Data multi-stage processing method for ZR equipment

Abstract

The invention discloses a data multistage processing method for ZR equipment, and relates to the technical field of data processing, and the method comprises the steps: obtaining to-be-processed ZR equipment control data through the initial control data of the ZR equipment; performing primary processing by using a priority mechanism based on the to-be-processed ZR equipment control data to obtain ZR equipment control data features; performing secondary processing on the ZR equipment control data features to obtain an initial processing result of the ZR equipment control data; constructing a historical ZR equipment control result data set according to the to-be-processed ZR equipment control data; and based on the primary processing result of the ZR equipment control data, performing verification processing by using the historical ZR equipment control result data set to obtain a data multi-stage processing result. The problem that the data synchronization time is too long is solved, and the data processing efficiency and collaboration are improved.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and specifically relates to a multi-level data processing method for ZR devices. Background Technology

[0002] With the development of sensor and IoT technologies, the data acquisition capabilities of ZR (Zero-Range) devices have been significantly improved. Modern sensors can acquire large amounts of high-frequency data in real time, providing a foundation for subsequent multi-level processing. Multi-level processing allows for repeated verification and optimization of the data. At different processing stages, different algorithms and models are used to filter, clean, and verify the data, reducing noise and errors and improving accuracy and reliability. However, with the increase in data volume, existing multi-level data processing methods for ZR devices have the following shortcomings and deficiencies:

[0003] (1) Data complexity and compatibility: As the amount of data increases, the diversity and complexity of data also increase. The data diversity of ZR devices may lead to different data formats and standards. Ensuring that the multi-level data processing method can be compatible with different types of data and effectively integrate and process them is a challenge.

[0004] (2) Data collaboration and efficiency: Different levels of processing may require different data formats, which can easily lead to compatibility problems during data transmission and conversion, resulting in low data processing efficiency;

[0005] (3) Data loss and consistency issues: When data is transferred between different processing stages, data loss or inconsistency may occur. For example, if a certain processing step fails to be executed correctly, subsequent steps may not be able to obtain accurate data, thus affecting the overall processing results.

[0006] (4) Data accuracy: ZR devices usually rely on various sensors to collect data, but the sensors themselves may have certain errors. In addition, interference in data transmission, data fusion problems, and lack of effective verification methods all lead to low accuracy in multi-level data processing.

[0007] Therefore, there is an urgent need for a multi-level data processing method for ZR devices to address the shortcomings of existing technologies. Summary of the Invention

[0008] The purpose of this invention is to propose a multi-level data processing method for ZR devices to solve the problem of excessively long data synchronization time and improve data processing efficiency and collaboration.

[0009] To achieve the above objectives, the present invention provides a multi-level data processing method for ZR devices, comprising the following steps:

[0010] S1. Use the initial control data of the ZR device to obtain the control data of the ZR device to be processed;

[0011] S2. Based on the control data of the ZR device to be processed, a priority mechanism is used to perform first-level processing to obtain the characteristics of the ZR device control data.

[0012] S3. Perform secondary processing on the ZR device control data features to obtain the initial processing result of the ZR device control data;

[0013] S4. Based on the ZR device control data to be processed, construct a historical ZR device control result dataset;

[0014] S5. Based on the initial processing results of the ZR device control data, the historical ZR device control result dataset is used for verification processing to obtain multi-level data processing results.

[0015] Optionally, S1, using the initial control data of the ZR device, obtain the control data of the ZR device to be processed, including:

[0016] Acquire the transmitter control data and receiver control data of the ZR device;

[0017] Determine whether the transmitting end control data and the receiving end control data are consistent. If they are, obtain the transmitting end control data and the receiving end control data as the initial control data of the ZR device, and use the ZR device to build a control data compliance standard and execute the first operation. Otherwise, return to execute the second operation.

[0018] The first operation is as follows: based on the initial control data of the ZR device and the control data compliance standard, obtain the control data of the ZR device to be processed;

[0019] The second operation is: to acquire the transmitter control data and receiver control data of the ZR device;

[0020] The control data compliance standard is the ZR device standard control data corresponding to the initial control data of the ZR device.

[0021] Optionally, based on the initial control data of the ZR device and the control data compliance standard, obtain the control data of the ZR device to be processed, including:

[0022] Obtain the current time as the initial time T for the initial control data of the ZR device;

[0023] Based on the initial time T of the ZR device initial control data, obtain the ZR device initial control data at initial time T, time T+1, and time T+2.

[0024] Determine whether the initial control data of the ZR device at the initial time T, T+1 and T+2 all meet the control data compliance standard. If so, obtain the initial control data of the ZR device at the initial time T, T+1 and T+2 as the control data of the ZR device to be processed. Otherwise, perform the third operation.

[0025] The third operation is as follows: determine whether the initial control data of the ZR device at the initial time T, time T+1 and time T+2 do not meet the control data compliance standard. If so, obtain time nT based on the initial time T and perform the fourth operation. Otherwise, obtain the initial control data of the ZR device that meets the control data compliance standard as the control data to be processed.

[0026] The fourth operation is: using the nT time, obtain the initial control data of the ZR device at nT time, nT+1 time, and nT+2 time, and then perform the fifth operation;

[0027] The fifth operation is to determine whether the initial control data of the ZR device at time nT, time nT+1, and time nT+2 all meet the control data compliance standard. If so, the initial control data of the ZR device at time nT, time nT+1, and time nT+2 are obtained as the control data of the ZR device to be processed. Otherwise, the sixth operation is performed.

[0028] The sixth operation is to determine whether the initial control data of the ZR device at time nT, time nT+1, and time nT+2 do not meet the control data compliance standard. If so, then re-acquire time nT based on the initial time T and return to execute the fourth operation. Otherwise, acquire the initial control data of the ZR device that meets the control data compliance standard as the control data to be processed.

[0029] Wherein, the nT times are all integer multiples of the initial time T.

[0030] Optionally, S2, based on the ZR device control data to be processed, a priority mechanism is used to perform first-level processing to obtain ZR device control data characteristics, including:

[0031] The control data of the ZR equipment to be processed is sequentially cleaned and filtered to obtain preprocessed ZR equipment control data;

[0032] Based on the preprocessed ZR device control data, a priority mechanism is constructed between the ZR device control data. The priority mechanism between the ZR device control data is a mechanism that executes sequentially according to the corresponding timing relationship of the ZR device control data.

