Agricultural machine operation data management method based on block chain

Abstract

The invention discloses an agricultural machine operation data management method based on a block chain, relates to the field of agricultural data management, and solves the problems of incomplete data collection and poor safety and traceability of an existing data management method. In the process of carrying out agricultural machinery operation under a bound operation land parcel, actual operation parameters are collected and preprocessed, basic operation data are generated according to the preprocessed actual operation parameters, core operation data are calculated in combination with the preprocessed actual operation parameters and the basic operation data, and the core operation data are calculated. And packaging the core operation data corresponding to the agricultural machine, performing hash abstract and digital signature, and submitting the data to a block chain through an under-chain contract to complete data management. The method is mainly used for agricultural data management.

Description

Technical Field

[0001] This invention relates to the field of agricultural data management. Background Technology

[0002] With the continuous advancement of agricultural mechanization, intelligentization, and informatization, agricultural machinery generates a large amount of data related to the operation process, quality, resource consumption, and spatial trajectory during field operations. Currently, existing agricultural machinery operation data management methods are mainly based on the following two models:

[0003] Centralized platform management model:

[0004] This model uploads operational data to a central server or data platform via agricultural machinery terminals. The platform then performs data storage, querying, and report generation based on established rules. While simple to deploy and convenient to query, it has significant drawbacks:

[0005] Data is centrally controlled by the platform, which poses a risk of tampering, forgery, or loss; inconsistent data standards among multiple parties and a lack of cross-platform interoperability create data silos.

[0006] The verification process relies on manual spot checks, which is inefficient, prone to disputes, and lacks transparency.

[0007] Offline data collection + manual reporting mode:

[0008] This type of model is often used in scenarios with poor network conditions, and adopts methods such as exporting data via USB flash drive and manually filling out work forms. It has the following problems: low data collection granularity and insufficient accuracy; excessive human intervention in the reporting process, making it difficult to guarantee the authenticity of the data; lack of a reliable evidence storage mechanism, making subsequent traceability difficult and increasing supervision and service costs.

[0009] Furthermore, both of the above management methods suffer from incomplete data collection, poor security and traceability, and the traditional approach to determining "whether the operation is qualified" is difficult to support "completion upon completion" or asynchronous auditing, thus failing to meet the needs of modern agricultural precision supervision and digital governance. These problems urgently need to be addressed. Summary of the Invention

[0010] To address the problems of incomplete data collection, poor security, and poor traceability in existing data management methods, this invention provides a blockchain-based method for managing agricultural machinery operation data.

[0011] A blockchain-based method for managing agricultural machinery operation data, comprising:

[0012] During the operation of agricultural machinery under the bound operation plot, the actual operation parameters are collected and preprocessed. Basic operation data is generated based on the preprocessed actual operation parameters. Core operation data is calculated by combining the preprocessed actual operation parameters and basic operation data. The core operation data corresponding to the agricultural machinery is packaged, hashed, and digitally signed. It is then submitted to the blockchain through an off-chain contract to complete data management.

[0013] Basic operation data includes the first Operation status indication for each sampling period Length of working trajectory Engine speed Power output shaft status Average operating speed Average actual operating depth Average soil moisture Midpoint coordinates ;in, Indicates a valid operation. This indicates an invalid operation. , , This represents the total number of sampling periods. The sampling time interval;

[0014] Core task data includes total task duration. Effective working time Total trajectory length of the land parcel Effective length within the plot Actual completed work area Site consistency judgment results, and quality score of soil-insertion component operation. Fuel consistency judgment results, and completion rate of the work area. And the completion and acceptance results.

[0015] Preferably, the preprocessing methods include noise reduction, interpolation, and data normalization.

