Medlar production data monitoring and early warning control system

Abstract

The invention discloses a wolfberry production data monitoring and early warning control system, and relates to the technical field of wolfberry production data processing. The method comprises the following steps of: periodically performing hash processing on pre-interactive production data by setting a hash interaction terminal to calculate verification identification data of sub-class data of all attribute items in each class of data in the production data; an interaction monitoring and early warning unit is arranged to overall plan execution of an execution script of a verification thread corresponding to each attribute item in hash interaction data, so that subclass data which have similar data capacities and cover more data formats can be preferentially verified compared with subclass data which cover less data formats; and if there is missing in verification, it can be ensured that the retransmitted data arrives at the cloud end more quickly for secondary waiting for secondary integrity verification, in this way, the verification sequence of complex data formats in the production data is reasonably planned, and the overall rate of integrity verification of the production data is also improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wolfberry production data processing, and particularly to a wolfberry production data monitoring and early warning control system. Background Art

[0002] In the process of wolfberry production, in order to achieve refined management and quality traceability, it is crucial to upload production data to the cloud for subsequent backtracking and analysis. Ensuring the integrity of production data during transmission is the basis of reliable data management.

[0003] Currently, there are mainly two common methods to ensure the integrity of production data transmission. The first is to calculate the hash value of production data, and the cloud verifies the integrity of production data based on the obtained hash value. However, when data loss is found during verification, all production data needs to be retransmitted, which not only wastes a large amount of bandwidth resources but also consumes more time, seriously affecting the data transmission and processing efficiency.

[0004] The second method is to perform segmented processing on production data and calculate the hash value for each segment of data. Compared with the first method, this method improves the accuracy and flexibility of verification to a certain extent. However, due to the wide range of sources for collecting wolfberry production data, covering various data formats, for data with the same data capacity, due to the different numbers of data formats it covers, there will be differences in the integrity verification rate. Currently, there is a lack of reasonable planning for the verification order of such complex data format data, resulting in a reduction in the overall rate of production data integrity verification.

[0005] To solve the above problems, the present invention proposes a solution. Summary of the Invention

[0006] The purpose of the present invention is to provide a wolfberry production data monitoring and early warning control system to solve the problems raised in the above background art.

[0007] The present invention provides a wolfberry production data monitoring and early warning control system, including:

[0008] A hashing interaction terminal, configured to perform hashing processing on all production data of wolfberries in a target factory area stored within each hashing interaction period every other hashing interaction period to obtain hashing interaction data corresponding to the hashing interaction period. The production data is composed of several categories of data, and each category of data contains sub - data with several attribute items; all sub - data in any category of data constituting the production data contains several data formats.

[0009] The interactive monitoring and warning unit is used to, after receiving the hashed interactive data of each hash interaction cycle, overall plan the enabling of the execution scripts of the verification threads corresponding to all attribute items according to the verification ratio parameters of all attribute items related to several data formats stored in the interactive monitoring and warning unit and the data capacity sizes of the data of each data format in the subclass data of all attribute items contained in the hashed interactive data.

[0010] Furthermore, several execution scripts of verification threads are stored in the interactive monitoring and warning unit. One execution script of a verification thread corresponds to one attribute item and is used to verify the integrity of the subclass data of the corresponding attribute item received.

[0011] Furthermore, based on all the production data stored in the hashed interactive terminal within any one hash interaction cycle in the order of their storage time, the check identification data of the corresponding attribute items is calculated for the subclass data of all attribute items contained in each production data by using an integrity algorithm; the hashed interactive data of the hash interaction cycle is generated according to the check identification data of all attribute items and their subclass data in all the production data obtained by calculation.

[0012] Furthermore, when the execution script of any verification thread starts to execute, a verification thread will be first created for the corresponding attribute item, and then the integrity of all the subclass data of the received attribute item will be verified according to the verification logic contained in the execution script. If the verification passes, a thread execution record data of the verification thread will be generated according to the verification duration of the verification thread and all the subclass data of the attribute item, and the thread execution record data will be transmitted to the execution record analysis unit. Otherwise, an interactive monitoring and warning instruction will be generated according to the attribute item, and the interactive monitoring and warning instruction will be transmitted to the hashed interactive terminal.

