A collaborative verification and processing method for multiple types of data in a classified recycling system

Abstract

The present invention relates to the field of comprehensive verification processing of multiple types of upstream and downstream data of a classification recycling system, and in particular to a method for collaborative verification processing of multiple types of data for a classification recycling system, comprising: respectively obtaining cloud database characteristics and real-time operation data of the classification recycling system; establishing a system data chain of the classification recycling system according to the cloud database characteristics of the classification recycling system; obtaining the periodic operation fluctuation characteristics of the classification recycling system according to the real-time operation data of the classification recycling system; performing cross-validation processing on the system data chain and the periodic operation fluctuation characteristics of the classification recycling system to obtain a result of collaborative verification processing of multiple types of data, separating and independently considering the data streams uploaded and downloaded in the classification recycling system, verifying the dual verification of the system and the data, discovering the essential cause of the data fluctuation, and enhancing the generalization ability of the solution for systematic problems and the accuracy of the result performance.

Description

Technical Field

[0001] The present invention relates to the field of comprehensive verification and processing of multiple types of uplink and downlink data of a classification recycling system, and in particular to a collaborative verification and processing method of multiple types of data for a classification recycling system. Background Art

[0002] As a comprehensive management application platform for waste classification and recycling, the classified recycling system has a good implementation and application effect on the management and monitoring of daily recycling and the overall summary of performance results. After connecting to the subordinate source data for platformization, cloudization and intelligent processing, the overall operational efficiency is improved. However, in the process of upstream and downstream data transmission, the traditional solution has only ordinary relatively independent verification solutions for data transmission and data values. In the case of large amounts of data and multiple types of data, there is a lack of effective combination of the verification process with the data itself. For large-scale data processing platform systems, the possibility of errors will increase significantly, resulting in deviations in the final system display results. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides a method for collaborative verification and processing of multiple types of data for a classified recycling system. By separately verifying and processing the upstream and downstream data flows of the cloud system, timely discovery and tracking of abnormal problems of various types of data under different circumstances can be achieved.

[0004] To achieve the above object, the present invention provides a multi-category data collaborative verification processing method for a classified recycling system, comprising:

[0005] S1, respectively obtaining the cloud database characteristics and real-time operation data of the classification and recycling system;

[0006] S2. Establishing a system data chain of the classified recycling system according to the cloud database characteristics of the classified recycling system;

[0007] S3, obtaining the phased operation fluctuation characteristics of the classification and recycling system according to the real-time operation data of the classification and recycling system;

[0008] S4. A cross-validation process is performed based on the system data chain of the classification recycling system and the phased operation fluctuation characteristics to obtain a collaborative validation process result of multiple types of data.

[0009] Preferably, the steps of respectively obtaining the cloud database characteristics and real-time operation data of the classified recycling system include:

[0010] S1-1. Obtain real-time operation data of the classification and recycling system;

[0011] S1-2, using the corresponding time of the real-time operation data of the classification recycling system as the standard time t;

[0012] S1-3, using the classification recycling system to obtain historical data from the cloud database according to the standard time t;

[0013] S1-4. Obtain cloud database characteristics based on historical data of the cloud database.

[0014] Furthermore, obtaining cloud database characteristics according to the historical data of the cloud database includes:

[0015] S1-4-1. Obtain the data type, data capacity and data generation time corresponding to the historical data of the cloud database respectively;

[0016] S1-4-2, using the data type, data capacity and data generation time corresponding to the historical data as addressing features, proofreading features and review features respectively;

[0017] S1-4-3, determine whether the data occurrence time corresponding to the historical data is continuous, if so, use the addressing feature, proofreading feature and review feature as cloud database features, otherwise, obtain the discontinuous data occurrence time as the breakpoint time label, and execute S1-4-4;

[0018] S1-4-4, obtaining historical data of the cloud database corresponding to the adjacent moments according to the breakpoint moment label as the breakpoint addressing data threshold;

[0019] S1-4-5. Determine whether there is corresponding historical addressing data between the cloud database and the breakpoint addressing data threshold. If so, obtain the data type, data capacity and data generation time of the intermediate data corresponding to the historical addressing data as addressing features, proofreading features and review features, respectively, and return to S1-4-3. Otherwise, use the relative preceding time from the standard time t to the breakpoint addressing data threshold as the new time period, and return to S1-3.