[0016] Preferably, the preprocessed actual operating parameters include:

[0017] Effective working width of machinery ;

[0018] No. The first sampling period corresponding to the first sampling period and the Coordinates of each sampling point and ,in, , , and The first The horizontal and vertical coordinates of each sampling point within the bound work area and The first The horizontal and vertical coordinates of each sampling point within the bound work area;

[0019] No. The first sampling period corresponding to the first sampling period and the Actual operating depth of each sampling point and ;

[0020] No. The first sampling period corresponding to the first sampling period and the Soil moisture at each sampling point and ;

[0021] No. The first sampling period corresponding to the first sampling period and the Instantaneous fuel consumption rate at each sampling point and ;

[0022] The 1st and the Sampling time of each sampling point and ;

[0023] No. Engine speed at each sampling point and power output shaft status ,and and Also as the first Engine speed during each sampling period and power output shaft status , Indicates joining, Indicates that the parts are not joined;

[0024] In basic operation data , , , and The expression is:

[0025] ;

[0026] ;

[0027] ;

[0028] ;

[0029] ;

[0030] in,

[0031] This represents a logical indicator function, where the value is 1 if the condition is true and 0 if the condition is false.

[0032] This is the lower limit threshold for rotational speed;

[0033] This is the lower limit threshold for the operating speed.

[0034] Preferably, the total operation time is obtained. and effective working hours The implementation method is as follows:

[0035] ;

[0036] .

[0037] Preferably, the total trajectory length of the land parcel is obtained. and effective length within the plot The implementation methods include:

[0038] ;

[0039] ;

[0040] in, For land parcel indicator functions, , This refers to the closed area where the plot of land is located.

[0041] Preferably, the land parcel consistency determination result is either the work parcel is outside the boundary or the work parcel is not outside the boundary;

[0042] The criteria for determining whether a work site has exceeded its boundaries are as follows: when the following conditions are met... If the work site is deemed to be outside its boundary, then the work site is deemed not to be outside its boundary; otherwise, the work site is deemed not to be outside its boundary.

[0043] ;

[0044] ;

[0045] In the formula, The proportion of exceeding the limit is determined by time. The proportion of boundary crossing determined by distance. For out-of-bounds threshold, For time intervals, For land parcel indicator functions, , This refers to the closed area where the plot of land is located.

[0046] Preferably, a quality score for the installation of components in the ground is obtained. The implementation method is as follows:

[0047] ;

[0048] ;

[0049] ;

[0050] in,

[0051] This represents a logical indicator function, where the value is 1 if the condition is true and 0 if the condition is false.

[0052] These are respectively the depth compliance rate and the soil moisture suitability rate;

[0053] The weights for depth compliance rate and humidity suitability rate are respectively.

[0054] For the target operation depth;

[0055] These are the lower and upper limits of humidity suitable for operation, respectively.

[0056] Allowable depth deviation.

[0057] Preferably, the fuel consistency judgment result is either that the fuel consumption per unit area is consistent with the historical distribution of fuel consumption in the same scenario, or that the fuel consumption per unit area is inconsistent with the historical distribution of fuel consumption in the same scenario.

[0058] The method to determine whether the fuel consumption per unit area is consistent with the historical distribution of fuel consumption in the same scene is: determine whether it exists. The result is yes, indicating that the fuel consumption per unit area is consistent with the historical distribution of fuel consumption in the same scenario; the result is no, indicating that the fuel consumption per unit area is inconsistent with the historical distribution of fuel consumption in the same scenario.

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] In the formula, To standardize the fuel consumption deviation, is the statistical test threshold, is the fuel consumption per unit area, are the mean and standard deviation of the historical distribution of fuel consumption in the same scenario, respectively, is the total fuel consumption, are the upper and lower boundaries of the given reasonable fuel consumption range.

[0064] Preferably, obtain the completion degree of the operation area The implementation method is:

[0065] ;

[0066] , ;

[0067] ;

[0068] Among them, is the operation volume completion rate, is the time efficiency ratio, is the planned operation area, is the planned effective operation time.

[0069] Preferably, the completion qualification determination result is completion qualified or completion unqualified;

[0070] The implementation method for determining whether the completion is qualified is: when , determine that the completion is qualified, otherwise, determine that the completion is unqualified; among them,

[0071] , ;

[0072] ;

[0073] In the formula, is the operation volume completion rate, is the time efficiency ratio, is the planned operation area, is the planned effective operation time, is the quality qualification threshold.