[0013] Furthermore, after receiving the transmitted interactive monitoring and warning instruction, the hashed interactive terminal extracts all the subclass data of the attribute item from the hashed interactive data according to the attribute item contained therein, and re-hashes the extracted all subclass data to generate new check identification data of the attribute item; the hashed interactive terminal generates the re-transmitted hashed interactive data of the attribute item according to the extracted all subclass data of the attribute item and the generated new check identification data.

[0014] Further, after receiving the retransmitted hashed interaction data transmitted, the interaction monitoring and warning unit checks whether the number of currently existing verification threads is less than the maximum number of threads that can be enabled currently. If it is less, it obtains the execution script corresponding to the attribute item according to the attribute items included in the retransmitted hashed interaction data and executes it. Otherwise, it waits until the number of currently existing verification threads is less than the maximum number of threads that can be enabled currently and then executes the execution script of the corresponding verification thread.

[0015] Compared with the prior art, the following beneficial effects are achieved:

[0016] In the present invention, a wolfberry data acquisition terminal is set to collect the production data of wolfberries in the target factory area. A hashed interaction terminal is set to perform hashing processing on the production data to be interacted periodically and calculate the verification identification data of the subclass data of all attribute items in each category of data in the production data. An interaction monitoring and warning unit is set to coordinate the execution of the execution script of the verification thread corresponding to each attribute item according to the data capacity size of the data of several data formats covered in the subclass data of each attribute item and the verification ratio parameters of several data formats. Thus, the subclass data with similar data capacity size and covering more data formats can be verified preferentially compared with the subclass data covering fewer data formats. If there is a missing in the verification, it can ensure that the retransmitted data reaches the cloud faster for a second waiting for a second integrity verification. In this way, not only the verification order of the complex data formats in the production data is reasonably planned, but also the overall rate of the production data integrity verification is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] Please refer to Figure 1 , this application provides a wolfberry production data monitoring and warning control system, including a wolfberry data acquisition terminal, a hashed interaction terminal, and a cloud monitoring and warning platform;

[0020] The goji berry data acquisition terminal is used to collect the production data of goji berries in the target factory area in real time. The production data consists of several categories of data, that is, the production data contains several categories of data, and one category of data contains sub-categories of data with several attribute items. All the sub-categories of data included in any one category of data contained in the production data cover several data formats. In this application, the data formats covered include text, image, and video formats;

[0021] In this application, several attribute items contained in one category of data are defined by the management personnel according to their acquisition sources;

[0022] In this application, the category data contained in the production data includes planting, environment, production management, harvesting, processing, and warehousing logistics category data;

[0023] In this application, the sub-category data of several attribute items contained in the planting category data includes planting area, planting location, planting variety, and planting density; the sub-category data of several attribute items contained in the environment category data includes meteorological data, water quality data, and air quality data; the sub-category data of several attribute items contained in the production management category data includes fertilization records, irrigation records, pest control records, and pruning records; the sub-category data of several attribute items contained in the harvesting category data includes harvesting time, harvesting method, harvesting quantity, and harvesting quality; the sub-category data of several attribute items contained in the processing category data includes cleaning records, drying methods, grading standards, and packaging data; the sub-category data of several attribute items contained in the warehousing logistics category data includes storage conditions, inventory levels, and transportation records;

[0024] The goji berry data acquisition terminal transmits the production data of goji berries in the target factory area collected in real time to the hashing interaction terminal;

[0025] The hashing interaction terminal is used to periodically perform hashing processing on the production data of goji berries in the target factory area collected and interact it to the cloud monitoring and early warning platform. After receiving the production data of goji berries in the target factory area transmitted in real time, the hashing interaction terminal stores it;

[0026] The hashing interaction terminal is also used to perform hashing processing on all the production data stored during the hashing interaction period every other hashing interaction period, and transmit the hashing interaction data corresponding to the hashing interaction period obtained after processing to the cloud monitoring and early warning platform;