[0020] Furthermore, establishing a system data chain of the classified recycling system according to the cloud database characteristics of the classified recycling system includes:

[0021] S2-1, obtaining the uplink data stream of the classified recycling system according to the cloud database characteristics of the classified recycling system;

[0022] S2-2, obtaining the downlink data flow of the classified recycling system according to the cloud database characteristics of the classified recycling system;

[0023] S2-3. Utilize the uplink data stream and downlink data stream of the classification and recycling system as the system data link of the classification and recycling system.

[0024] Further, obtaining the uplink data flow of the classified recycling system according to the cloud database characteristics of the classified recycling system includes:

[0025] S2-2-1. Obtain the uplink start transmission time t corresponding to the uplink data stream of the classification recycling system;

[0026] S2-2-2, obtaining the data type, data capacity and data generation time corresponding to the real-time operation data at the uplink start transmission time t as the uplink data addressing label, uplink data proofreading label and uplink data review label respectively;

[0027] S2-2-3, using the uplink data addressing tag, uplink data proofreading tag and uplink data review tag of the uplink start transmission time t to obtain corresponding historical data as the uplink data standard according to the cloud database characteristics of the classification recycling system;

[0028] S2-2-4, obtaining the real-time operation data of the next moment adjacent to the uplink start transmission moment t;

[0029] S2-2-5, determine whether the data type corresponding to the real-time operation data at the next moment adjacent to the uplink start transmission time t is the same as the uplink data addressing label corresponding to the real-time operation data at the uplink start transmission time t, if so, execute S2-2-6. Otherwise, directly execute S2-2-7;

[0030] S2-2-6, determine whether the data capacity corresponding to the real-time operation data at the next moment adjacent to the uplink start transmission moment t, the uplink data proofreading label corresponding to the real-time operation data at the data generation moment and the uplink start transmission moment t, and the uplink data review label are all corresponding. If so, update the current moment and return to S2-2-4. Otherwise, keep the previous moment adjacent to the current moment as the uplink end moment and execute S2-2-7;

[0031] S2-2-7. Use the real-time operation data from the uplink start transmission time t to the uplink end time as the uplink data flow of the classification recovery system.

[0032] Further, obtaining the downlink data flow of the classified recycling system according to the cloud database characteristics of the classified recycling system includes:

[0033] S2-3-1. Obtain the downlink start transmission time k corresponding to the downlink data flow of the classification recycling system;

[0034] S2-3-2, obtaining the data type, data capacity and data generation time corresponding to the real-time operation data at the downlink start transmission time k as the downlink data addressing label, downlink data proofreading label and downlink data review label respectively;

[0035] S2-3-3, using the downlink data addressing tag, downlink data proofreading tag and downlink data review tag of the downlink start transmission time k to obtain corresponding historical data as the downlink data standard according to the cloud database characteristics of the classification recycling system;

[0036] S2-3-4, obtaining the real-time operation data of the next time point adjacent to the downlink start transmission time point k;

[0037] S2-3-5, determine whether the data type corresponding to the real-time operation data at the next moment adjacent to the downlink start transmission time k is the same as the downlink data addressing label corresponding to the real-time operation data at the downlink start transmission time k, if so, execute S2-3-6. Otherwise, directly execute S2-3-7;

[0038] S2-3-6, determine whether the data capacity corresponding to the real-time operation data at the next moment adjacent to the downlink start transmission moment k, the downlink data proofreading label corresponding to the real-time operation data at the data generation moment and the downlink start transmission moment k, and the downlink data review label are all corresponding. If so, update the current moment and return to S2-3-4. Otherwise, keep the previous moment adjacent to the current moment as the downlink end moment and execute S2-3-7;

[0039] S2-3-7, using the real-time operation data from the downlink start transmission time k to the downlink end time as the downlink initial data flow of the classification recovery system;

[0040] S2-2-8. Determine whether the downlink data flow of the classification recycling system corresponds to the uplink data flow of the classification recycling system. If so, use the downlink initial data flow as the downlink data flow of the classification recycling system. Otherwise, update the downlink starting transmission time k according to the uplink starting transmission time t, and return to S2-3-1.

[0041] Furthermore, obtaining the phased operation fluctuation characteristics of the classified recycling system according to the real-time operation data of the classified recycling system includes:

[0042] S3-1, using the real-time operation data of the classification and recycling system to obtain the deviation point of the real-time operation data of the classification and recycling system;

[0043] S3-2. Obtaining the phased operation fluctuation characteristics of the classification and recycling system according to the deviation points of the real-time operation data of the classification and recycling system.

