An intelligent control system for crop transportation and storage

Abstract

The present invention discloses an intelligent control system for the transportation and storage of crops, belonging to the technical field of transportation and storage control. By performing data analysis on the grain weight supervision processing sequences of corresponding processing combinations of different target crops from the dimension of grain weight, the corresponding grain weight supervision status is obtained. By performing data analysis on the first grain quality supervision processing sequence and the second grain quality supervision processing sequence of corresponding processing combinations of different target crops from the dimension of grain quality respectively, the corresponding grain quality supervision status is obtained. Using the local supervision analysis data of different dimensions in the early stage to perform digital processing and analysis on the overall supervision status of different target crops in the overall dimension, and adaptively implementing targeted supervision and rectification on different links of different target crops according to the analysis results. It can solve the technical problems of poor local supervision effects in different dimensions of crop transportation and storage and poor adaptive dynamic control effects in the existing solutions.

Description

Technical Field

[0001] The present invention relates to the technical field of transportation and storage control, and particularly relates to an intelligent control system for crop transportation and storage. Background Art

[0002] The transportation and storage of crops are important components of agricultural production and supply chain management, involving the entire process of ensuring the safe and efficient transfer of crops from the field to consumers. These two aspects are crucial for maintaining the quality of agricultural products, reducing losses, and ensuring food safety.

[0003] Existing transportation and storage solutions for food crops face various challenges and there are also some areas that need improvement. When implementing existing transportation and storage solutions for food crops, most still remain at monitoring the entire process of food crop transportation and storage through Internet of Things technology, as well as data statistics and storage. They cannot conduct local supervision and evaluation and overall supervision and evaluation of different dimensions on the anomalies existing in the process of food crop transportation and storage, and cannot adaptively dynamically control the process of food crop transportation and storage according to different evaluation results, resulting in poor local supervision effects of different dimensions of crop transportation and storage and poor adaptive dynamic control effects. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent control system for crop transportation and storage, which is used to solve the technical problems of poor local supervision effects of different dimensions of crop transportation and storage and poor adaptive dynamic control effects in existing solutions.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An intelligent control system for crop transportation and storage, comprising:

[0007] A crop transportation and storage supervision and processing module, which is used to monitor the entire process and conduct data statistics on the transportation link and storage link of different target crops respectively, and conduct digital processing and monitoring status analysis of different dimensions on the data monitored and statistically analyzed in different links, so as to obtain first supervision and processing data and second supervision and processing data of different dimensions corresponding to different target crops and upload them to the transportation and storage control platform in real time;

[0008] Among them, according to the first grain monitoring weight and the second grain monitoring weight obtained by monitoring the transportation link and storage link of the target crop, the digital processing and combination of the grain weight supervision status of the transportation link and storage link corresponding to different target crops are carried out respectively, so as to obtain the first supervision and processing data corresponding to different target crops;

[0009] Moreover, monitor and obtain the internal temperature distribution maps of grain stacks in the transportation and storage links corresponding to different target crops, digitally process and combine the monitored temperature values corresponding to different monitoring points in the internal temperature distribution maps of different links to obtain the first grain quality supervision processing sequence and the second grain quality supervision processing sequence for the corresponding links;

[0010] Perform data analysis on the first grain quality supervision processing sequence and the second grain quality supervision processing sequence to obtain the quality supervision normal label or quality supervision abnormal label for the grain quality dimension corresponding to the target crop;

[0011] Combine the first grain quality supervision processing sequence and the second grain quality supervision processing sequence corresponding to the target crop, and the quality supervision normal label or quality supervision abnormal label obtained through analysis to obtain the second supervision processing data for the corresponding target crop;

[0012] The crop transportation and storage supervision and control module is used to integrate and calculate the local abnormal supervision data obtained from different dimensions of the transportation and storage links corresponding to different target crops, and analyze the overall supervision status of the overall dimension, and adaptively implement targeted optimization control on the transportation and storage links corresponding to the target crop according to the analysis result of the overall supervision status of the overall dimension.