[0027] In this application, the steps of hashing processing are as follows:

[0028] Based on all the production data stored in the hashed interaction terminal within any one hashing interaction cycle, in the chronological order of their storage, the integrity algorithm is used to calculate the verification identification data of the corresponding attribute items for all sub-class data of all attribute items included in each production data; the hashed interaction data of the hashing interaction cycle is generated according to the verification identification data of all attribute items and their sub-class data in all the production data obtained by calculation;

[0029] In this application, the integrity algorithm can be arbitrarily selected from the hash algorithm, MAC algorithm, AEAD algorithm, Merkle Tree algorithm, and digital signature algorithm;

[0030] In this application, the verification identification data corresponds to different data according to the integrity algorithm adopted. For example, if the integrity algorithm adopted is the hash algorithm, the verification identification data is a hash value; if the integrity algorithm adopted is the MAC algorithm, the verification identification data is an output code of a fixed length;

[0031] The cloud monitoring and early warning platform is used to monitor the interaction process of the production data of wolfberries and quickly send early warnings to the target factory area based on the missing production data in the interaction. The cloud monitoring and early warning platform includes an interaction monitoring and early warning unit and an execution record analysis unit;

[0032] After receiving the hashed interaction data of one hashing interaction cycle, the cloud monitoring and early warning platform transmits the hashed interaction data to the interaction monitoring and early warning unit;

[0033] There are execution scripts of several verification threads stored in the interaction monitoring and early warning unit. The execution script of one verification thread corresponds to one attribute item, and one execution script is an executable file;

[0034] The execution script of one verification thread contains the integrity verification logic for the sub-class data of the corresponding attribute item, and is used to verify the integrity of the received sub-class data of the corresponding attribute item;

[0035] After receiving the transmitted hashed interaction data, the interaction monitoring and early warning unit obtains all the sub-class data of all attribute items included therein, and then based on all the sub-class data of all attribute items in the hashed interaction data, conducts overall planning on the enabling of the execution scripts of the verification threads corresponding to all attribute items in the hashed interaction data according to the preset overall planning rules. The overall planning rules are as follows:

[0036] SS11: Select an attribute item included in the production data of wolfberries as the execution evaluation item, extract all subclass data of the execution evaluation item from the hashed interaction data, and sequentially obtain the data capacity sizes of the data with data formats B1, B2, ..., Bb from the extracted all subclass data, which are marked as G1, G2, ..., Gb respectively;

[0037] SS12: Sequentially extract the verification ratio parameters H1, H2, ..., Hb related to the data formats B1, B2, ..., Bb of the execution evaluation item from the interaction monitoring and warning unit;

[0038] SS13: Use the formula to calculate the verification priority I1 of the execution evaluation item. In the formula, Gh represents each of the data capacity sizes G1, G2, ..., Gb, Hh represents each of the verification ratio parameters H1, H2, ..., Hb, and ɑ1, ɑ2 are the preset first and second evaluation feature ratios in sequence, which are used to adjust the features of different calculation dimensions to the same calculation dimension for calculation;

[0039] SS14: Sequentially select all types of attribute items included in the production data as the execution evaluation item, and calculate and obtain the verification priorities of all types of attribute items in sequence according to SS11 to SS13;

[0040] SS15: Mark all types of attribute items as J1, J2, ..., Jj in descending order of their verification priorities, where j ≥ 1;

[0041] According to the maximum number of threads P1 that can be enabled currently, respectively obtain the execution scripts of the verification threads corresponding to the attribute items J1, J2, ..., JP1, synchronously execute all the obtained execution scripts. After each execution script is executed, continue to obtain the execution script of the verification thread corresponding to the next attribute item in the order of the attribute items JP1+1, JP1+2, ..., Jj and execute the execution script until all the execution scripts of the verification threads corresponding to all attribute items are executed;