[0074] The beneficial effects of the present invention:

[0075] The blockchain-based agricultural machinery operation data management method proposed by the present invention systematically solves the pain points such as incomplete acquisition data, difficult to guarantee data authenticity, security, inconsistent standards, and poor traceability commonly existing in the existing agricultural machinery operation data management by constructing a trusted data framework of "committing to the chain and ensuring authenticity off the chain", and has the following advantages:

[0076] 1. Data authenticity and process traceability have been significantly improved.

[0077] This invention achieves end-to-end immutability and verifiable source of data by off-chain processing of core operational data generated during agricultural machinery operations (such as operation duration, trajectory, fuel consistency judgment results, and actual completed operation area), and then solidifying the hash digest and signature on-chain. All operational data retains digital signatures and digest records, allowing for rapid tracing and auditing in case of subsequent disputes through a mechanism of "minimum parameter recalculation + hash comparison," significantly improving the credibility of the operational data.

[0078] 2. Standardized and comparable indicators such as work quality, completion rate, and fuel consumption.

[0079] The method defines the quality scoring for the installation of underground components. Completion rate of work area Fuel consistency judgment results

[0080] It has good interpretability and standardization capabilities, supports horizontal comparison of operational behaviors across plots, equipment, and service providers, and provides a mathematical foundation for the platform to establish a "behavior-based credit and efficiency system".

[0081] 3. It can be extended to scenarios such as agricultural finance, insurance, and green assessment.

[0082] The operational data generated by this invention has high credibility, high reconfigurability, and high standardization, making it naturally suitable for extended scenarios such as agricultural insurance claims, carbon footprint assessment, and agricultural credit risk control. It provides a data foundation and reliable support for the subsequent expansion of agricultural financial services, green and compliant agricultural supervision, and smart agriculture assessment systems.

[0083] 4. Strong closed-loop design, good edge compatibility, and strong adaptability.

[0084] This invention can perform preliminary identification and preprocessing on agricultural machinery. It can be used for single-point task management as well as for multi-entity, multi-level agricultural machinery management and operation scheduling systems such as regional supervision. It has good deployment flexibility and scenario adaptability. Attached Figure Description

[0085] Figure 1 This is a schematic diagram illustrating the principle of the blockchain-based agricultural machinery operation data management method described in this invention. Detailed Implementation

[0086] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0087] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0088] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0089] Specific Implementation Method 1: Combination Figure 1 As shown, this embodiment provides a blockchain-based method for managing agricultural machinery operation data, which includes:

[0090] During the operation of agricultural machinery under the bound operation plot, the actual operation parameters are collected and preprocessed. Basic operation data is generated based on the preprocessed actual operation parameters. Core operation data is calculated by combining the preprocessed actual operation parameters and basic operation data. The core operation data corresponding to the agricultural machinery is packaged, hashed, and digitally signed. It is then submitted to the blockchain through an off-chain contract to complete data management.

[0091] Basic operation data includes the first Operation status indication for each sampling period Length of working trajectory Engine speed Power output shaft status Average operating speed Average actual operating depth Average soil moisture Midpoint coordinates ;in, Indicates a valid operation. This indicates an invalid operation. , , This represents the total number of sampling periods;

[0092] Core task data includes total task duration. Effective working time Total trajectory length of the land parcel Effective length within the plot Actual completed work area Site consistency judgment results, and quality score of soil-insertion component operation. Fuel consistency judgment results, and completion rate of the work area. And the completion and acceptance results.

[0093] This invention achieves end-to-end immutability and verifiable source of data by off-chain processing of core operational data generated during agricultural machinery operations (such as operation duration, trajectory, fuel consistency judgment results, and actual completed operation area), and then solidifying the hash digest and signature on-chain. All operational data retains digital signatures and digest records, allowing for rapid tracing and auditing in case of subsequent disputes through a mechanism of "minimum parameter recalculation + hash comparison," significantly improving the credibility of the operational data.

[0094] In practical applications, core operational data can be visualized; the platform can periodically generate operational statistical analysis reports; and in the event of an audit or dispute, it can reconstruct hashes and indicators using the minimum parameter combination to recalculate and verify data without disclosing the complete business plaintext.

[0095] Furthermore, preprocessing methods include noise reduction, interpolation, and data normalization. The purpose of data normalization is to standardize data definitions, unify data formats, units, and precision, and eliminate inconsistencies in standards.