[0013] Preferably, number and label different target crops for transportation and storage as i, where i = {1, 2, 3, ……, N}; N is a positive integer;

[0014] Obtain the initial grain weight LZi of different target crops before the start of transportation, and the first grain monitoring weight LJi1 and the second grain monitoring weight LJi2 corresponding to different target crops after the end of the transportation and storage links;

[0015] When analyzing and processing the grain weight supervision status of the transportation and storage links corresponding to different target crops based on the monitored first grain monitoring weight and the second grain monitoring weight, input the first grain monitoring weight and the second grain monitoring weight corresponding to different target crops into the grain weight monitoring and recognition function for data analysis, and output the grain weight supervision identifier ZJik corresponding to the transportation and storage links;

[0016] The grain weight supervision identifiers corresponding to the transportation and storage links both contain numerical values of 0 or 1;

[0017] A grain weight supervision identifier with a numerical value of 0 indicates that the grain weight monitoring status of the corresponding link is normal;

[0018] A grain weight supervision identifier with a numerical value of 1 indicates that the grain weight monitoring status of the corresponding link is abnormal;

[0019] Sort and combine the food weight supervision identifiers obtained from the processing of the corresponding transportation and storage links of the target crops to obtain the food weight supervision processing sequence.

[0020] Preferably, the expression of the food weight monitoring and recognition function is ; where k = 1, 2, representing different links; LJik is LJi1, LJi2; Wik is Wi1, Wi2, which are the transportation weight standard errors and storage weight standard errors of the corresponding transportation and storage links of different target crops respectively.

[0021] Preferably, when performing data analysis on the food weight supervision processing sequence to determine the local supervision status of the food weight dimension corresponding to the target crop;

[0022] Perform a traversal analysis on the food weight supervision processing sequence;

[0023] If the element values in the food weight supervision processing sequence are all 0, generate a weight supervision normal label and mark the food weight supervision status of the associated target crop as normal;

[0024] If there is an element with a value of 1 in the food weight supervision processing sequence, generate a weight supervision abnormal label and mark the food weight supervision status of the associated target crop as abnormal.

[0025] Preferably, monitor and obtain the internal temperature distribution maps of the food stacks in the transportation and storage links corresponding to different target crops, and obtain the monitoring temperature values corresponding to different monitoring points in the internal temperature distribution maps of different links; sort and combine the monitoring temperature values corresponding to different monitoring points in different links respectively to obtain the monitoring temperature sequence [wjk] for the corresponding link; where j is different monitoring points in the internal temperature distribution map, j = {1, 2, 3,..., m}; m is a positive integer; [wj1], [wj2] are the monitoring temperature sequences corresponding to the food stacks in the transportation and storage links respectively;

[0026] When performing data analysis on the food quality supervision status corresponding to different target crops based on the monitoring temperature sequences of the food stacks in the transportation and storage links, sequentially pass all the element values in the monitoring temperature sequences of the food stacks in the transportation and storage links corresponding to different target crops through the food quality monitoring and recognition function for data analysis, and output the food quality supervision identifiers ZBjk corresponding to different elements in the monitoring temperature sequence;

[0027] The food quality supervision identifier contains a value of 0 or 1;

[0028] A food quality supervision identifier with a value of 0 indicates that the monitoring temperature status of the monitoring point corresponding to the food stack in the transportation or storage link of the target crop is normal;

[0029] A food quality supervision label with a value of 1 indicates that the monitored temperature status at the monitoring point corresponding to the grain stack in the transportation link or storage link of the target crop is abnormal;

[0030] The different food quality supervision labels obtained by processing the monitored temperature sequences of the grain stacks in the transportation link and storage link corresponding to the target crop are sorted and combined respectively to obtain the first food quality supervision processing sequence and the second food quality supervision processing sequence for the corresponding link.

[0031] Preferably, the expression of the food quality monitoring and recognition function is ; where Ujk are Uj1 and Uj2, which are the standard ranges of monitored temperatures corresponding to different monitoring points of the grain stacks in the transportation link and storage link of the target crop respectively.