[0044] Further, using the real-time operation data of the classification and recycling system to obtain the deviation point of the real-time operation data of the classification and recycling system includes:

[0045] S3-1-1, respectively obtaining the historical uplink data flow and the historical downlink data flow according to the data type and data capacity corresponding to the real-time operation data of the classification recycling system;

[0046] S3-1-2, using each data value of the historical uplink data stream to establish a historical uplink data value sequence;

[0047] S3-1-3, using each data value of the historical downstream data stream to establish a historical downstream data value sequence;

[0048] S3-1-4, determine whether the historical uplink data value sequence and the historical downlink data value sequence are both corresponding, if so, directly execute S3-1-6, otherwise, use the sequence position of the non-corresponding data value as the historical running data deviation point;

[0049] S3-1-5, determine whether the historical operation data deviation point and the corresponding upstream data stream and downstream data stream of the real-time operation data of the classified recycling system have corresponding sequence positions, if so, use the current sequence position as the real-time operation data deviation point of the classified recycling system, otherwise, the real-time operation data deviation point of the classified recycling system is empty;

[0050] S3-1-6. Determine whether the historical upstream data value sequence and the real-time operating data of the classification recycling system correspond to the upstream data flow, and the historical downstream data value sequence and the real-time operating data of the classification recycling system correspond to the downstream data flow. If so, the deviation point of the real-time operating data of the classification recycling system is empty. Otherwise, the sequence position of the uncorresponding data value is used as the deviation point of the real-time operating data of the classification recycling system.

[0051] Furthermore, obtaining the phased operation fluctuation characteristics of the classified recycling system according to the deviation point of the real-time operation data of the classified recycling system includes:

[0052] S3-2-1, judging whether there is a deviation point of the real-time operation data of the classification and recycling system at the current moment, if so, executing S3-2-2, otherwise, the staged operation fluctuation characteristic of the classification and recycling system is stable;

[0053] S3-2-2, using the real-time operation data deviation point as a sampling base point to respectively obtain adjacent preceding and succeeding data as pre-fluctuation sampling data and post-fluctuation sampling data;

[0054] S3-2-3, obtaining the data change rate of the post-fluctuation sampled data relative to the pre-fluctuation sampled data as a normal fluctuation threshold;

[0055] S3-2-4, obtaining the data change rate of the deviation point of the real-time operation data relative to the sampled data before the fluctuation as the fluctuation starting threshold;

[0056] S3-2-5, obtaining the data change rate of the post-fluctuation sampling data relative to the deviation point of the real-time operation data as the fluctuation downward threshold;

[0057] S3-2-6. Use the normal fluctuation threshold, fluctuation starting threshold and fluctuation falling threshold as the staged operation fluctuation characteristics of the classification and recycling system.

[0058] Furthermore, the cross-validation processing is performed based on the system data chain of the classification recycling system and the phased operation fluctuation characteristics to obtain the collaborative validation processing results of multiple types of data, including:

[0059] S4-1, judging whether the system data chain of the classified recycling system corresponds to the phased operation fluctuation characteristics, if so, executing S4-2, otherwise, the result of the multi-category data collaborative verification processing is normal;

[0060] S4-2, determine whether the number of occurrences of the phased operation fluctuation characteristics of the system data chain of the classified recycling system is 1, if so, there is a transmission anomaly in the collaborative verification processing result of the multi-category data, and output the data position of the deviation point of the real-time operation data corresponding to the phased operation fluctuation characteristics in the system data chain of the classified recycling system, otherwise, execute S4-3;

[0061] S4-3, determine whether the staged operation fluctuation characteristics are adjacent to the corresponding positions of the system data chain of the same classification and recycling system. If so, there is a system abnormality in the collaborative verification processing results of the multi-category data, and output the data position of the deviation point of the real-time operation data corresponding to the staged operation fluctuation characteristics in the system data chain of the classification and recycling system. Otherwise, obtain the abnormal floating difference between the fluctuation starting threshold and the fluctuation falling threshold corresponding to each staged operation fluctuation characteristic, and execute S4-4;

[0062] S4-4. Determine whether the abnormal floating differences corresponding to the various stage-by-stage operation fluctuation characteristics are all smaller than the normal fluctuation threshold. If so, there is a transmission anomaly in the collaborative verification processing results of the multi-category data, and the data position of the deviation point of the real-time operation data corresponding to the stage-by-stage operation fluctuation characteristics in the system data chain of the classification and recycling system is output. Otherwise, there is a numerical anomaly in the collaborative verification processing results of the multi-category data, and the abnormal data value of the real-time operation data is output.