[0096] Furthermore, the actual operational parameters after preprocessing include:

[0097] Effective working width of machinery ;

[0098] No. The first sampling period corresponding to the first sampling period and the Coordinates of each sampling point and ,in, , , and The first The horizontal and vertical coordinates of each sampling point within the bound work area and The first The horizontal and vertical coordinates of each sampling point within the bound work area;

[0099] No. The first sampling period corresponding to the first sampling period and the Actual operating depth of each sampling point and ;

[0100] No. The first sampling period corresponding to the first sampling period and the Soil moisture at each sampling point and ;

[0101] No. The first sampling period corresponding to the first sampling period and the Instantaneous fuel consumption rate at each sampling point and ;

[0102] The 1st and the Sampling time of each sampling point and ;

[0103] No. Engine speed at each sampling point and power output shaft status ,and and Also as the first Engine speed during each sampling period and power output shaft status , Indicates joining, Indicates that the parts are not joined;

[0104] In basic operation data , , , and The expression is:

[0105] Formula 1;

[0106] Formula 2;

[0107] Formula 3;

[0108] Formula 4;

[0109] Formula 5;

[0110] in,

[0111] This represents a logical indicator function, where the value is 1 if the condition is true and 0 if the condition is false.

[0112] This is the lower limit threshold for rotational speed;

[0113] This is the lower limit threshold for the operating speed.

[0114] Furthermore, obtain the total operation time. and effective working hours The implementation method is as follows:

[0115] Formula 6;

[0116] Formula 7;

[0117] in, This represents the sampling time interval.

[0118] In this preferred embodiment, the total operation time is... This represents the total duration from the first valid timestamp to the last valid timestamp, reflecting the task's on-site time and organizational scheduling efficiency; effective operation time. The sum of time slices that meet the operating condition threshold is limited, and an operating status indicator is introduced. That is, only when PTO is engaged ( ), segment speed meets standard ( And the rotational speed of the working parts meets the standard. Only when that time period is specified... Effective working time is included to characterize the effective input of "truly productive work". Effective working time Exclude non-productive processes such as waiting, relocation, and idling from effective operations to prevent inflated workloads caused by prolonged stays / idling; effective operation time. Suppress non-productive behaviors such as "time-farming" and "waiting in circles" to improve the fairness and compliance of settlement; in cases of spot checks or disputes, only disclose the total operation time for the two types of time. and effective working hours By combining on-chain hash commitments, a traceable chain of evidence is formed.

[0119] Furthermore, the total trajectory length of the land parcel is obtained. and effective length within the plot The implementation methods include:

[0120] Formula 8;

[0121] Formula 9;

[0122] in, For land parcel indicator functions, , This refers to the closed area where the plot of land is located.

[0123] The total trajectory length of the land parcel constructed in this preferred embodiment and effective length within the plot It takes into account time synchronization and unified coordinate projection, operation status filtering (obtained by PTO, speed and rotation speed thresholds), geometric discrimination of plot boundaries (determined by the point of the midpoint of the segment being in the closed area where the plot is located, including the boundary, and numerical tolerance is set), segment-level robust handling of cornering / round trip overlap and turning around, and only counts the length of "within the plot and in effective operation", which truly reflects the operation output; the length and area use the same effective operation set and the same boundary caliber, which facilitates linkage settlement and supervision; the closed area where the plot is located can be a closed set formed by polygonal regions.

[0124] Furthermore, the land parcel consistency determination result is either the work parcel is outside the boundary or the work parcel is not outside the boundary;

[0125] The criteria for determining whether a work site has exceeded its boundaries are as follows: when the following conditions are met... If the work site is deemed to be outside its boundary, then the work site is deemed not to be outside its boundary; otherwise, the work site is deemed not to be outside its boundary.

[0126] Formula 10;

[0127] Formula 11;

[0128] In the formula, The proportion of exceeding the limit is determined by time. The proportion of boundary crossing determined by distance. For out-of-bounds threshold, For time intervals, For land parcel indicator functions, , This refers to the closed area where the plot of land is located.