[0032] Preferably, when analyzing the data of the first food quality supervision processing sequence and the second food quality supervision processing sequence to determine the local supervision status of the food quality dimension corresponding to the target crop;

[0033] If the element values in both the first food quality supervision processing sequence and the second food quality supervision processing sequence are 0, a quality supervision normal label is generated and the target crop is associated with a mark indicating normal food quality supervision status;

[0034] If there are elements with a value of 1 in the first food quality supervision processing sequence and / or the second food quality supervision processing sequence, a quality supervision abnormal label is generated and the target crop is associated with a mark indicating abnormal food quality supervision status.

[0035] Preferably, the identity of the target crop is dynamically marked according to the first supervision processing data and the second supervision processing data corresponding to different target crops;

[0036] If there are no weight supervision abnormal labels and quality supervision abnormal labels in both the first supervision processing data and the second supervision processing data, an overall normal label is generated and the target crop is marked as a regular target crop;

[0037] Otherwise, an overall abnormal label is generated and the target crop is marked as a special target crop.

[0038] Preferably, the total number N1 of weight supervision abnormalities and the total number N2 of quality supervision abnormalities corresponding to all special target crops are counted, and the overall abnormality degree Zy of all special target crops is calculated through the overall supervision formula ; where α and β are different weight coefficients, 0 < α < β; N is the total number of transportation and storage of all target crops under supervision.

[0039] Preferably, when analyzing the data of the overall abnormality to determine the overall supervision status of the overall dimension, through the overall abnormality status formula calculate and obtain the overall abnormality status identifier Zb corresponding to all special target crops and analyze it; in the formula, A is the overall abnormality status standard value; [x] is the rounding function, indicating that the largest integer not exceeding the real number x is called the integer part of x;

[0040] If the overall abnormality status identifier is 0, generate an overall mild abnormality status and prompt;

[0041] If the overall abnormality status identifier is not 0, generate an overall severe abnormality status and prompt, and at the same time control the transportation and storage links of subsequent target crops to implement comprehensive supervision and rectification.

[0042] Compared with the existing scheme, the beneficial effects achieved by the present invention are:

[0043] By analyzing the data of the grain weight supervision processing sequence of the corresponding processing combinations of different target crops from the grain weight dimension, the present invention obtains the grain weight supervision status corresponding to different target crops, and can also provide reliable local grain weight analysis data support for the analysis of the overall supervision status of the overall dimension corresponding to all subsequent abnormal target crops.

[0044] By analyzing the data of the first grain quality supervision processing sequence and the second grain quality supervision processing sequence of the corresponding processing combinations of different target crops from the grain quality dimension respectively, the present invention obtains the grain quality supervision status corresponding to different target crops, and can also provide reliable local grain quality analysis data support for the analysis of the overall supervision status of the overall dimension corresponding to all subsequent abnormal target crops.

[0045] The present invention uses the local supervision analysis data of different dimensions in the early stage to dynamically analyze and mark the identities of different target crops, digitally process and analyze the overall supervision status of the overall dimension of all special target crops obtained by the analysis, and adaptively implement targeted supervision and rectification on different links of different target crops according to the analysis results, improving the local supervision effect and the adaptive dynamic control effect of different dimensions of crop transportation and storage. Description of the Drawings

[0046] The following further describes the present invention with reference to the drawings.

[0047] Figure 1 It is a module block diagram of an intelligent control system for crop transportation and storage according to the present invention.

[0048] Figure 2 It is a step block diagram for implementing an intelligent control system for crop transportation and storage according to the present invention.

[0049] Figure 3 It is a block diagram of the steps for determining the local supervision status of the grain weight dimension corresponding to different target crops in the present invention.

[0050] Figure 4 It is a block diagram of the steps for determining the local supervision status of the grain quality dimension corresponding to the target crop in the present invention.

[0051] Figure 5 It is a block diagram of the steps for dynamically marking the identities of different target crops in the present invention. Specific Embodiments

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying 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 making creative efforts belong to the scope of protection of the present invention.