[0042] When starting to execute the execution script of any verification thread, a verification thread will be created for the corresponding attribute item first, and then the integrity check will be performed on all the subclass data of the attribute item received according to the verification logic included in the execution script. If the check passes, a thread execution record data of the verification thread will be generated according to the verification duration of the verification thread and all the subclass data of the attribute item, and the thread execution record data will be transmitted to the execution record analysis unit. Otherwise, an interaction monitoring and warning instruction will be generated according to the attribute item and the interaction monitoring and warning instruction will be transmitted to the hashed interaction terminal;

[0043] In this application, the value of P1 is determined by comprehensively considering factors such as the number and frequency of CPU cores, the physical memory capacity, the memory allocation strategy, the management restrictions of the operating system on thread resources (such as the maximum thread number quota that a single process can occupy), the server load condition (the proportion of system resources occupied by currently running processes and threads), and the demand characteristics of the application itself for thread resources (whether it is computationally intensive or I / O intensive);

[0044] After receiving the transmitted interactive monitoring and warning instruction, the hashed interactive terminal extracts all subclass data of the attribute item from the hashed interactive data according to the attribute items included therein, and re-hashes the extracted all subclass data to generate new verification identification data for the attribute item;

[0045] The hashed interactive terminal generates re-transmitted hashed interactive data for the attribute item according to all subclass data of the extracted attribute item and the generated new verification identification data, and transmits the re-transmitted hashed interactive data to the interactive monitoring and warning unit;

[0046] After receiving the transmitted re-transmitted hashed interactive data, the interactive monitoring and warning unit checks whether the number of currently existing verification threads is less than the maximum number of threads that can be enabled currently. If it is less, it obtains the execution script corresponding to the attribute item according to the attribute item included in the re-transmitted hashed interactive data and executes it. Otherwise, it waits temporarily until the number of currently existing verification threads is less than the maximum number of threads that can be enabled currently, and then executes the execution script of the corresponding verification thread;

[0047] It should be noted here that if several re-transmitted hashed interactive data are received, the execution scripts of the corresponding verification threads are executed in the order of receipt;

[0048] The execution record analysis unit is used to analyze all thread execution record data stored every other record analysis period. The analysis steps are as follows:

[0049] S11: Select an attribute item included in the production data of wolfberries as the verification and analysis item, and extract all subclass data of the verification and analysis item included in all thread execution record data stored during the record analysis period, and mark them as A1, A2,..., Aa respectively, where a≥1;

[0050] S12: Obtain all data formats involved in the subclass data A1, A2,..., Aa, and mark them as B1, B2,..., Bb respectively, where b≥1;

[0051] S13: Obtain the equation for the verification analysis item based on the sub-category data A1 according to the data capacity sizes C1, C2, ..., Cb of the data in the data formats B1, B2, ..., Bb obtained successively from the sub-category data A1.

[0052] In the equation, E1 is the execution time of the verification thread whose verification object is subclass data A1, Cc is represented by each of the data capacity sizes C1, C2, ..., Cb, and Dc is represented by each of the verification proportion factors of the data formats B1, B2, ..., Bb in the subclass data A1. It should be noted here that the verification proportion factor is artificially defined to represent the restriction proportion of each data format in the subclass data A1 on the execution time of the verification thread when the corresponding verification thread verifies the integrity of the subclass data A1;

[0053] S14: according to S13, equations E2, E3, ..., Ea based on sub-category data A2, A3, ..., Aa of the verification analysis items are calculated in sequence, and then several sets of equations can be obtained according to equations E1, E2, ..., Ea combined with P1, wherein one of the equations is obtained by randomly selecting P1 equations from equations E1, E2, ..., Ea and combining them, and P1 is a preset standard equation selection quantity;

[0054] By solving the obtained several sets of equations respectively, several verification proportion factors in the data format of B1, B2, ..., Bb can be obtained respectively. It should be noted here that after solving a set of equations, a verification proportion factor in the data format of B1, B2, ..., Bb can be obtained respectively;

[0055] S15: Use a discrete point filtering algorithm to process all the verification proportion factors obtained in the data format B1, and calculate the average value of all the verification proportion factors remaining after the data processing, and mark the average value as the verification proportion parameter of the verification analysis item related to the data format B1. Similarly, the verification proportion parameters of the verification analysis item related to the data formats B2, B3, ..., Bb are obtained successively;