[0063] Compared with the closest prior art, the present invention has the following beneficial effects:

[0064] The data streams uploaded and downloaded in the classified recycling system are considered separately and independently, and the data values ​​and the relevant characteristics of the data themselves are extracted during the transmission process, which ensures the dual verification of the system and the data, and significantly improves the high correlation after the implementation and deployment of the solution. At the same time, the concept of threshold difference verification is innovatively proposed within the solution. Under the premise of the traditional data change rate, the difference between the before and after change rates is considered again, and the concept of linear regression is simplified to discover the essential reasons for data fluctuations, thereby enhancing the solution's generalization ability for systematic problems and the accuracy of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of a method for collaborative verification and processing of multiple types of data for a classified recycling system provided by the present invention. DETAILED DESCRIPTION

[0066] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0067] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Embodiment 1:

[0069] The present invention provides a method for collaborative verification and processing of multiple types of data for a classified recycling system, such as Figure 1 As shown, including:

[0070] S1, respectively obtaining the cloud database characteristics and real-time operation data of the classification and recycling system;

[0071] S2. Establishing a system data chain of the classified recycling system according to the cloud database characteristics of the classified recycling system;

[0072] S3, obtaining the phased operation fluctuation characteristics of the classification and recycling system according to the real-time operation data of the classification and recycling system;

[0073] S4. A cross-validation process is performed based on the system data chain of the classification recycling system and the phased operation fluctuation characteristics to obtain a collaborative validation process result of multiple types of data.

[0074] S1 specifically includes:

[0075] S1-1. Obtain real-time operation data of the classification and recycling system;

[0076] S1-2, using the corresponding time of the real-time operation data of the classification recycling system as the standard time t;

[0077] S1-3, using the classification recycling system to obtain historical data from the cloud database according to the standard time t;

[0078] S1-4. Obtain cloud database characteristics based on historical data of the cloud database.

[0079] S1-4 specifically includes:

[0080] S1-4-1. Obtain the data type, data capacity and data generation time corresponding to the historical data of the cloud database respectively;

[0081] S1-4-2, using the data type, data capacity and data generation time corresponding to the historical data as addressing features, proofreading features and review features respectively;

[0082] S1-4-3, determine whether the data occurrence time corresponding to the historical data is continuous, if so, use the addressing feature, proofreading feature and review feature as cloud database features, otherwise, obtain the discontinuous data occurrence time as the breakpoint time label, and execute S1-4-4;

[0083] S1-4-4, obtaining historical data of the cloud database corresponding to the adjacent moments according to the breakpoint moment label as the breakpoint addressing data threshold;

[0084] S1-4-5. Determine whether there is corresponding historical addressing data between the cloud database and the breakpoint addressing data threshold. If so, obtain the data type, data capacity and data generation time of the intermediate data corresponding to the historical addressing data as addressing features, proofreading features and review features, respectively, and return to S1-4-3. Otherwise, use the relative preceding time from the standard time t to the breakpoint addressing data threshold as the new time period, and return to S1-3.

[0085] In this embodiment, a method for collaborative verification and processing of multiple types of data for a classified recycling system is provided, wherein the addressing data refers to an identifier used to locate and reference a specific cell, cell range, or data set in a spreadsheet or other data storage system.

[0086] S2 specifically includes:

[0087] S2-1, obtaining the uplink data stream of the classified recycling system according to the cloud database characteristics of the classified recycling system;

[0088] S2-2, obtaining the downlink data flow of the classified recycling system according to the cloud database characteristics of the classified recycling system;

[0089] S2-3. Utilize the uplink data stream and downlink data stream of the classification and recycling system as the system data link of the classification and recycling system.

[0090] S2-2 specifically includes:

[0091] S2-2-1. Obtain the uplink start transmission time t corresponding to the uplink data stream of the classification recycling system;

[0092] S2-2-2, obtaining the data type, data capacity and data generation time corresponding to the real-time operation data at the uplink start transmission time t as the uplink data addressing label, uplink data proofreading label and uplink data review label respectively;

[0093] S2-2-3, using the uplink data addressing tag, uplink data proofreading tag and uplink data review tag of the uplink start transmission time t to obtain corresponding historical data as the uplink data standard according to the cloud database characteristics of the classification recycling system;

[0094] S2-2-4, obtaining the real-time operation data of the next moment adjacent to the uplink start transmission moment t;

[0095] S2-2-5, determine whether the data type corresponding to the real-time operation data at the next moment adjacent to the uplink start transmission time t is the same as the uplink data addressing label corresponding to the real-time operation data at the uplink start transmission time t, if so, execute S2-2-6. Otherwise, directly execute S2-2-7;

[0096] S2-2-6, determine whether the data capacity corresponding to the real-time operation data at the next moment adjacent to the uplink start transmission moment t, the uplink data proofreading label corresponding to the real-time operation data at the data generation moment and the uplink start transmission moment t, and the uplink data review label are all corresponding. If so, update the current moment and return to S2-2-4. Otherwise, keep the previous moment adjacent to the current moment as the uplink end moment and execute S2-2-7;

[0097] S2-2-7. Use the real-time operation data from the uplink start transmission time t to the uplink end time as the uplink data flow of the classification recovery system.