[0053] As Figures 1 to 2 shown, the present invention is an intelligent control system for crop transportation and storage, including a transportation and storage control platform, and a crop transportation and storage supervision processing module and a crop transportation and storage supervision control module that are communicatively connected to the transportation and storage control platform;

[0054] The crop transportation and storage supervision processing module is used to monitor the transportation and storage links of different target crops throughout the process and perform data statistics respectively, and perform digital processing and monitoring status analysis on the data monitored and statistically analyzed in different links to obtain the first supervision processing data and the second supervision processing data corresponding to different dimensions of different target crops and upload them to the transportation and storage control platform in real time; including:

[0055] Number and mark different target crops for transportation and storage as i, i = {1, 2, 3,..., N}; N is a positive integer representing the total number of target crops for transportation and storage;

[0056] Among them, the crop can specifically be paddy rice; the target crop is a truckload of paddy rice transported by a transport vehicle;

[0057] Obtain the initial grain weight LZi of different target crops before the start of transportation, and the first grain monitoring weight LJi1 and the second grain monitoring weight LJi2 corresponding to different target crops after the end of the transportation link and the storage link;

[0058] It should be noted that there are losses in the grain weight during the transportation and storage of the target crops, and the single grain loss specified in the existing regulations generally does not exceed five ten-thousandths at most; in the embodiments of the present invention, by statistically processing the grain weight monitoring data corresponding to different target crops before the start of transportation and after the end of the transportation and storage links, it can provide reliable data support for the analysis of the grain weight supervision status of different target crops in different subsequent links; in addition, the weighing of different target crops corresponding to the grain weight can be realized based on the existing weighbridge.

[0059] When analyzing and processing the grain weight supervision status of different target crops in the transportation and storage links according to the first grain monitoring weight and the second grain monitoring weight obtained by monitoring, the first grain monitoring weight and the second grain monitoring weight corresponding to different target crops are input into the grain weight monitoring and recognition function for data analysis, and the grain weight supervision identifier ZJik corresponding to the transportation and storage links is output.

[0060] Among them, the expression of the grain weight monitoring and recognition function is ; in the formula, k = 1, 2, representing different links; LJik is LJi1, LJi2; Wik is Wi1, Wi2, which are the transportation weight standard error and storage weight standard error of different target crops corresponding to the transportation and storage links respectively, and can be determined according to the existing transportation and storage data.

[0061] It should be noted that the grain weight supervision identifier is used to perform data calculation and digital representation on the grain weight supervision status corresponding to different target crops after different links.

[0062] The grain weight supervision identifiers corresponding to the transportation and storage links both contain values of 0 or 1.

[0063] The grain weight supervision identifier with a value of 0 indicates that the grain weight monitoring status of the corresponding link is normal.

[0064] The grain weight supervision identifier with a value of 1 indicates that the grain weight monitoring status of the corresponding link is abnormal.

[0065] Sort and combine the grain weight supervision identifiers obtained by processing the target crops corresponding to the transportation and storage links to obtain the grain weight supervision processing sequence.

[0066] In the embodiments of the present invention, by processing, calculating and combining the grain weight monitoring data of different target crops corresponding to different links, the grain weight supervision processing sequences corresponding to different target crops are obtained. While realizing the monitoring and processing of the data in the grain weight dimension, it can also provide reliable data support for the subsequent analysis of the local supervision status of the grain weight dimension corresponding to different target crops.

[0067] As Figure 3 shown, when analyzing the data of the grain weight supervision processing sequence to determine the local supervision status of the grain weight dimension corresponding to the target crop;

[0068] Traverse and analyze the grain weight supervision processing sequence;

[0069] If the element values in the grain weight supervision processing sequence are all 0, generate a weight supervision normal label and mark the grain weight supervision status of the associated target crop as normal;

[0070] If there is an element with a value of 1 in the grain weight supervision processing sequence, generate a weight supervision abnormal label and mark the grain weight supervision status of the associated target crop as abnormal;

[0071] Sort and combine the grain weight supervision processing sequence corresponding to the target crop, the obtained weight supervision normal label or weight supervision abnormal label to obtain the first supervision processing data corresponding to the target crop;

[0072] In the embodiments of the present invention, by analyzing the data of the grain weight supervision processing sequence of different target crops corresponding to the processing combinations from the grain weight dimension, the grain weight supervision status of different target crops is obtained, and at the same time, it can also provide reliable local grain weight analysis data support for the overall supervision status analysis of the overall dimension corresponding to all subsequent abnormal target crops.