[0056] In this application, the discrete point filtering algorithm may be any one of a Z-score filtering algorithm, an IQR filtering algorithm, and a density filtering algorithm;

[0057] Generate monitoring analysis data of the verification analysis item in the recording analysis period according to the verification ratio parameters of the verification analysis item related to the data formats B1, B2, ..., Bb;

[0058] S16: Select all the attribute items contained in the production data of wolfberry as verification analysis items respectively, and calculate and obtain the monitoring analysis data of all the attribute items in the recording and analysis period respectively according to S12 to S15;

[0059] The execution record analysis unit transmits the monitoring and analysis data of all types of attribute items in the record analysis period to the interactive monitoring and early warning unit for updated storage;

[0060] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well known to those skilled in the art.

[0061] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A wolfberry production data monitoring and early warning control system, characterized in that, Including: A hashed interaction terminal, which is used to hash all production data of wolfberries in the target factory area stored within each hashed interaction cycle every other hashed interaction cycle to obtain hashed interaction data corresponding to the hashed interaction cycle. The production data is composed of several category data, and each category data contains sub-category data with several attribute items; all sub-category data in any one category data that constitutes the production data contains several data formats; An interaction monitoring and warning unit, which is used to overall plan the enabling of execution scripts of verification threads corresponding to all attribute items according to the verification ratio parameters of all attribute items related to several data formats stored in the interaction monitoring and warning unit and the data capacity sizes of each data format in the sub-category data of all attribute items contained in the hashed interaction data after receiving the hashed interaction data of each hashed interaction cycle.

2. The wolfberry production data monitoring and early warning control system according to claim 1, characterized in that, The interaction monitoring and warning unit stores execution scripts of several verification threads. One execution script of a verification thread corresponds to one attribute item and is used to verify the integrity of the received sub-category data of the corresponding attribute item.

3. The wolfberry production data monitoring and early warning control system according to claim 1, characterized in that It further includes an execution record analysis unit, which is used to analyze all thread execution record data stored every other record analysis cycle. The analysis steps are as follows: S11: Select an attribute item contained in the production data of wolfberries as the verification analysis item, and extract all sub-category data containing the verification analysis item from all thread execution record data stored within the record analysis cycle, and mark them as A1, A2,..., Aa respectively, where a≥1; S12: Obtain all data formats contained in the sub-category data A1, A2,..., Aa, and mark them as B1, B2,..., Bb respectively, where b≥1; S13: Based on the data capacities C1, C2, ..., Cb of the data in the formats B1, B2, ..., Bb successively obtained from the subclass data A1, obtain the equation of the verification analysis item based on the subclass data A1 In the equation, E1 is the execution duration of the verification thread with the verification object being the sub-category data A1, Cc represents each of the data capacity sizes C1, C2,..., Cb, and Dc represents each of the verification ratio factors of the data formats B1, B2,..., Bb in the sub-category data A1. The verification ratio factor is defined artificially and is used to represent the proportion of the restriction of each data format in the sub-category data A1 on the execution duration of the verification thread when verifying the integrity of the sub-category data A1; S14: Calculate and obtain the equations E2, E3,..., Ea of the verification analysis item based on the sub-category data A2, A3,..., Aa in sequence according to S13, and then combine the equations E1, E2,..., Ea with P1 to obtain several groups of equations. P1 is the preset standard equation selection quantity; solve the obtained several groups of equations respectively to obtain several verification ratio factors of the data formats B1, B2,..., Bb; S15: Use the discrete point filtering algorithm to process all the verification ratio factors with the data format of B1 obtained, and calculate the average value of all the remaining verification ratio factors after data processing. Calibrate the average value as the verification ratio parameter of the verification analysis item related to the data format B1. Similarly, obtain the verification ratio parameters of the verification analysis item related to the data formats B2, B3, ..., Bb successively; Generate the monitoring analysis data of the verification analysis item in the record analysis period according to the verification ratio parameters of the verification analysis item related to the data formats B1, B2, ..., Bb; S16: Select all the species attribute items included in the production data of Chinese wolfberry as the verification analysis items respectively, and calculate and obtain the monitoring analysis data of all the species attribute items in the record analysis period according to S12 to S15 respectively; The execution record analysis unit transmits the monitoring analysis data of all the attribute items in the record analysis period to the interactive monitoring and warning unit for update and storage.