[0098] S2-3 specifically includes:

[0099] S2-3-1. Obtain the downlink start transmission time k corresponding to the downlink data flow of the classification recycling system;

[0100] S2-3-2, obtaining the data type, data capacity and data generation time corresponding to the real-time operation data at the downlink start transmission time k as the downlink data addressing label, downlink data proofreading label and downlink data review label respectively;

[0101] S2-3-3, using the downlink data addressing tag, downlink data proofreading tag and downlink data review tag of the downlink start transmission time k to obtain corresponding historical data as the downlink data standard according to the cloud database characteristics of the classification recycling system;

[0102] S2-3-4, obtaining the real-time operation data of the next time point adjacent to the downlink start transmission time point k;

[0103] S2-3-5, determine whether the data type corresponding to the real-time operation data at the next moment adjacent to the downlink start transmission time k is the same as the downlink data addressing label corresponding to the real-time operation data at the downlink start transmission time k, if so, execute S2-3-6. Otherwise, directly execute S2-3-7;

[0104] S2-3-6, determine whether the data capacity corresponding to the real-time operation data at the next moment adjacent to the downlink start transmission moment k, the downlink data proofreading label corresponding to the real-time operation data at the data generation moment and the downlink start transmission moment k, and the downlink data review label are all corresponding. If so, update the current moment and return to S2-3-4. Otherwise, keep the previous moment adjacent to the current moment as the downlink end moment and execute S2-3-7;

[0105] S2-3-7, using the real-time operation data from the downlink start transmission time k to the downlink end time as the downlink initial data flow of the classification recovery system;

[0106] S2-2-8. Determine whether the downlink data flow of the classification recycling system corresponds to the uplink data flow of the classification recycling system. If so, use the downlink initial data flow as the downlink data flow of the classification recycling system. Otherwise, update the downlink starting transmission time k according to the uplink starting transmission time t, and return to S2-3-1.

[0107] S3 specifically includes:

[0108] S3-1, using the real-time operation data of the classification and recycling system to obtain the deviation point of the real-time operation data of the classification and recycling system;

[0109] S3-2. Obtaining the phased operation fluctuation characteristics of the classification and recycling system according to the deviation points of the real-time operation data of the classification and recycling system.

[0110] S3-1 specifically includes:

[0111] S3-1-1, respectively obtaining the historical uplink data flow and the historical downlink data flow according to the data type and data capacity corresponding to the real-time operation data of the classification recycling system;

[0112] S3-1-2, using each data value of the historical uplink data stream to establish a historical uplink data value sequence;

[0113] S3-1-3, using each data value of the historical downstream data stream to establish a historical downstream data value sequence;

[0114] S3-1-4, determine whether the historical uplink data value sequence and the historical downlink data value sequence are both corresponding, if so, directly execute S3-1-6, otherwise, use the sequence position of the non-corresponding data value as the historical running data deviation point;

[0115] S3-1-5, determine whether the historical operation data deviation point and the corresponding upstream data stream and downstream data stream of the real-time operation data of the classified recycling system have corresponding sequence positions, if so, use the current sequence position as the real-time operation data deviation point of the classified recycling system, otherwise, the real-time operation data deviation point of the classified recycling system is empty;

[0116] S3-1-6. Determine whether the historical upstream data value sequence and the real-time operating data of the classification recycling system correspond to the upstream data flow, and the historical downstream data value sequence and the real-time operating data of the classification recycling system correspond to the downstream data flow. If so, the deviation point of the real-time operating data of the classification recycling system is empty. Otherwise, the sequence position of the uncorresponding data value is used as the deviation point of the real-time operating data of the classification recycling system.