[0073] And, monitor and obtain the internal temperature distribution maps of the grain stacks in the transportation and storage links corresponding to different target crops, and obtain the monitoring temperature values corresponding to different monitoring points in the internal temperature distribution maps of different links; sort and combine the monitoring temperature values of the monitoring points corresponding to different links respectively to obtain the monitoring temperature sequence [wjk] corresponding to the link; where j is different monitoring points in the internal temperature distribution map, j = {1, 2, 3,..., m}; m is a positive integer representing the total number of all monitoring points in the internal temperature distribution map; [wj1] and [wj2] are the monitoring temperature sequences corresponding to the grain stacks in the transportation and storage links respectively;

[0074] It should be noted that infrared imaging technology can detect the temperature distribution inside the grain stack, and abnormal hot spots may be signs of mildew or pests; the acquisition of the internal temperature distribution maps of the grain stacks in the transportation and storage links corresponding to the target crops is achieved through existing conventional technical means. For example, using infrared thermal imaging technology to detect the temperature distribution of different preset monitoring points inside the grain stack and performing data processing on the temperature distribution of different preset monitoring points to obtain the corresponding internal temperature distribution map. The specific data processing steps are not elaborated here;

[0075] In addition, the positions of all preset monitoring points corresponding to the transportation link of the target crop and the grain stack in the storage link are the same; for the settings of different preset monitoring points inside the grain stack, hot spots or problem areas found in previous monitoring can be used as monitoring points; monitoring points are set at different heights and different orientations of the grain stack; additional monitoring points are set at places where heat is likely to accumulate, such as the center and bottom of the stack, and the settings of other monitoring points can be customized according to the actual application scenario;

[0076] When performing data analysis on the grain quality supervision status corresponding to different target crops according to the monitored temperature sequences of the grain stacks in the transportation link and the storage link, all the element values in the monitored temperature sequences of the grain stacks in the transportation link and the storage link corresponding to different target crops are sequentially analyzed through the grain quality monitoring and identification function, and the grain quality supervision identification ZBjk corresponding to different elements in the monitored temperature sequence is output;

[0077] Among them, the expression of the grain quality monitoring and identification function is ; in the formula, Ujk is Uj1 and Uj2, which are respectively the standard temperature ranges corresponding to different monitoring points of the grain stacks in the transportation link and the storage link of the target crop. The standard temperature ranges corresponding to different monitoring points can be determined according to the actual ambient temperature and the surface temperature of the grain stack, or can be determined according to the historical monitoring temperature ranges corresponding to the same monitoring points;

[0078] It should be noted that the grain quality supervision identification is used for data calculation and digital representation of the grain quality supervision status of different target crops corresponding to the grain stacks in different links;

[0079] The grain quality supervision identification contains values of 0 or 1;

[0080] A grain quality supervision identification with a value of 0 indicates that the monitored temperature status of the monitoring point corresponding to the grain stack in the transportation link or the storage link of the target crop is normal;

[0081] A grain quality supervision identification with a value of 1 indicates that the monitored temperature status of the monitoring point corresponding to the grain stack in the transportation link or the storage link of the target crop is abnormal;

[0082] The different grain quality supervision identifications obtained by processing the monitored temperature sequences of the grain stacks in the transportation link and the storage link corresponding to the target crop are sorted and combined respectively to obtain the first grain quality supervision processing sequence and the second grain quality supervision processing sequence corresponding to the corresponding links;

[0083] In the embodiments of the present invention, by processing, calculating and combining the grain quality monitoring data corresponding to different target crops in different links, the first grain quality supervision processing sequence and the second grain quality supervision processing sequence corresponding to different target crops are obtained. While realizing the monitoring and processing of data in the dimension of grain quality, it can also provide reliable data support for the subsequent analysis of the local supervision status of the grain quality dimension corresponding to different target crops;

[0084] As Figure 4 shown, when analyzing the data of the first grain quality supervision processing sequence and the second grain quality supervision processing sequence to determine the local supervision status of the grain quality dimension corresponding to the target crop;