[0033] The ZR device control data is processed first-level using a priority mechanism among the ZR device control data to obtain ZR device control data features.

[0034] Optionally, based on the preprocessed ZR device control data, a priority mechanism is constructed among the ZR device control data, including:

[0035] Determine whether there is a logical relationship between the preprocessed ZR device control data. If so, obtain the priority of the preprocessed ZR device control data according to the logical relationship between the preprocessed ZR device control data and execute the seventh operation; otherwise, execute the eighth operation.

[0036] The seventh operation is as follows: mark the preprocessed ZR device control data according to the priority of the preprocessed ZR device control data, obtain the marked ZR device control data, and perform the ninth operation.

[0037] The eighth operation is as follows: based on the preprocessed ZR device control data, obtain preprocessed ZR device control data with logical relationship and preprocessed ZR device control data without logical relationship as the first preprocessed ZR device control data and the second preprocessed ZR device control data, respectively, and execute the tenth operation.

[0038] The ninth operation is: establishing a priority mechanism among ZR device control data based on the marked ZR device control data;

[0039] The tenth operation is: using the first preprocessed ZR device control data, establishing the logical relationship of the first preprocessed ZR device control data, and executing the eleventh operation;

[0040] The eleventh operation is: using the second preprocessed ZR device control data, establishing a parallel relationship for the second preprocessed ZR device control data, and then executing the twelfth operation;

[0041] The twelfth operation is as follows: based on the logical relationship of the first preprocessed ZR device control data and the parallel relationship of the second preprocessed ZR device control data, the timing relationship of the preprocessed ZR device control data is obtained as the priority of the preprocessed ZR device control data, and the seventh operation is executed.

[0042] Optionally, the preprocessed ZR device control data is processed using a priority mechanism among the ZR device control data to obtain ZR device control data characteristics, including:

[0043] The preprocessed ZR device control data is filtered using a priority mechanism among the ZR device control data to obtain the filtered ZR device control data;

[0044] Using the filtered ZR device control data, the first feature, the second feature, and the third feature of the ZR device control data are obtained as the basic control data features of the ZR device.

[0045] Based on the basic control data characteristics of the ZR equipment, the historical ZR equipment control data corresponding to the filtered ZR equipment control data is obtained from the historical ZR equipment control data as the historical ZR equipment control data characteristics.

[0046] Determine whether the first feature of the ZR device control data corresponds to the feature of the historical ZR device control data. If yes, execute the thirteenth operation; otherwise, update the basic control data of the ZR device using the first feature of the ZR device control data and return to the fourteenth operation.

[0047] The thirteenth operation is to determine whether the second feature of the ZR device control data corresponds to the feature of the historical ZR device control data. If yes, the fifteenth operation is performed; otherwise, the basic control data of the ZR device is updated using the second feature of the ZR device control data, and the fourteenth operation is performed.

[0048] The fourteenth operation is: based on the ZR equipment basic control data characteristics, obtain the historical ZR equipment control data corresponding to the filtered ZR equipment control data from the historical ZR equipment control data as the historical ZR equipment control data characteristics;

[0049] The fifteenth operation is as follows: determine whether the third feature of the ZR device control data corresponds to the historical ZR device control data feature. If yes, obtain the first, second, and third features of the ZR device control data as the ZR device control data feature. Otherwise, update the ZR device basic control data using the third feature of the ZR device control data and execute the fourteenth operation.

[0050] Optionally, S3, perform secondary processing on the ZR device control data features to obtain the initial processing result of the ZR device control data, including:

[0051] Using the ZR device to control data features, first training features and second training features are obtained as the training set for the multi-level data processing model;

[0052] The first training feature and the second training feature are input into the data multi-level processing model to obtain the first ZR device control data processing result and the second ZR device control data processing result, respectively.

[0053] Determine whether the control data processing result of the first ZR device is consistent with the control data processing result of the second ZR device. If so, obtain the data multi-level processing model as the trained multi-level processing model and execute the sixteenth operation. Otherwise, update the training set of the data multi-level processing model according to the control data processing result of the first ZR device and the control data processing result of the second ZR device and execute the seventeenth operation.

[0054] The sixteenth operation is to input the ZR device control data features into the trained multi-level processing model to obtain the initial processing results of the ZR device control data.

[0055] The seventeenth operation is as follows: input the first training feature and the second training feature into the data multi-level processing model to obtain the first ZR device control data processing result and the second ZR device control data processing result, respectively.

[0056] Optionally, S4, based on the ZR device control data to be processed, construct a historical ZR device control result dataset, including:

[0057] Based on the ZR device control data to be processed, obtain the historical ZR device control result data corresponding to the ZR device control data to be processed from the historical ZR device control data.

[0058] The historical ZR equipment control result data is divided to obtain a first historical ZR equipment control result dataset and a second historical ZR equipment control result dataset as the primary control result dataset of the historical ZR equipment.

[0059] Determine whether the first historical ZR device control result dataset corresponds to the second historical ZR device control result dataset. If yes, obtain the first historical ZR device control result dataset and the second historical ZR device control result dataset as the historical ZR device control result dataset. Otherwise, update the historical ZR device control result data according to the first historical ZR device control result dataset and the second historical ZR device control result dataset, and perform the eighteenth operation.

[0060] The eighteenth operation is as follows: the historical ZR equipment control result data is divided to obtain a first historical ZR equipment control result dataset and a second historical ZR equipment control result dataset as the primary control result dataset of the historical ZR equipment.

[0061] Optionally, S5, the initial processing result based on the ZR equipment control data is verified using the historical ZR equipment control result dataset to obtain multi-level data processing results, including:

[0062] Determine whether the primary processing result of the ZR device control data matches the historical ZR device control result dataset. If yes, obtain the primary processing result of the ZR device control data as the multi-level data processing result; otherwise, execute the nineteenth operation.

[0063] The nineteenth operation is as follows: determine whether the historical ZR device control result dataset meets the control data compliance standard. If it does, update the transmitter control data and receiver control data of the ZR device according to the primary processing result of the ZR device control data, and perform the twentieth operation. Otherwise, perform the twenty-first operation.

[0064] The twentieth operation is: determining whether the transmitting end control data and the receiving end control data are consistent;

[0065] The 21st operation is as follows: Based on the ZR device control data to be processed, obtain the historical ZR device control result data corresponding to the ZR device control data to be processed from the historical ZR device control data.