4. The wolfberry production data monitoring and early warning control system according to claim 1, characterized in that, Based on all the production data stored in the hashed interactive terminal within any one hashing interaction period in the order of their storage time, calculate the check identification data of the corresponding attribute items for each subclass data of all the attribute items included in each production data by using the integrity algorithm; Generate the hashed interactive data of the hashing interaction period according to the check identification data of all the attribute items in all the production data obtained by calculation and their subclass data.

5. A wolfberry production data monitoring and early warning control system according to claim 3, characterized in that, Overall plan the enabling of the execution scripts of the verification threads corresponding to all the attribute items in a hashed interactive data, specifically as follows: SS11: Select one attribute item included in the production data of Chinese wolfberry as the execution evaluation item, extract all the subclass data of the execution evaluation item from the hashed interactive data, and successively obtain the data capacity sizes of the data with the data formats of B1, B2, ..., Bb from all the extracted subclass data and mark them as G1, G2, ..., Gb; SS12: Extract the verification ratio parameters H1, H2, ..., Hb of the execution evaluation item related to the data formats B1, B2, ..., Bb from the interactive monitoring and warning unit in sequence; SS13: Using the formula calculate the verification priority I1 for the execution evaluation item. In the formula, Gh represents each of the data capacity sizes G1, G2, ..., Gb, Hh represents each of the verification ratio parameters H1, H2, ..., Hb, and ɑ1 and ɑ2 are the preset first and second evaluation feature ratios in sequence; SS14: Select all the species attribute items included in the production data as the execution evaluation items in sequence, and calculate and obtain the verification priority amounts of all the species attribute items according to SS11 to SS13 in sequence; SS15: Mark all the species attribute items as J1, J2, ..., Jj in the order of their verification priority amounts from large to small, where j ≥ 1; According to the maximum number of threads P1 that can be enabled currently, obtain the execution scripts of the verification threads corresponding to the attribute items J1, J2, ..., JP1 respectively, synchronously execute all the obtained execution scripts. After each execution script is executed, continue to obtain the execution script of the verification thread corresponding to the next attribute item in the order of the attribute items JP1 + 1, JP1 + 2, ..., Jj and execute the execution script until all the execution scripts of the verification threads corresponding to all the attribute items are executed.

6. The wolfberry production data monitoring and early warning control system according to claim 5, characterized in that, When the execution script for any verification thread starts to execute, a verification thread will first be created for the corresponding property item, and then the integrity check of all subclass data of the received property item will be performed according to the verification logic included in the execution script. If the check passes, a thread execution record data of the verification thread will be generated based on the verification duration of the verification thread and all subclass data of the property item, and the thread execution record data will be transmitted to the execution record analysis unit for storage. Otherwise, an interactive monitoring warning instruction will be generated according to the property item and transmitted to the hashed interactive terminal.

7. The wolfberry production data monitoring and early warning control system according to claim 6, characterized in that, After receiving the transmitted interactive monitoring warning instruction, the hashed interactive terminal extracts all subclass data of the property item from the hashed interactive data according to the property item included therein, and re-performs hashing processing on the extracted all subclass data to generate new check identification data of the property item; the hashed interactive terminal generates re-transmitted hashed interactive data of the property item according to the extracted all subclass data of the property item and the generated new check identification data.

8. The wolfberry production data monitoring and early warning control system according to claim 7, characterized in that, After receiving the transmitted re-transmitted hashed interactive data, the interactive monitoring warning unit checks whether the number of currently existing verification threads is less than the maximum number of threads that can be currently enabled. If it is less, the execution script corresponding to the property item is obtained according to the property item included in the re-transmitted hashed interactive data for execution. Otherwise, it waits until the number of currently existing verification threads is less than the maximum number of threads that can be currently enabled to execute the execution script of the corresponding verification thread.

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