[0085] If the element values in both the first grain quality supervision processing sequence and the second grain quality supervision processing sequence are 0, a quality supervision normal label is generated and the mark of normal grain quality supervision status is associated with the target crop;

[0086] If there is an element with a value of 1 in the first grain quality supervision processing sequence and / or the second grain quality supervision processing sequence, a quality supervision abnormal label is generated and the mark of abnormal grain quality supervision status is associated with the target crop;

[0087] The first grain quality supervision processing sequence and the second grain quality supervision processing sequence corresponding to the target crop, and the obtained quality supervision normal label or quality supervision abnormal label are analyzed to obtain the second supervision processing data corresponding to the target crop;

[0088] In the embodiments of the present invention, by respectively analyzing the data of the first grain quality supervision processing sequence and the second grain quality supervision processing sequence corresponding to different target crops in the grain quality dimension, the grain quality supervision status corresponding to different target crops is obtained. At the same time, it can also provide reliable local grain quality analysis data support for the subsequent analysis of the overall supervision status of the overall dimension corresponding to all abnormal target crops.

[0089] The crop transportation and storage supervision control module is used to integrate and calculate the local abnormal supervision data obtained from different dimensions of the transportation link and the storage link corresponding to different target crops, and analyze the overall supervision status of the overall dimension, and adaptively implement targeted optimization control on the transportation link and the storage link corresponding to the target crop according to the analysis result of the overall supervision status of the overall dimension; including:

[0090] As Figure 5 shown, the identity of the target crop is dynamically marked according to the first supervision processing data and the second supervision processing data corresponding to different target crops;

[0091] If neither the weight supervision exception label nor the quality supervision exception label exists in the first supervision processing data and the second supervision processing data, an overall normal label is generated and the target crop to which it belongs is marked as a conventional target crop;

[0092] Conversely, an overall exception label is generated and the target crop to which it belongs is marked as a special target crop. The total number N1 of weight supervision exceptions and the total number N2 of quality supervision exceptions corresponding to all special target crops are counted, and through the overall supervision formula the overall exception degree Zy corresponding to all special target crops is calculated and obtained; in the formula, α and β are different weight coefficients, 0 < α < β, and the specific values can be determined according to the actual design requirement data of the actual application scenario. The specific values are not limited, as long as they meet the condition requirements corresponding to the weight coefficients; N is the total number of all target crop transports and storages under supervision;

[0093] It should be noted that the overall exception degree is used to integrally calculate all the exception supervision data corresponding to different special target crops to digitally represent the overall supervision status of different special target crops from an overall dimension;

[0094] When analyzing the data of the overall exception degree to determine the overall supervision status in the overall dimension, through the overall exception status formula the overall exception status identifier Zb corresponding to all special target crops is calculated and obtained and analyzed; in the formula, A is the overall exception status standard value, which can be determined according to the overall design requirement data of the existing crop corresponding transports and storages; [] is the rounding function;

[0095] If the overall exception status identifier is 0, an overall mild exception status is generated and a prompt is given; specific control measures may not be taken here;

[0096] If the overall exception status identifier is not 0, an overall severe exception status is generated and a prompt is given, and at the same time, comprehensive supervision and rectification are implemented for the subsequent transportation and storage links of the target crops;

[0097] Among them, comprehensive supervision and rectification are implemented for the transportation and storage links, including optimizing the supervision measures corresponding to the grain weight and grain quality in different links, such as adding supervision measures and preventive measures in different aspects.

[0098] In the embodiments of the present invention, the identities of different target crops are dynamically analyzed and marked by using the local supervision analysis data in different dimensions in the early stage, and the overall supervision status of all special target crops obtained by analysis is digitally processed and analyzed, and targeted supervision and rectification are adaptively implemented for different links of different target crops according to the analysis results, improving the local supervision effects in different dimensions of crop transportation and storage and the adaptive dynamic control effects.

[0099] In addition, the formulas involved above are all calculated by removing the dimension and taking their numerical values, and are obtained by collecting a large amount of data and simulating through simulation software to get a formula that is closest to the actual situation.