[0066] Compared with the closest existing technology, the present invention has the following advantages:

[0067] This invention performs integrity verification through the transmitter and receiver control data of the ZR device, and performs compliance verification using control data compliance standards. This avoids the execution of erroneous instructions and prevents erroneous operations caused by data corruption or loss, thereby improving the accuracy and reliability of the data. By performing multi-level processing on the control data of the ZR device, multi-level data processing for the ZR device can be achieved quickly and accurately. At the same time, it improves data processing efficiency and collaboration, and solves the problem of excessively long data synchronization time. Attached Figure Description

[0068] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0069] Figure 1 This is a flowchart of a multi-level data processing method for ZR devices according to an embodiment of the present invention. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0071] The terminology used in the embodiments section of this invention is for the purpose of explaining specific embodiments of the invention only, and is not intended to limit the invention.

[0072] like Figure 1 As shown, this embodiment of the invention provides a multi-level data processing method for ZR devices, including the following steps:

[0073] S1. Use the initial control data of the ZR device to obtain the control data of the ZR device to be processed;

[0074] This step first acquires and verifies the consistency of control data from both the transmitting and receiving ends to ensure the reliability of the data source. Then, it establishes a control data compliance standard, and based on this standard, performs multiple rounds of verification on the initial control data, selecting data that meets the standard as the data to be processed. This process, through a rigorous data acquisition and verification mechanism, effectively eliminates interference and errors in data transmission, ensuring the accuracy and reliability of the data to be processed. This provides a high-quality data foundation for subsequent processing and significantly reduces the system's misjudgment rate caused by data issues.

[0075] S2. Based on the control data of the ZR device to be processed, a priority mechanism is used to perform first-level processing to obtain the characteristics of the ZR device control data.

[0076] This step first preprocesses the control data of the ZR equipment to be processed, improving data quality. Next, a priority mechanism is established to determine the processing order based on the logical and temporal relationships between data, ensuring orderly data processing. Finally, feature extraction and comparison with historical data are used for verification to ensure that the extracted features accurately reflect the equipment status. This processing mechanism enables in-depth data mining and analysis, optimizes the processing flow through priority ranking, and improves the accuracy of feature extraction by utilizing historical data verification, significantly enhancing the processing capability and reliability of ZR equipment control data.

[0077] S3. Perform secondary processing on the ZR device control data features to obtain the initial processing result of the ZR device control data;

[0078] Secondary processing is performed on the acquired ZR equipment control data features. Through in-depth analysis, computation, or model mapping of the features, the abstract feature data is transformed into preliminary results with practical significance, i.e., the initial processing results of the ZR equipment control data. This process, by transforming data features into preliminary usable results, concretizes abstract feature information into preliminary outputs that can be used for subsequent verification, providing a foundation for the generation of the final data processing results and promoting the data processing workflow towards final application.

[0079] S4. Based on the ZR device control data to be processed, construct a historical ZR device control result dataset;

[0080] This step retrieves control result data corresponding to the current data to be processed from historical data, divides it into two validation sets, and performs correspondence verification to ensure the integrity and consistency of the dataset. This process, by integrating historical data resources, constructs a reference system that can be used to verify the current processing results, providing rich historical experience support for subsequent verification processes, and helping to identify potential problems and optimize processing strategies.

[0081] S5. Based on the initial processing results of the ZR equipment control data, the historical ZR equipment control result dataset is used for verification processing to obtain multi-level data processing results.

[0082] This step compares the initial processing results with the historical control result dataset, and updates the transmitter and receiver control data or reconstructs the historical dataset based on the verification results. This verification mechanism forms a closed-loop feedback system, ensuring the accuracy and reliability of the processing results through comparison with historical data. It also enables timely detection and correction of problems in the historical dataset, continuously improving the system's processing capabilities and adaptability.

[0083] In summary, steps S1 to S5 collect data from both the transmitting and receiving ends and verify their consistency. Data to be processed is then selected based on compliance standards, ensuring the accuracy of the initial data. Following multiple stages of processing, including cleaning, filtering, prioritization, feature extraction, and model training, initial processing results are generated, improving the depth and reliability of data processing. Simultaneously, a historical control result dataset is constructed to provide a reference for verification. Finally, the initial processing results are compared with historical data, forming a closed-loop verification mechanism. This process, through multiple rounds of data verification, deep feature mining, and historical experience reference, significantly improves the processing accuracy and system stability of ZR equipment control data, effectively reduces the false positive rate, and enhances the ability to accurately grasp and control the equipment's operating status.

[0084] As one possible implementation, in the above embodiments, step S1 may specifically include the following steps:

[0085] S1-1. Obtain the transmitter control data and receiver control data of the ZR device;

[0086] This step acquires the transmitter and receiver control data of the ZR device collected by the sensors, providing the raw data source for subsequent data verification and processing. The transmitter control data includes commands and parameters for controlling signal transmission, such as transmit power, signal frequency, modulation method, and transmission timing. The receiver control data includes commands and parameters for controlling signal reception, such as receive gain, filtering parameters, demodulation method, and receive timing. By simultaneously acquiring data from both ends, comprehensive device control information can be obtained, laying the foundation for subsequent assessments of data consistency and device operating status.

[0087] S1-2. Determine whether the transmitting end control data and the receiving end control data are consistent. If so, obtain the transmitting end control data and the receiving end control data as the initial control data of the ZR device, and use the ZR device to build a control data compliance standard. Execute S1-3. Otherwise, return to S1-1.

[0088] This step verifies the consistency between the transmitting and receiving control data to ensure no errors or data loss occur during transmission. Only when the data from both ends is consistent is it used as the initial control data for the ZR device and further processed. Simultaneously, a control data compliance standard is established based on the ZR device, i.e., the standard control data corresponding to the initial control data. Otherwise, data is re-acquired. This verification mechanism effectively avoids erroneous processing due to data transmission problems, improving data reliability and system stability. The control data compliance standard provides a clear reference for subsequent judgments on data compliance, ensuring the accuracy and consistency of data screening.