[0117] S3-2 specifically includes:

[0118] S3-2-1, judging whether there is a deviation point of the real-time operation data of the classification and recycling system at the current moment, if so, executing S3-2-2, otherwise, the staged operation fluctuation characteristic of the classification and recycling system is stable;

[0119] S3-2-2, using the real-time operation data deviation point as a sampling base point to respectively obtain adjacent preceding and succeeding data as pre-fluctuation sampling data and post-fluctuation sampling data;

[0120] S3-2-3, obtaining the data change rate of the post-fluctuation sampled data relative to the pre-fluctuation sampled data as a normal fluctuation threshold;

[0121] S3-2-4, obtaining the data change rate of the deviation point of the real-time operation data relative to the sampled data before the fluctuation as the fluctuation starting threshold;

[0122] S3-2-5, obtaining the data change rate of the post-fluctuation sampling data relative to the deviation point of the real-time operation data as the fluctuation downward threshold;

[0123] S3-2-6. Use the normal fluctuation threshold, fluctuation starting threshold and fluctuation falling threshold as the staged operation fluctuation characteristics of the classification and recycling system.

[0124] S4 specifically includes:

[0125] S4-1, judging whether the system data chain of the classified recycling system corresponds to the phased operation fluctuation characteristics, if so, executing S4-2, otherwise, the result of the multi-category data collaborative verification processing is normal;

[0126] S4-2, determine whether the number of occurrences of the phased operation fluctuation characteristics of the system data chain of the classified recycling system is 1, if so, there is a transmission anomaly in the collaborative verification processing result of the multi-category data, and output the data position of the deviation point of the real-time operation data corresponding to the phased operation fluctuation characteristics in the system data chain of the classified recycling system, otherwise, execute S4-3;

[0127] S4-3, determine whether the staged operation fluctuation characteristics are adjacent to the corresponding positions of the system data chain of the same classification and recycling system. If so, there is a system abnormality in the collaborative verification processing results of the multi-category data, and output the data position of the deviation point of the real-time operation data corresponding to the staged operation fluctuation characteristics in the system data chain of the classification and recycling system. Otherwise, obtain the abnormal floating difference between the fluctuation starting threshold and the fluctuation falling threshold corresponding to each staged operation fluctuation characteristic, and execute S4-4;

[0128] S4-4. Determine whether the abnormal floating differences corresponding to the various stage-by-stage operation fluctuation characteristics are all smaller than the normal fluctuation threshold. If so, there is a transmission anomaly in the collaborative verification processing results of the multi-category data, and the data position of the deviation point of the real-time operation data corresponding to the stage-by-stage operation fluctuation characteristics in the system data chain of the classification and recycling system is output. Otherwise, there is a numerical anomaly in the collaborative verification processing results of the multi-category data, and the abnormal data value of the real-time operation data is output.

[0129] In this embodiment, a method for collaborative verification and processing of multiple types of data for a classified recycling system is provided. The transmission exception is directed to the transmission exception problem that occurs in the collaborative verification and processing results of multiple types of data. The specific situation in actual deployment and application is as follows:

[0130] 1. Verify whether the protocol used for data transmission complies with standard specifications;

[0131] 2. Check the stability of network connection and ensure the continuity of data transmission;

[0132] 3. Evaluate whether the size of the data packet is suitable for the constraints of the current transmission system;

[0133] 4. Confirm whether the encryption mechanism and integrity verification measures during data transmission are appropriate;

[0134] 5. Review the completeness of the exception handling mechanism and logging system;

[0135] 6. Analyze the rationality of transmission timeout parameters and retry strategies to ensure the reliability of data transmission;

[0136] The system exceptions mentioned above are system exceptions that occur during the joint verification process of multiple types of data. The specific situations in actual deployment applications are as follows:

[0137] 1. Conduct a detailed review of system logs to accurately locate abnormal information;

[0138] 2. Verify whether the format, type and size of the data meet the system specification requirements;

[0139] 3. Verify the stability of the data source and check for the possibility of data contamination or errors;

[0140] 4. Check system resource usage, including memory, CPU, etc., to eliminate overload issues;

[0141] 5. Update or reset system configuration parameters to ensure that they are compatible with current data processing requirements;

[0142] 6. If the problem is caused by the software, consider upgrading the software or installing the latest patch;

[0143] 7. Restart the system when necessary to resolve possible resource usage issues;

[0144] 8. If the problem still cannot be solved, it is recommended to contact technical support for professional assistance;

[0145] The numerical anomaly is detected during the collaborative verification of multiple types of data, including verification of data collection, storage and transmission processes to ensure that no data points are missed or damaged; secondly, the correctness of the data processing logic is verified, involving the correct implementation of the algorithm and the rationality of the data processing flow; the associated data around the abnormal value will be affected by external factors.

[0146] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented 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.