[0100] In several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described invention embodiments are merely illustrative. For example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0101] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] In addition, in each embodiment of the present invention, the functional modules can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0103] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0104] 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 them. 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 solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent control system for crop transportation and storage, characterized in that: include: The crop transportation and storage supervision and processing module is used to monitor and collect data on the transportation and storage links of different target crops, and to perform digital processing and monitoring status analysis on the data monitored and collected in different links in different dimensions, so as to obtain the first supervision and processing data and the second supervision and processing data of different dimensions corresponding to different target crops and upload them to the transportation and storage control platform in real time; Among them, according to the first grain monitoring weight and the second grain monitoring weight obtained by monitoring the transportation link and the storage link of the target crop, the grain weight supervision status of the transportation link and the storage link corresponding to different target crops is digitally processed and combined respectively to obtain the first supervision processing data corresponding to the different target crops; Also, monitoring and obtaining internal temperature distribution diagrams of grain piles corresponding to the transportation link and storage link of different target crops, and digitally processing and combining the monitoring temperature values ​​corresponding to different monitoring points in the internal temperature distribution diagrams of different links to obtain the first grain quality supervision processing sequence and the second grain quality supervision processing sequence of the corresponding links; Perform data analysis on the first grain quality supervision processing sequence and the second grain quality supervision processing sequence to obtain a normal quality supervision label or an abnormal quality supervision label corresponding to the grain quality dimension of the target crop; The first grain quality supervision processing sequence and the second grain quality supervision processing sequence corresponding to the target crop are analyzed and the obtained normal quality supervision label or abnormal quality supervision label are obtained to obtain the second supervision processing data corresponding to the target crop; The crop transportation and storage supervision and control module is used to integrate and calculate the local abnormal supervision data obtained from different dimensions of the transportation and storage links corresponding to different target crops, and analyze the overall supervision status of the overall dimension, and adaptively implement targeted optimization control on the transportation and storage links corresponding to the target crops according to the overall supervision status analysis results of the overall dimension; The identities of the target crops are dynamically marked according to the first supervision processing data and the second supervision processing data corresponding to the different target crops; If there are no weight supervision abnormality labels and quality supervision abnormality labels in the first supervision processing data and the second supervision processing data, an overall normal label is generated and the target crop is marked as a regular target crop; Otherwise, an overall abnormal label is generated and the target crop is marked as a special target crop; Count the total number of weight supervision anomalies N1 and quality supervision anomalies N2 corresponding to all special target crops, and use the overall supervision formula Calculate and obtain the overall abnormality Zy corresponding to all special target crops; where α and β are different weight coefficients, 0<α<β; N is the total number of all target crops transported and stored under supervision; When analyzing the overall abnormality to determine the overall supervision status of the overall dimension, the overall abnormal status formula is used Calculate and analyze the overall abnormal state identification Zb corresponding to all special target crops; where A is the standard value of the overall abnormal state; [] is the rounding function; If the overall abnormal status indicator is 0, an overall mild abnormal status is generated and prompted; If the overall abnormal status flag is not 0, an overall severe abnormal status will be generated and a prompt will be given. At the same time, the transportation and storage links of subsequent target crops will be controlled to implement comprehensive supervision and rectification.

2. The intelligent control system for crop transportation and storage according to claim 1 is characterized in that: The target crops for different transportation and storage are numbered and marked as i, i={1, 2, 3, ..., N}; N is a positive integer; Obtain the initial weight LZi of grain before the transportation of different target crops, as well as the first grain monitoring weight LJi1 and the second grain monitoring weight LJi2 corresponding to the transportation and storage stages of different target crops; When processing and analyzing the grain weight supervision status of the transportation link and the storage link corresponding to different target crops according to the first grain monitoring weight and the second grain monitoring weight obtained by monitoring, the first grain monitoring weight and the second grain monitoring weight corresponding to the different target crops are input into the grain weight monitoring identification function for data analysis, and the grain weight supervision identification ZJik corresponding to the transportation link and the storage link is output; The food weight supervision marks corresponding to the transportation and storage stages all contain values ​​of 0 or 1; A grain weight supervision mark with a value of 0 indicates that the grain weight monitoring status of the corresponding link is normal; A grain weight supervision mark with a value of 1 indicates that the grain weight monitoring status of the corresponding link is abnormal; The grain weight supervision identifications obtained from the transportation and storage stages of the target crops are sorted and combined to obtain a grain weight supervision processing sequence.

