trigger start time to obtain the absolute dwell time, and maps the absolute dwell time to the same global clock domain to output the first dwell time and the second dwell time.

4. The data traceability and operational analysis system for aquaculture robots according to claim 1, characterized in that, The distributed ledger clearing module is used to generate directed edge data structures between the target business logic unit and each topological adjacent unit after multiple second-share data are written. The directed edge data structure includes a weight attribute field and a timestamp field. The distributed ledger clearing module is used to write the basic values ​​of the dynamically split weight system into the weight attribute field to establish the mapping relationship of data flow.

5. The system for tracing and analyzing operational data of a livestock robot according to claim 1, characterized in that, The system also includes: a risk tracing module; the risk tracing module, connected to the distributed ledger clearing module, is used to receive anomaly trigger signals containing the business logic unit identifier of the trigger source and time window parameters; the risk tracing module extracts the time window parameters and starts the reverse graph traversal logic along the traceability link edges inside the directed acyclic graph structure; the risk tracing module extracts the device registration code sequence bound between nodes through the reverse graph traversal logic and outputs a risk unit set containing the device registration code sequence.

6. The data traceability and operational analysis system for aquaculture robots according to claim 5, characterized in that, The distributed ledger clearing module is also used to receive cross-domain clearing depreciation rate parameters and add compensation traceability link edges between non-directly connected business logic unit nodes based on the cross-domain clearing depreciation rate parameters. When running the reverse graph tracing logic, the risk tracing module scans the source link edges and compensation source link edges to generate a set of data compensation associations that span direct topological adjacency relationships in the global data ledger.

7. The system for tracing and analyzing operational data of a livestock robot according to claim 1, characterized in that, The operational data includes material space consumption characterization data; the status data acquisition module receives the environmental consumption probability distribution model, inputs the material space consumption characterization data into the environmental consumption probability distribution model for comparison; the status data acquisition module outputs the boundary penetration Boolean value for the topological adjacent unit based on the comparison result, and uses the boundary penetration Boolean value as a filtering condition to determine the validity of the second dwell status flag bit. The system also includes: an operating indicator accounting module; the operating indicator accounting module, connected to the distributed ledger clearing module, receives the benchmark asset and liability conversion template, and reads the accumulated first share data and second share data from the first data ledger of the target business logic unit and the second data ledger of the topological adjacent unit; the operating indicator accounting module maps the first share data and second share data with the benchmark asset and liability conversion template to generate an operating decision report data stream.

8. The data traceability and operational analysis system for aquaculture robots according to claim 7, characterized in that, The operational indicator calculation module is also used to receive traceability review instructions; the operational indicator calculation module extracts the atomic traceability node data that is locked in the directed acyclic graph structure according to the traceability review instructions, and combines the feature hash values ​​in the atomic traceability node data into a supply chain traceability verification code sequence; the operational indicator calculation module transmits the supply chain traceability verification code sequence to the preset data review interface.

9. A method for tracing and analyzing operational data of a livestock robot, used to implement the livestock robot operational data tracing and analysis system described in claim 1, characterized in that, Includes the following steps: Step S1: Receive operation data and standard cost equivalent data, map the operation data to atomic traceability node data in a directed acyclic graph structure, encapsulate the standard cost equivalent data in the atomic traceability node data, and extract the first residence status flag of the target business logic unit and the second residence status flag of multiple topologically adjacent units that are topologically adjacent to the target business logic unit. Step S2: Determine the first duration of the target business logic unit based on the first residence status identifier bit, determine the second duration of each topology adjacent unit based on each second residence status identifier bit, and generate a dynamic splitting weight system based on the ratio of the first duration to each second duration. Step S3: According to the dynamic splitting weight system, the standard cost equivalent data encapsulated in the atomic traceability node data is divided into a first share of data and multiple second share data. The first share of data is written into the first data ledger of the target business logic unit, and the multiple second share data are written into the second data ledger of each corresponding topological adjacency unit along the traceability link edges inside the directed acyclic graph structure.

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