[0089] S1-3. Obtain the control data of the ZR device to be processed based on the initial control data of the ZR device and the control data compliance standard;

[0090] This step, based on control data compliance standards, involves multiple rounds of verification and screening of the initial control data of the ZR device to obtain the control data of the ZR device to be processed. By collecting data at different time points and verifying its compliance, it can effectively eliminate the interference of accidental factors, ensure the stability and reliability of the data to be processed, and provide high-quality data input for subsequent processing.

[0091] In summary, steps S1-1 to S1-3 acquire and verify the consistency of control data from the ZR device's transceiver, establishing a control data compliance standard. This, in turn, performs compliance checks on data from multiple time points, achieving rigorous screening and preprocessing of the initial control data. Through data comparison at both ends, multi-time point verification, and compliance filtering, this process effectively eliminates data transmission interference and outliers, ensuring the accuracy, completeness, and temporal continuity of the data to be processed. This significantly improves data quality, provides a reliable foundation for subsequent multi-level processing, and reduces the system's false positive rate and processing complexity.

[0092] As one possible implementation, in the above embodiments, steps S1-3 may specifically include the following steps:

[0093] S1-3-1. Obtain the current time as the initial time T for the initial control data of the ZR device;

[0094] This step, by clearly defining the initial time, ensures the consistency of data collection timing, enabling subsequent processing to be carried out within a unified time frame. This avoids data analysis errors caused by time discrepancies and lays the foundation for accurately screening compliant data.

[0095] S1-3-2. Based on the initial time T of the ZR device initial control data, obtain the ZR device initial control data at initial time T, time T+1, and time T+2.

[0096] By collecting data at continuous time points from the initial time T, the dynamic changes in the data can be captured, providing a multi-dimensional reference for subsequent judgment on whether the data meets the control data compliance standards, thus enhancing the comprehensiveness and accuracy of data verification.

[0097] S1-3-3: Determine whether the initial control data of the ZR device at initial time T, time T+1 and time T+2 all meet the control data compliance standard. If so, obtain the initial control data of the ZR device at initial time T, time T+1 and time T+2 as the control data of the ZR device to be processed. Otherwise, execute S1-3-4.

[0098] This step determines whether the initial control data of the ZR device at three consecutive time points all comply with the control data compliance standards. If all three comply, the data is used as the control data to be processed; otherwise, subsequent steps are executed. This mechanism effectively eliminates incidentally compliant data through consistency verification of data at multiple time points, ensuring the stability and reliability of the data to be processed and significantly reducing the interference of abnormal data on subsequent processing.

[0099] S1-3-4. Determine whether the initial control data of the ZR device at the initial time T, time T+1 and time T+2 do not meet the control data compliance standard. If so, obtain time nT based on the initial time T and execute S1-3-5. Otherwise, obtain the initial control data of the ZR device that meets the control data compliance standard as the control data to be processed.

[0100] When data from three consecutive time points does not meet the compliance standards, this step triggers the operation of obtaining time nT based on the initial time T; if some data meets the standards, then the data that meets the standards is directly obtained as the data to be processed. This case-by-case processing strategy ensures that the data collection strategy can be adjusted in a timely manner when data is abnormal, while avoiding over-processing of data that meets the standards, thus improving the efficiency and flexibility of data filtering.

[0101] S1-3-5. Using the nT time, obtain the initial control data of the ZR device at nT time, nT+1 time, and nT+2 time;

[0102] This step involves acquiring initial control data for the ZR device at consecutive times based on time nT, where nT is an integer multiple of the initial time T. By collecting data over a larger time span, it is possible to detect periodic anomalies in the data, expanding the time range for data verification, helping to identify potential systemic problems, and improving the comprehensiveness of data screening.

[0103] S1-3-6. Determine whether the initial control data of the ZR device at time nT, time nT+1, and time nT+2 all meet the control data compliance standard. If so, obtain the initial control data of the ZR device at time nT, time nT+1, and time nT+2 as the control data of the ZR device to be processed. Otherwise, execute S1-3-7.

[0104] This step determines whether the data at time nT and the two consecutive times thereafter meet compliance standards. If so, it is treated as data to be processed; otherwise, analysis continues. This operation repeats compliance verification across an extended time dimension, further enhancing the rigor of data screening and ensuring the stability and reliability of the data to be processed over a longer period.

[0105] S1-3-7. Determine whether the initial control data of the ZR device at time nT, time nT+1, and time nT+2 do not meet the control data compliance standard. If so, re-acquire time nT based on the initial time T and execute S1-3-5. Otherwise, acquire the initial control data of the ZR device that meets the control data compliance standard as the control data to be processed.

[0106] If the data at time nT and the two subsequent times do not meet the standard, time nT is reacquired and the verification process is repeated; if some data meets the standard, the compliant data is directly adopted. This iterative verification mechanism forms a closed-loop screening process, which can continuously track changes in data compliance, ensuring that the data entering subsequent processing stages has high accuracy and reliability, effectively improving the stability and anti-interference capability of the entire system.

[0107] In summary, steps S1-3-1 to S1-3-7 construct a dynamic data filtering and verification mechanism by collecting initial control data of the ZR device at multiple time points and performing compliance verification. This mechanism achieves multi-dimensional temporal verification of the data by setting an initial time T and extending it to times T+1 and T+2, and jumping to time nT for continued verification when verification fails. Through continuous compliance judgment and non-compliance handling strategies, this process effectively eliminates accidental interference and transient anomalies, ensuring the temporal stability and continuous compliance of the data to be processed. This significantly improves data quality and reliability, provides a high-quality data foundation for subsequent processing, and reduces the risk of system misjudgment due to data fluctuations.

[0108] As one possible implementation, in the above embodiments, step S2 may specifically include the following steps:

[0109] S2-1. Perform data cleaning and data filtering on the ZR equipment control data to be processed in sequence to obtain preprocessed ZR equipment control data;

[0110] This step involves preprocessing the control data of the ZR equipment to be processed, such as cleaning and filtering, which can remove noise and outliers, improve data quality, and reduce the impact of interference factors on subsequent analysis.

[0111] S2-2. Based on the preprocessed ZR device control data, construct a priority mechanism among the ZR device control data;

[0112] This step analyzes the logical and temporal relationships between data to determine the data processing order, and then constructs a priority mechanism for the ZR device control data. This ensures that data is processed in a reasonable order, avoiding errors or inefficiencies caused by improper processing order, optimizing the data processing flow, and improving processing efficiency. In this embodiment, the priority mechanism is a mechanism that executes data sequentially based on the temporal relationships of the ZR device control data.