3. The intelligent control system for crop transportation and storage according to claim 2 is characterized in that: The expression of the grain weight monitoring identification function is: ; In the formula, k=1, 2, representing different links; LJik is LJi1, LJi2; Wik is Wi1 and Wi2, which are the standard error of transportation weight and storage weight of different target crops in the corresponding transportation link and storage link respectively.

4. The intelligent control system for crop transportation and storage according to claim 2 is characterized in that: When data analysis is performed on the grain weight supervision processing sequence to determine the local supervision status of the target crops corresponding to the grain weight dimension; Conduct traversal analysis on grain weight supervision processing sequence; If the values ​​of the elements in the grain weight supervision processing sequence are all 0, a normal weight supervision label is generated and the target crop is associated with a normal grain weight supervision status label; If there is an element with a value of 1 in the grain weight supervision processing sequence, a weight supervision abnormality label is generated and the target crop is associated with a mark indicating that the grain weight supervision status is abnormal.

5. The intelligent control system for crop transportation and storage according to claim 4 is characterized in that: Monitor and obtain the internal temperature distribution diagrams of grain stacks in the transportation link and storage link corresponding to different target crops, and obtain the monitoring temperature values ​​corresponding to different monitoring points in the internal temperature distribution diagrams of different links; sort and combine the monitoring temperature values ​​of the corresponding monitoring points in different links to obtain the monitoring temperature sequence [wjk] of the corresponding link; where j is the different monitoring points in the internal temperature distribution diagram, j={1, 2, 3, ..., m}; m is a positive integer; [wj1] and [wj2] are the monitoring temperature sequences corresponding to the grain stacks in the transportation link and the storage link, respectively; When performing data analysis on the grain quality supervision status corresponding to different target crops according to the monitoring temperature sequence of grain stacking in the transportation link and storage link, all element values ​​of the monitoring temperature sequence of grain stacking in the transportation link and storage link corresponding to different target crops are sequentially analyzed through the grain quality monitoring identification function, and the grain quality supervision identification ZBjk corresponding to different elements in the monitoring temperature sequence is output; Grain quality control labels contain values ​​of 0 or 1; A grain quality supervision mark with a value of 0 indicates that the monitoring temperature status of the corresponding monitoring point of the grain stack in the transportation link or storage link of the target crop is normal; A grain quality supervision mark with a value of 1 indicates that the monitoring temperature status of the corresponding monitoring point of the grain stack in the transportation link or storage link of the target crop is abnormal; Different grain quality supervision identifications obtained by processing the monitored temperature sequences of grain stacks in the transportation and storage stages of target crops are sorted and combined to obtain the first grain quality supervision processing sequence and the second grain quality supervision processing sequence of the corresponding stages.

6. The intelligent control system for crop transportation and storage according to claim 5 is characterized in that: The expression of the grain quality monitoring identification function is: ; In the formula, Ujk is Uj1 and Uj2, which are the monitoring temperature standard ranges corresponding to different monitoring points of grain stacking in the transportation link and storage link of the target crops.

7. The intelligent control system for crop transportation and storage according to claim 5 is characterized in that: When the data of the first grain quality supervision processing sequence and the second grain quality supervision processing sequence are analyzed to determine the local supervision status of the grain quality dimension corresponding to the target crops; If the values ​​of the elements in the first grain quality supervision processing sequence and the second grain quality supervision processing sequence are both 0, a normal quality supervision label is generated and the target crop is associated with a normal grain quality supervision status label; If there is an element with a value of 1 in the first grain quality supervision processing sequence and / or the second grain quality supervision processing sequence, a quality supervision abnormality label is generated and a mark indicating an abnormal grain quality supervision status is associated with the target crop.

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