[0147] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does 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 method for collaborative verification and processing of multiple types of data for a classified recycling system, characterized in that: include: S1, respectively obtaining the cloud database characteristics and real-time operation data of the classification and recycling system; S1-1. Obtain real-time operation data of the classification and recycling system; S1-2, using the corresponding time of the real-time operation data of the classification recycling system as the standard time t; S1-3, using the classification recycling system to obtain historical data from the cloud database according to the standard time t; S1-4, obtaining cloud database characteristics according to the historical data of the cloud database; S2. Establishing a system data chain of the classified recycling system according to the cloud database characteristics of the classified recycling system; S2-1, obtaining the uplink data stream of the classified recycling system according to the cloud database characteristics of the classified recycling system; S2-2, obtaining the downlink data flow of the classified recycling system according to the cloud database characteristics of the classified recycling system; S2-2-1. Obtain the uplink start transmission time t corresponding to the uplink data stream of the classification recycling system; S2-2-2, obtaining the data type, data capacity and data generation time corresponding to the real-time operation data at the uplink start transmission time t as the uplink data addressing label, uplink data proofreading label and uplink data review label respectively; S2-2-3, obtaining the real-time operation data of the next moment adjacent to the uplink start transmission moment t; S2-2-4, determine whether the data type corresponding to the real-time operation data at the next moment adjacent to the uplink start transmission time t is the same as the uplink data addressing label corresponding to the real-time operation data at the uplink start transmission time t, if so, execute S2-2-6, otherwise, directly execute S2-2-6; S2-2-5, determine whether the data capacity corresponding to the real-time operation data at the next moment adjacent to the uplink start transmission moment t, the uplink data proofreading label corresponding to the real-time operation data at the data generation moment and the uplink start transmission moment t, and the uplink data review label are all corresponding. If so, update the current moment and return to S2-2-3. Otherwise, keep the previous moment adjacent to the current moment as the uplink end moment and execute S2-2-6; S2-2-6. Use the real-time operation data from the uplink start transmission time t to the uplink end time as the uplink data flow of the classification recovery system S2-3, using the uplink data flow and downlink data flow of the classification recycling system as the system data link of the classification recycling system; S2-3-1. Obtain the downlink start transmission time k corresponding to the downlink data flow of the classification recycling system; S2-3-2, obtaining the data type, data capacity and data generation time corresponding to the real-time operation data at the downlink start transmission time k as the downlink data addressing label, downlink data proofreading label and downlink data review label respectively; S2-3-3, obtaining the real-time operation data of the next time point adjacent to the downlink start transmission time point k; S2-3-4, determine whether the data type corresponding to the real-time operation data at the next moment adjacent to the downlink start transmission time k is the same as the downlink data addressing label corresponding to the real-time operation data at the downlink start transmission time k, if so, execute S2-3-5, otherwise, directly execute S2-3-6; S2-3-5, determine whether the data capacity corresponding to the real-time operation data at the next moment adjacent to the downlink start transmission moment k, the downlink data proofreading label corresponding to the real-time operation data at the downlink start transmission moment k, and the downlink data review label are all corresponding. If so, update the current moment and return to S2-3-3. Otherwise, keep the previous moment adjacent to the current moment as the downlink end moment and execute S2-3-6; S2-3-6, using the real-time operation data from the downlink start transmission time k to the downlink end time as the downlink initial data flow of the classification recovery system; S2-2-7, determine whether the downlink data flow of the classified recycling system corresponds to the uplink data flow of the classified recycling system, if so, use the downlink initial data flow as the downlink data flow of the classified recycling system, otherwise, update the downlink start transmission time k according to the uplink start transmission time t, and return to S2-3-1; S3, obtaining the phased operation fluctuation characteristics of the classification and recycling system according to the real-time operation data of the classification and recycling system; S3-1, using the real-time operation data of the classification and recycling system to obtain the deviation point of the real-time operation data of the classification and recycling system; S3-1-1, respectively obtaining the historical uplink data flow and the historical downlink data flow according to the data type and data capacity corresponding to the real-time operation data of the classification recycling system; S3-1-2, using each data value of the historical uplink data stream to establish a historical uplink data value sequence; S3-1-3, using each data value of the historical downstream data stream to establish a historical downstream data value sequence; S3-1-4, determine whether the historical uplink data value sequence and the historical downlink data value sequence are both corresponding, if so, directly execute S3-1-6, otherwise, use the sequence position of the non-corresponding data value as the historical running data deviation point; S3-1-5, determine whether the historical operation data deviation point and the corresponding upstream data stream and downstream data stream of the real-time operation data of the classified recycling system have corresponding sequence positions, if so, use the current sequence position as