[0113] S2-3. The ZR device control data is processed first-level using the priority mechanism among the ZR device control data to obtain the ZR device control data characteristics.

[0114] This step filters and extracts features from preprocessed ZR equipment control data based on a priority mechanism. It then compares and verifies the extracted multi-dimensional features with historical data to obtain the features of ZR equipment control data. This ensures that the extracted features accurately reflect the equipment status, providing strong support for subsequent model training and result prediction.

[0115] In summary, steps S2-1 to S2-3 first improve data quality through cleaning and filtering, then establish a priority mechanism to optimize the processing order, and finally extract multi-dimensional features and compare them with historical data to ensure feature accuracy. This process, through data purification, process optimization, and feature enhancement, significantly improves data processing capabilities and result reliability, enabling in-depth mining and precise analysis of ZR equipment control data, and providing strong support for equipment control and optimization.

[0116] As one possible implementation, in the above embodiments, step S2-2 may specifically include the following steps:

[0117] S2-2-1. Determine whether there is a logical relationship between the control data of the preprocessing ZR equipment. If so, obtain the priority of the control data of the preprocessing ZR equipment according to the logical relationship of the control data of the preprocessing ZR equipment, and directly execute S2-2-6. Otherwise, execute S2-2-2.

[0118] This step determines whether there is a logical relationship between the preprocessed ZR device control data. If so, the data priority is directly determined, avoiding unnecessary subsequent analysis and effectively improving processing efficiency. This judgment mechanism can quickly identify data with clear logical connections, reducing blind processing and making the data processing flow more efficient.

[0119] S2-2-2. Based on the preprocessed ZR device control data, obtain preprocessed ZR device control data with logical relationship and preprocessed ZR device control data without logical relationship as the first preprocessed ZR device control data and the second preprocessed ZR device control data, respectively.

[0120] When the preprocessed ZR device control data does not have an overall logical relationship, this step divides it into first preprocessed ZR device control data with a logical relationship and second preprocessed ZR device control data without a logical relationship. This classification method allows for different processing strategies to be applied to different types of data, avoiding the limitations of uniform processing and improving the targeting and accuracy of the processing.

[0121] S2-2-3. Using the first preprocessed ZR device control data, establish the logical relationship of the first preprocessed ZR device control data;

[0122] For the first preprocessed ZR device control data that has logical relationships, clarifying the logical connections between the data provides a basis for determining the subsequent processing order, enabling the data to be processed in an orderly manner according to its inherent logic, avoiding errors or inefficiencies caused by improper processing order, and improving the rationality and reliability of data processing.

[0123] S2-2-4. Using the second preprocessed ZR device control data, establish a parallel relationship between the second preprocessed ZR device control data.

[0124] For the second preprocessed ZR device control data that does not have a logical relationship, this step establishes its parallel relationship. This allows the data to be processed simultaneously, making full use of system resources, significantly improving data processing efficiency, and shortening the overall processing time.

[0125] S2-2-5. Based on the logical relationship of the first preprocessing ZR device control data and the parallel relationship of the second preprocessing ZR device control data, obtain the timing relationship of the preprocessing ZR device control data as the priority of the preprocessing ZR device control data.

[0126] This step determines the timing relationship of the preprocessed ZR device control data based on the established logical relationship between the first preprocessed ZR device control data and the parallel relationship between the second preprocessed ZR device control data, thereby forming data priorities. This timing determination method, which comprehensively considers both logical and parallel relationships, fully optimizes the data processing order, ensuring both the orderly processing of logically related data and the efficient parallel processing of data without logical relationships.

[0127] S2-2-6. Mark the preprocessed ZR equipment control data according to the priority of the preprocessed ZR equipment control data, and obtain the marked ZR equipment control data;

[0128] This step involves marking the data using the established priorities of the pre-processed ZR equipment control data. This marking process visualizes the data's priority information, facilitating rapid identification and application of priorities in subsequent processing stages. It reduces the complexity of information transmission and processing, thereby improving processing efficiency.

[0129] S2-2-7. Based on the marked ZR device control data, establish a priority mechanism among the ZR device control data;

[0130] This step establishes a priority mechanism based on the marked ZR device control data. This mechanism translates data priorities into executable processing rules, ensuring that subsequent data processing strictly follows the priority order. This achieves standardization and orderliness in data processing, improving the stability and reliability of the overall processing flow.

[0131] In summary, steps S2-2-1 to S2-2-7, through logical relationship judgment, data classification, relationship establishment, timing determination, data labeling, and mechanism construction, form a complete system for determining the priority of ZR device control data. This system first determines the logical relationships between data, classifies data without an overall logical relationship, establishes logical and parallel relationships separately, determines the timing to form priorities, and then transforms the priorities into executable rules through data labeling and mechanism construction. This process achieves scientific planning of the data processing order, ensuring the orderly processing of logically related data while improving the efficiency of parallel processing of data without logical relationships. It significantly optimizes the overall processing flow, reduces processing complexity, and improves system response speed and the reliability of processing results.

[0132] As one possible implementation, in the above embodiments, step S2-3 may specifically include the following steps:

[0133] S2-3-1. Filter the preprocessed ZR device control data using the priority mechanism among the ZR device control data to obtain the filtered ZR device control data;

[0134] This step filters the preprocessed ZR equipment control data based on the established priority mechanism. The priority mechanism guides the filtering, which can quickly focus on key data, eliminate redundant information, effectively improve the relevance and efficiency of data processing, and lay the foundation for subsequent feature extraction.

[0135] S2-3-2. Using the filtered ZR device control data, obtain the first feature, second feature and third feature of the ZR device control data as the basic control data features of the ZR device.

[0136] The first, second, and third features were extracted from the selected ZR equipment control data as basic control data features. These features were defined based on the specific functions, control requirements, and data characteristics of the ZR equipment, and respectively represented the equipment's operating parameters (such as speed and pressure), status indicators (such as temperature and energy consumption), and interactive signals (such as command response and fault feedback). This multi-dimensional feature extraction comprehensively captured the key information of the equipment control data, covering different levels of equipment operation, providing rich data support for accurately reflecting the equipment status, and enhancing the representativeness of the features.