[0129] In this preferred embodiment, the legality of the operation is verified by determining whether the operation trajectory is within the boundary of the target plot, checking for any boundary violations, and the constructed... and The criteria for determining whether a work area has exceeded its boundaries include considerations such as unified projection and time synchronization, boundary closure and tolerance handling, work status filtering (PTO / speed / rotation speed), and the proportion of boundary violations from both length and time perspectives. This standard for determining whether a work area has exceeded its boundaries has the following advantages:

[0130] Accurate and robust: The closed set judgment of the midpoint of the segment and the setting of tolerance significantly reduce the misjudgment of data acquisition caused by boundary jitter and GNSS jump points of the positioning system; the length and time dual perspectives are mutually verified, resulting in higher stability.

[0131] Consistent business scope: Only segments that are "within the plot and in an effective working state" are included in the statistics to avoid interference from non-output activities such as site relocation / idling. The scope is consistent with settlement and subsidy verification.

[0132] To prevent data manipulation and fraud: behaviors such as circling outside the plot, dragging outside the boundary, and idling to extend the time will simultaneously increase the effective workload, making it difficult to "inflate" the effective workload through abnormal trajectories.

[0133] Furthermore, obtain the quality score of the installation of the buried components. The implementation method is as follows:

[0134] Formula 12;

[0135] Formula 13;

[0136] Formula 14;

[0137] in,

[0138] This represents a logical indicator function, where the value is 1 if the condition is true and 0 if the condition is false.

[0139] These are respectively the depth compliance rate and the soil moisture suitability rate;

[0140] The weights for depth compliance rate and humidity suitability rate are respectively.

[0141] For the target operation depth;

[0142] These are the lower and upper limits of humidity suitable for operation, respectively.

[0143] Allowable depth deviation;

[0144] This represents the sampling time interval.

[0145] In this preferred embodiment, the quality of the operation is determined by analyzing whether agronomic parameters such as the depth of operation and soil moisture are within the acceptable range, in order to calculate the quality score of the soil-inserting component. The evaluation results directly correspond to the field acceptance standards based on the criteria of "target depth ± tolerance" and "suitable humidity range". Statistics are only collected within the "effective operation segment" (PTO connection, speed and rotation speed meet the standards), and the system automatically excludes transfer / idling / idling to avoid "quality brushing" during non-production periods. The weights of depth and humidity can be configured according to crop / soil conditions / machine configuration, which is convenient for cross-regional reuse. The calculation only requires the local sensors (depth, humidity, location, status) and public thresholds, and the edge side can output in real time. The framework of this preferred method can be seamlessly incorporated into IoT indicators such as operation depth and width, flow rate / application rate, vibration / flatness to form a multi-dimensional quality score for more refined monitoring and optimization.

[0146] Furthermore, the fuel consistency judgment result is either that the fuel consumption per unit area is consistent with the historical distribution fuel consumption in the same scenario, or that the fuel consumption per unit area is inconsistent with the historical distribution fuel consumption in the same scenario.

[0147] The method to determine whether the fuel consumption per unit area is consistent with the historical distribution of fuel consumption in the same scene is: determine whether it exists. The result is yes, indicating that the fuel consumption per unit area is consistent with the historical distribution of fuel consumption in the same scenario; the result is no, indicating that the fuel consumption per unit area is inconsistent with the historical distribution of fuel consumption in the same scenario.

[0148] Formula 15;

[0149] Formula 16;

[0150] Formula 17;

[0151] Formula 18;

[0152] In the formula, To standardize fuel consumption deviation, The threshold for statistical testing, Fuel consumption per unit area These represent the mean and standard deviation of historical fuel consumption distribution in the same scenario, respectively. This represents total fuel consumption. These are the upper and lower boundaries of a given reasonable range for fuel consumption.

[0153] In this preferred embodiment, the fuel consumption rationality is judged based on fuel consumption per unit area to determine whether there are abnormalities such as energy waste or sensor errors. Obtaining fuel consistency judgment results can detect anomalies and suppress waste; excessively high values ​​indicate abnormal energy consumption (improper operation, abnormal load, poor equipment adjustment, inefficient route, etc.), facilitating on-site correction; fuel consumption and area are cross-checked from independent sources, and high... However, low operating intensity may indicate flow / fuel level sensor deviation, missed data segments, or GNSS jitter leading to underestimation of area. In practical applications, a statistical database should be used to automatically normalize differences between different machine models, soil conditions, and agronomic practices to avoid misjudgments caused by applying a "one-size-fits-all" threshold. (The last sentence appears to be incomplete and possibly refers to a separate issue: "Persistently high...") It may trigger maintenance checks or driver training, etc.