the real-time operation data deviation point of the classified recycling system, otherwise, the real-time operation data deviation point of the classified recycling system is empty; S3-1-6, determine whether the historical uplink data value sequence corresponds to the uplink data flow corresponding to the real-time operation data of the classification recycling system, and whether the historical downlink data value sequence corresponds to the downlink data flow corresponding to the real-time operation data of the classification recycling system. If so, the deviation point of the real-time operation data of the classification recycling system is empty. Otherwise, the sequence position of the uncorresponding data value is used as the deviation point of the real-time operation data of the classification recycling system; S3-2, obtaining the phased operation fluctuation characteristics of the classification and recycling system according to the deviation point of the real-time operation data of the classification and recycling system; S3-2-1, judging whether there is a deviation point of the real-time operation data of the classification and recycling system at the current moment, if so, executing S3-2-2, otherwise, the staged operation fluctuation characteristic of the classification and recycling system is stable; S3-2-2, using the real-time operation data deviation point as a sampling base point to respectively obtain adjacent preceding and succeeding data as pre-fluctuation sampling data and post-fluctuation sampling data; S3-2-3, obtaining the data change rate of the post-fluctuation sampled data relative to the pre-fluctuation sampled data as a normal fluctuation threshold; S3-2-4, obtaining the data change rate of the deviation point of the real-time operation data relative to the sampled data before the fluctuation as the fluctuation starting threshold; S3-2-5, obtaining the data change rate of the post-fluctuation sampling data relative to the deviation point of the real-time operation data as the fluctuation downward threshold; S3-2-6, using the normal fluctuation threshold, fluctuation starting threshold and fluctuation falling threshold as the staged operation fluctuation characteristics of the classification recycling system; S4, cross-validation processing is performed according to the system data chain of the classification recycling system and the phased operation fluctuation characteristics to obtain a multi-type data collaborative validation processing result; S4-1, judging whether the system data chain of the classified recycling system corresponds to the phased operation fluctuation characteristics, if so, executing S4-2, otherwise, the result of the multi-category data collaborative verification processing is normal; S4-2, determine whether the number of occurrences of the phased operation fluctuation characteristics of the system data chain of the classified recycling system is 1, if so, there is a transmission anomaly in the collaborative verification processing result of the multi-category data, and output the data position of the deviation point of the real-time operation data corresponding to the phased operation fluctuation characteristics in the system data chain of the classified recycling system, otherwise, execute S4-3; S4-3, determine whether the staged operation fluctuation characteristics are adjacent to the corresponding positions of the system data chain of the same classification and recycling system. If so, there is a system abnormality in the collaborative verification processing results of the multi-category data, and output the data position of the deviation point of the real-time operation data corresponding to the staged operation fluctuation characteristics in the system data chain of the classification and recycling system. Otherwise, obtain the abnormal floating difference between the fluctuation starting threshold and the fluctuation falling threshold corresponding to each staged operation fluctuation characteristic, and execute S4-4; S4-4. Determine whether the abnormal floating differences corresponding to the various stage-by-stage operation fluctuation characteristics are all smaller than the normal fluctuation threshold. If so, there is a transmission anomaly in the collaborative verification processing results of the multi-category data, and the data position of the deviation point of the real-time operation data corresponding to the stage-by-stage operation fluctuation characteristics in the system data chain of the classification and recycling system is output. Otherwise, there is a numerical anomaly in the collaborative verification processing results of the multi-category data, and the abnormal data value of the real-time operation data is output.

2. A method for collaborative verification and processing of multiple types of data for a classified recycling system as claimed in claim 1, characterized in that: The cloud database characteristics obtained according to the historical data of the cloud database include: S1-4-1. Obtain the data type, data capacity and data generation time corresponding to the historical data of the cloud database respectively; S1-4-2, using the data type, data capacity and data generation time corresponding to the historical data as addressing features, proofreading features and review features respectively; S1-4-3, determine whether the data occurrence time corresponding to the historical data is continuous, if so, use the addressing feature, proofreading feature and review feature as cloud database features, otherwise, obtain the discontinuous data occurrence time as the breakpoint time label, and execute S1-4-4; S1-4-4, obtaining historical data of the cloud database corresponding to the adjacent moments according to the breakpoint moment label as the breakpoint addressing data threshold; S1-4-5. Determine whether there is corresponding historical addressing data between the cloud database and the breakpoint addressing data threshold. If so, obtain the data type, data capacity and data generation time of the intermediate data corresponding to the historical addressing data as addressing features, proofreading features and review features, respectively, and return to S1-4-3. Otherwise, use the relative preceding time from the standard time t to the breakpoint addressing data threshold as the new time period, and return to S1-3.

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