[0137] S2-3-3. Based on the basic control data characteristics of the ZR equipment, obtain the historical ZR equipment control data that corresponds to the selected ZR equipment control data from the historical ZR equipment control data as the historical ZR equipment control data characteristics.

[0138] Based on the filtered data, the corresponding historical ZR equipment control data is obtained as historical features. The introduction of historical features provides a reference benchmark for current features. With the accumulation of experience from historical data, it is helpful to more accurately judge the rationality and effectiveness of current features and improve the depth of feature analysis.

[0139] S2-3-4. Determine whether the first feature of the ZR device control data corresponds to the feature of the historical ZR device control data. If yes, execute S2-3-5. Otherwise, update the basic control data of the ZR device using the first feature of the ZR device control data and return to S2-3-3.

[0140] The system determines whether the first feature corresponds to historical features. If they do not correspond, the base data is updated with the first feature, and historical features are re-acquired. This dynamic update mechanism ensures the compatibility between the base data and the current features, avoids misjudgments of features due to outdated historical data, and maintains the timeliness and accuracy of feature analysis.

[0141] S2-3-5. Determine whether the second feature of the ZR device control data corresponds to the feature of the historical ZR device control data. If yes, execute S2-3-6; otherwise, update the basic control data of the ZR device using the second feature of the ZR device control data and return to S2-3-3.

[0142] The second feature is compared with historical features for correspondence. If they do not correspond, the base data is updated and the historical features are reacquired. This step continues the dynamic adjustment approach, further ensuring the matching degree between the second feature and historical data. By continuously optimizing the base data, the reliability of feature verification is improved.

[0143] S2-3-6. Determine whether the third feature of the ZR device control data corresponds to the historical ZR device control data feature. If yes, obtain the first, second, and third features of the ZR device control data as the ZR device control data feature. Otherwise, update the ZR device basic control data using the third feature of the ZR device control data and return to S2-3-3.

[0144] The correspondence between the third feature and historical features is verified. Only when all three correspond is it determined as the final ZR device control data feature; otherwise, the basic data is updated and the verification is repeated. This rigorous multi-round verification process, through step-by-step verification and dynamic updates, ensures the consistency between the extracted features and historical data, significantly improving the accuracy and stability of the features and providing high-quality feature data for subsequent processing.

[0145] In summary, steps S2-3-1 to S2-3-6 filter data based on a priority mechanism, extract multi-dimensional basic features, and introduce historical features as a reference. By checking the correspondence between the first, second, and third features and historical features one by one, the basic data is dynamically updated and repeatedly verified until all three correspond, at which point the final features are determined. This process improves the targeting of data processing through priority filtering, ensures the comprehensiveness of information through multi-dimensional feature extraction, enhances the depth of analysis through historical feature reference, and ensures the accuracy and timeliness of features through dynamic updates and multiple rounds of verification. Overall, it significantly improves the reliability and effectiveness of features, providing high-quality data support for precise equipment control.

[0146] As one possible implementation, in the above embodiments, step S3 may specifically include the following steps:

[0147] S3-1. Using the ZR device to control data features, obtain the first training feature and the second training feature as the training set for the multi-level data processing model;

[0148] Based on the finalized ZR equipment control data characteristics, feature vectors are constructed, transforming multidimensional features into a quantifiable mathematical expression. This feature vector construction provides standardized input for subsequent data analysis and processing, facilitating efficient identification and processing by computer systems and improving data operability and analytical efficiency.

[0149] S3-2. Input the first training feature and the second training feature into the data multi-level processing model to obtain the first ZR device control data processing result and the second ZR device control data processing result, respectively.

[0150] Standardizing the eigenvectors eliminates the influence of differences in units and numerical ranges among different features. Standardization ensures that each feature has equal weight in data analysis, avoiding analytical biases caused by different units, enhancing the model's adaptability to different features, and improving the accuracy and reliability of subsequent analysis results.

[0151] S3-3. Determine whether the control data processing result of the first ZR device is consistent with the control data processing result of the second ZR device. If yes, obtain the data multi-level processing model as the trained multi-level processing model and execute S3-4. Otherwise, update the training set of the data multi-level processing model according to the control data processing result of the first ZR device and the control data processing result of the second ZR device, and return to S3-2.

[0152] By inputting the standardized feature vectors into a preset feature prediction model, and leveraging the learning and prediction capabilities of the preset model, potential patterns and trends in the data can be discovered, changes in equipment control data can be predicted in advance, providing a forward-looking basis for equipment control decisions and improving the system's response speed and intelligence level.

[0153] S3-4. Input the ZR device control data features into the trained multi-level processing model to obtain the initial processing results of the ZR device control data.

[0154] By directly applying the prediction results to equipment control, dynamic and precise regulation of ZR equipment is achieved, enabling the equipment operating status to be adjusted in a timely manner according to the predicted trend. This effectively optimizes equipment performance, reduces energy consumption and failure rate, and improves equipment operating efficiency and stability.

[0155] In summary, steps S3-1 to S3-4 achieve standardized data representation through feature vector transformation. Standardization avoids interference between features and improves the consistency of model input. The application of the predictive model enables forward-looking judgment of equipment status. Control based on predictive features can dynamically adapt to equipment needs, significantly improving the accuracy and timeliness of control, optimizing equipment operating efficiency, and reducing resource consumption and failure risks.

[0156] As one possible implementation, in the above embodiments, step S4 may specifically include the following steps:

[0157] S4-1. Based on the ZR device control data to be processed, obtain the historical ZR device control result data corresponding to the ZR device control data to be processed from the historical ZR device control data;

[0158] This step retrieves historical control result data corresponding to the data to be processed from historical ZR device control data, providing a foundation for building a historical dataset. By associating current and historical data, historical experience can be fully utilized to provide a reference for subsequent verification processing.

[0159] S4-2. Divide the historical ZR equipment control result data and obtain the first historical ZR equipment control result dataset and the second historical ZR equipment control result dataset as the historical ZR equipment primary control result dataset.

[0160] This step divides the historical control result data into a first historical ZR equipment control result dataset and a second historical ZR equipment control result dataset. This division helps to verify and compare the datasets in the future, ensuring the integrity and consistency of the datasets.