[0154] Furthermore, obtain the completion rate of the work area. The implementation method is as follows:

[0155] Formula 19;

[0156] , Formula 20;

[0157] Formula 21;

[0158] in, For the completion rate of the workload, For time efficiency ratio, For the planned work area, The effective working time is the planned time.

[0159] This preferred implementation calculates completion rate by comparing the completed area with the planned area to determine whether the task has been completed and analyzes whether the work has exceeded time limits, been delayed, or ended early. In practical applications, a unified standard is used to quantify the "completed / incomplete" ratio, facilitating command and dispatch, additional machinery, or adjustments to the work sequence. The completion rate directly corresponds to the target area of ​​the plot and the actual effective coverage (obtained by the buffer-trimming method), naturally aligning with on-site acceptance and contract terms. Based on minimum disclosure and recalculation (the area is obtained by buffering and trimming), and in conjunction with on-chain commitments, third parties can independently verify the data.

[0160] Furthermore, the completion and acceptance result is either "completed and accepted" or "uncompleted and rejected".

[0161] The method for determining whether completion is acceptable is as follows: when If the completion time is 1, the work is deemed complete and acceptable; otherwise, the work is deemed unacceptable. This is the quality qualification threshold.

[0162] In this preferred embodiment, the ratio of the effective working area obtained by the buffer-cutting method to the planned area is used to measure whether "the land area has been fully utilized".

[0163] In terms of time efficiency: the ratio of effective working time to planned working hours is used to measure whether the work is completed on time / within the required working hours.

[0164] The quality score is calculated by weighting the depth compliance rate and humidity suitability rate, reflecting whether the agronomic effect is up to standard.

[0165] Regarding spatiotemporal consistency / compliance: Redundancy consistency scores and thresholds based on the sum of "time percentage within the plot", "length percentage within the plot", and "speed reasonableness rate" are used to determine whether there is a breach of boundaries or abnormal operation.

[0166] Regarding fuel consumption consistency (energy efficiency / anomaly verification): fuel consumption per unit area is used as a threshold to suppress energy waste and sensing errors.

[0167] In terms of scalability and configurability: weights and thresholds can be configured according to crops, soil conditions, machine types and regional policies, which facilitates cross-regional reuse and continuous optimization.

[0168] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A blockchain-based method for managing agricultural machinery operation data, characterized in that: The method includes: During the operation of agricultural machinery under the bound operation plot, the actual operation parameters are collected and preprocessed. Basic operation data is generated based on the preprocessed actual operation parameters. Core operation data is calculated by combining the preprocessed actual operation parameters and basic operation data. The core operation data corresponding to the agricultural machinery is packaged, hashed, and digitally signed. It is then submitted to the blockchain through an off-chain contract to complete data management. Basic operation data includes the first Operation status indication for each sampling period Length of working trajectory Engine speed Power output shaft status Average operating speed Average actual operating depth Average soil moisture Midpoint coordinates ;in, Indicates a valid operation. This indicates an invalid operation. , , This represents the total number of sampling periods. The sampling time interval; Core task data includes total task duration. Effective working time Total trajectory length of the land parcel Effective length within the plot Actual completed work area Site consistency judgment results, and quality score of soil-insertion component operation. Fuel consistency judgment results, and completion rate of the work area. And the completion and acceptance results.

2. The blockchain-based agricultural machinery operation data management method according to claim 1, characterized in that, Preprocessing methods include noise reduction, interpolation, and data normalization.

3. The blockchain-based agricultural machinery operation data management method according to claim 1, characterized in that, The actual operating parameters after preprocessing include: Effective working width of machinery ; No. The first sampling period corresponding to the first sampling period and the Coordinates of each sampling point and ,in, , , and The first The horizontal and vertical coordinates of each sampling point within the bound work area and The first The horizontal and vertical coordinates of each sampling point within the bound work area; No. The first sampling period corresponding to the first sampling period and the Actual operating depth of each sampling point and ; No. The first sampling period corresponding to the first sampling period and the Soil moisture at each sampling point and ; No. The first sampling period corresponding to the first sampling period and the Instantaneous fuel consumption rate at each sampling point and ; The 1st and the Sampling time of each sampling point and ; No. Engine speed at each sampling point and power output shaft status ,and and Also as the first Engine speed during each sampling period and power output shaft status , Indicates joining, This indicates that the connection is not complete; (This is from the basic operation data.) , , , and The expression is: ； ； ； ； ； in, This represents a logical indicator function, where the value is 1 if the condition is true and 0 if the condition is false. This is the lower limit threshold for rotational speed; This is the lower limit threshold for the operating speed.

4. The blockchain-based agricultural machinery operation data management method according to claim 3, characterized in that, Get the total assignment time and effective working hours The implementation method is as follows: ； 。 5. The blockchain-based agricultural machinery operation data management method according to claim 1, characterized in that, Obtain the total trajectory length of the plot and effective length within the plot The implementation methods include:  ； ； in, For land parcel indicator functions, , This refers to the closed area where the plot of land is located.

6. The blockchain-based agricultural machinery operation data management method according to claim 1, characterized in that, The land parcel consistency determination result is either the work parcel is outside the boundary or the work parcel is not outside the boundary; The criteria for determining whether a work site has exceeded its boundaries are as follows: when the following conditions are met... If the work site is deemed to be outside its boundary, then the work site is deemed not to be outside its boundary; otherwise, the work site is deemed not to be outside its boundary. ； ； In the formula, The proportion of exceeding the limit is determined by time. The proportion of boundary crossing determined by distance. For out-of-bounds threshold, For time intervals, For land parcel indicator functions, , This refers to the closed area where the plot of land is located.

7. The blockchain-based agricultural machinery operation data management method according to claim 1, characterized in that, Obtain the quality score of the buried component operation The implementation method is as follows: ； ； ； in, This represents a logical indicator function, where the value is 1 if the condition is true and 0 if the condition is false. These are respectively the depth compliance rate and the soil moisture suitability rate; The weights for depth compliance rate and humidity suitability rate are respectively. For the target operation depth; These are the lower and upper limits of humidity suitable for operation, respectively. Allowable depth deviation.

8. The blockchain-based agricultural machinery operation data management method according to claim 3, characterized in that, The fuel consistency judgment result is either that the fuel consumption per unit area is consistent with the historical distribution of fuel consumption in the same scenario, or that the fuel consumption per unit area is inconsistent with the historical distribution of fuel consumption in the same scenario. The method to determine whether the fuel consumption per unit area is consistent with the historical distribution of fuel consumption in the same scene is: determine whether it exists. The result is yes, indicating that the fuel consumption per unit area is consistent with the historical distribution of fuel consumption in the same scenario; the result is no, indicating that the fuel consumption per unit area is inconsistent with the historical distribution of fuel consumption in the same scenario. ； ； ； ； In the formula, To standardize fuel consumption deviation, The threshold for statistical testing, Fuel consumption per unit area These represent the mean and standard deviation of historical fuel consumption distribution in the same scenario, respectively. This represents total fuel consumption. These are the upper and lower boundaries of a given reasonable range for fuel consumption.

9. The blockchain-based agricultural machinery operation data management method according to claim 3, characterized in that, Obtain the completion rate of the work area The implementation method is as follows: ； ， ； ； in, For the completion rate of the workload, For time efficiency ratio, For the planned work area, The effective working time is the planned time.

10. The blockchain-based agricultural machinery operation data management method according to claim 3, characterized in that, The completion and acceptance result is either "completed and accepted" or "uncompleted". The implementation method for determining whether the completion is qualified is: when is satisfied, it is determined that the completion is qualified; otherwise, it is determined that the completion is unqualified; where ， ； ； In the formula, For the completion rate of the workload, For time efficiency ratio, For the planned work area, For the planned effective working time, This is the quality qualification threshold.

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