[0161] S4-3. Determine whether the first historical ZR device control result dataset corresponds to the second historical ZR device control result dataset. If yes, obtain the first historical ZR device control result dataset and the second historical ZR device control result dataset as the historical ZR device control result dataset. Otherwise, update the historical ZR device control result data according to the first historical ZR device control result dataset and the second historical ZR device control result dataset, and return to S4-2.

[0162] This step verifies the correspondence between the two historical datasets to ensure data accuracy and consistency. If they correspond, the dataset is used as the final historical ZR device control result dataset; otherwise, the historical data is updated and reconstructed. This verification mechanism guarantees the quality of the historical datasets and provides a reliable reference for subsequent verification processes.

[0163] In summary, steps S4-1 to S4-3 ensure the relevance and relevance of the dataset by accurately matching historical result data. The construction of the dual validation set and the corresponding judgment can effectively identify data deviations, while the dynamic update mechanism further guarantees the accuracy and applicability of the historical dataset, providing a reliable basis for subsequent analysis or decision-making based on historical data, and improving the stability of the relevant processing process and the credibility of the results.

[0164] As one possible implementation, in the above embodiments, step S5 may specifically include the following steps:

[0165] S5-1. Determine whether the primary processing result of the ZR equipment control data matches the historical ZR equipment control result dataset. If yes, obtain the primary processing result of the ZR equipment control data as the multi-level data processing result. Otherwise, execute S5-2.

[0166] This step compares the initial processing results of the ZR equipment control data with historical ZR equipment control result datasets to determine if they conform to historical experience. If they do, they are used as the final multi-level processing result; otherwise, further analysis and processing are performed. This verification process utilizes the reference value of historical data to ensure the rationality and reliability of the processing results.

[0167] S5-2. Determine whether the historical ZR device control result dataset meets the control data compliance standard. If yes, update the transmitter control data and receiver control data of the ZR device according to the primary processing result of the ZR device control data, and return to S1-2. Otherwise, return to S4-1.

[0168] If the initial processing result does not match the historical dataset, this step further verifies whether the historical dataset meets the control data compliance standards. If it does, the control data at the transmitting and receiving ends is updated and re-verified; otherwise, the historical dataset is reconstructed. This mechanism forms a closed-loop feedback system that can promptly identify and correct problems, continuously optimize processing results and historical datasets, and improve the system's adaptability and processing capabilities.

[0169] In summary, steps S5-1 to S5-2 ensure the reliability of the initial processing results through multi-level judgment and dynamic adjustment. When the results are inconsistent, the deviation can be corrected in a timely manner with the help of compliance standard verification and data update mechanism, ensuring the consistency of control data between the transmitting end and the receiving end. At the same time, the adaptability of the data is enhanced by re-acquiring historical data, thus improving the accuracy, compliance and stability of ZR equipment control data processing as a whole.

[0170] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0171] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0172] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0173] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-level data processing method for ZR equipment, characterized in that, include: S1. Use the initial control data of the ZR device to obtain the control data of the ZR device to be processed; S2. Based on the control data of the ZR device to be processed, a priority mechanism is used to perform first-level processing to obtain the characteristics of the ZR device control data. S3. Perform secondary processing on the ZR device control data features to obtain the initial processing result of the ZR device control data; S4. Based on the ZR device control data to be processed, construct a historical ZR device control result dataset; S5. Based on the initial processing results of the ZR device control data, the historical ZR device control result dataset is used for verification processing to obtain multi-level data processing results.

2. The multi-level data processing method for ZR equipment according to claim 1, characterized in that, S1. Using the initial control data of the ZR device, obtain the control data of the ZR device to be processed, including: Acquire the transmitter control data and receiver control data of the ZR device; Determine whether the transmitting end control data and the receiving end control data are consistent. If they are, obtain the transmitting end control data and the receiving end control data as the initial control data of the ZR device, and use the ZR device to build a control data compliance standard and execute the first operation. Otherwise, return to execute the second operation. The first operation is as follows: based on the initial control data of the ZR device and the control data compliance standard, obtain the control data of the ZR device to be processed; The second operation is: to acquire the transmitter control data and receiver control data of the ZR device; The control data compliance standard is the ZR device standard control data corresponding to the initial control data of the ZR device.

3. The multi-level data processing method for ZR equipment according to claim 2, characterized in that, Based on the initial control data of the ZR device and the control data compliance standard, obtain the control data of the ZR device to be processed, including: Obtain the current time as the initial time T for the initial control data of the ZR device; Based on the initial time T of the ZR device initial control data, obtain the ZR device initial control data at initial time T, time T+1, and time T+2. Determine whether the initial control data of the ZR device at the initial time T, T+1 and T+2 all meet the control data compliance standard. If so, obtain the initial control data of the ZR device at the initial time T, T+1 and T+2 as the control data of the ZR device to be processed. Otherwise, perform the third operation. The third operation is as follows: determine whether the initial control data of the ZR device at the initial time T, time T+1 and time T+2 do not meet the control data compliance standard. If so, obtain time nT based on the initial time T and perform the fourth operation. Otherwise, obtain the initial control data of the ZR device that meets the control data compliance standard as the control data to be processed. The fourth operation is: using the nT time, obtain the initial control data of the ZR device at nT time, nT+1 time, and nT+2 time, and then perform the fifth operation; The fifth operation is to determine whether the initial control data of the ZR device at time nT, time nT+1, and time nT+2 all meet the control data compliance standard. If so, the initial control data of the ZR device at time nT, time nT+1, and time nT+2 are obtained as the control data of the ZR device to be processed. Otherwise, the sixth operation is performed. The sixth operation is to determine whether the initial control data of the ZR device at time nT, time nT+1, and time nT+2 do not meet the control data compliance standard. If so, then re-acquire time nT based on the initial time T and return to execute the fourth operation. Otherwise, acquire the initial control data of the ZR device that meets the control data compliance standard as the control data to be processed. Wherein, the nT times are all integer multiples of the initial time T.

4. A multi-level data processing method for ZR equipment according to claim 2, characterized in that, S2. Based on the ZR device control data to be processed, perform first-level processing using a priority mechanism to obtain ZR device control data characteristics, including: The control data of the ZR equipment to be processed is sequentially cleaned and filtered to obtain preprocessed ZR equipment control data; Based on the preprocessed ZR device control data, a priority mechanism is constructed between the ZR device control data. The priority mechanism between the ZR device control data is a mechanism that executes sequentially according to the corresponding timing relationship of the ZR device control data. The ZR device control data is processed first-level using a priority mechanism among the ZR device control data to obtain ZR device control data features.

5. A multi-level data processing method for ZR equipment according to claim 4, characterized in that, Based on the preprocessed ZR device control data, a priority mechanism is constructed among the ZR device control data, including: Determine whether there is a logical relationship between the preprocessed ZR device control data. If so, obtain the priority of the preprocessed ZR device control data according to the logical relationship between the preprocessed ZR device control data and execute the seventh operation; otherwise, execute the eighth operation. The seventh operation is as follows: mark the preprocessed ZR device control data according to the priority of the preprocessed ZR device control data, obtain the marked ZR device control data, and perform the ninth operation. The eighth operation is as follows: based on the preprocessed ZR device control data, obtain preprocessed ZR device control data with logical relationship and preprocessed ZR device control data without logical relationship as the first preprocessed ZR device control data and the second preprocessed ZR device control data, respectively, and execute the tenth operation. The ninth operation is: establishing a priority mechanism among ZR device control data based on the marked ZR device control data; The tenth operation is: using the first preprocessed ZR device control data, establishing the logical relationship of the first preprocessed ZR device control data, and executing the eleventh operation; The eleventh operation is: using the second preprocessed ZR device control data, establishing a parallel relationship for the second preprocessed ZR device control data, and then executing the twelfth operation; The twelfth operation is as follows: based on the logical relationship of the first preprocessed ZR device control data and the parallel relationship of the second preprocessed ZR device control data, the timing relationship of the preprocessed ZR device control data is obtained as the priority of the preprocessed ZR device control data, and the seventh operation is executed.

6. A multi-level data processing method for ZR equipment according to claim 4, characterized in that, The preprocessed ZR device control data is processed using a priority mechanism among the ZR device control data to obtain ZR device control data characteristics, including: The preprocessed ZR device control data is filtered using a priority mechanism among the ZR device control data to obtain the filtered ZR device control data; Using the filtered ZR device control data, the first feature, the second feature, and the third feature of the ZR device control data are obtained as the basic control data features of the ZR device. Based on the basic control data characteristics of the ZR equipment, the historical ZR equipment control data corresponding to the filtered ZR equipment control data is obtained from the historical ZR equipment control data as the historical ZR equipment control data characteristics. Determine whether the first feature of the ZR device control data corresponds to the feature of the historical ZR device control data. If yes, execute the thirteenth operation; otherwise, update the basic control data of the ZR device using the first feature of the ZR device control data and return to the fourteenth operation. The thirteenth operation is to determine whether the second feature of the ZR device control data corresponds to the feature of the historical ZR device control data. If yes, the fifteenth operation is performed; otherwise, the basic control data of the ZR device is updated using the second feature of the ZR device control data, and the fourteenth operation is performed. The fourteenth operation is: based on the ZR equipment basic control data characteristics, obtain the historical ZR equipment control data corresponding to the filtered ZR equipment control data from the historical ZR equipment control data as the historical ZR equipment control data characteristics; The fifteenth operation is as follows: determine whether the third feature of the ZR device control data corresponds to the historical ZR device control data feature. If yes, obtain the first, second, and third features of the ZR device control data as the ZR device control data feature. Otherwise, update the ZR device basic control data using the third feature of the ZR device control data and execute the fourteenth operation.

7. A multi-level data processing method for ZR equipment according to claim 1, characterized in that, S3. Perform secondary processing on the ZR device control data features to obtain the initial processing results of the ZR device control data, including: Using the ZR device to control data features, first training features and second training features are obtained as the training set for the multi-level data processing model; The first training feature and the second training feature are input into the data multi-level processing model to obtain the first ZR device control data processing result and the second ZR device control data processing result, respectively. Determine whether the control data processing result of the first ZR device is consistent with the control data processing result of the second ZR device. If so, obtain the data multi-level processing model as the trained multi-level processing model and execute the sixteenth operation. Otherwise, update the training set of the data multi-level processing model according to the control data processing result of the first ZR device and the control data processing result of the second ZR device and execute the seventeenth operation. The sixteenth operation is to input the ZR device control data features into the trained multi-level processing model to obtain the initial processing results of the ZR device control data. The seventeenth operation is as follows: input the first training feature and the second training feature into the data multi-level processing model to obtain the first ZR device control data processing result and the second ZR device control data processing result, respectively.

8. A multi-level data processing method for ZR equipment according to claim 6, characterized in that, S4. Based on the ZR device control data to be processed, construct a historical ZR device control result dataset, including: Based on the ZR device control data to be processed, obtain the historical ZR device control result data corresponding to the ZR device control data to be processed from the historical ZR device control data. The historical ZR equipment control result data is divided to obtain a first historical ZR equipment control result dataset and a second historical ZR equipment control result dataset as the primary control result dataset of the historical ZR equipment. Determine whether the first historical ZR device control result dataset corresponds to the second historical ZR device control result dataset. If yes, obtain the first historical ZR device control result dataset and the second historical ZR device control result dataset as the historical ZR device control result dataset. Otherwise, update the historical ZR device control result data according to the first historical ZR device control result dataset and the second historical ZR device control result dataset, and perform the eighteenth operation. The eighteenth operation is as follows: the historical ZR equipment control result data is divided to obtain a first historical ZR equipment control result dataset and a second historical ZR equipment control result dataset as the primary control result dataset of the historical ZR equipment.

9. A multi-level data processing method for ZR equipment according to claim 8, characterized in that, S5. The initial processing results based on the ZR equipment control data are verified using the historical ZR equipment control result dataset to obtain multi-level data processing results, including: Determine whether the primary processing result of the ZR device control data matches the historical ZR device control result dataset. If yes, obtain the primary processing result of the ZR device control data as the multi-level data processing result; otherwise, execute the nineteenth operation. The nineteenth operation is as follows: determine whether the historical ZR device control result dataset meets the control data compliance standard. If it does, update the transmitter control data and receiver control data of the ZR device according to the primary processing result of the ZR device control data, and perform the twentieth operation. Otherwise, perform the twenty-first operation. The twentieth operation is: determining whether the transmitting end control data and the receiving end control data are consistent; The 21st operation is as follows: Based on the ZR device control data to be processed, obtain the historical ZR device control result data corresponding to the ZR device control data to be processed from the historical ZR device control data.

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