Temperature monitoring method and system of drying system, electronic equipment and storage medium

Abstract

The invention discloses a temperature monitoring method and system for a drying system, electronic equipment and a storage medium, and relates to the technical field of drying system monitoring, and the method comprises the steps: carrying out the correlation degree analysis of the temperature value of each preset position of a reference drying system, and determining a plurality of target positions according to the correlation degree analysis result; temperature acquisition is carried out by setting a temperature sensor at each target position of the target drying system, and the temperature value of each non-target position of the target drying system is reversely deduced according to the temperature value of each target position; according to the method, the temperature values of all the preset positions can be monitored only by installing the temperature sensors at all the target positions, on one hand, operation such as punching or groove forming in equipment of the drying system can be reduced, the operation stability of the drying system is guaranteed, and on the other hand, the temperature values of all the preset positions can be monitored. The interference on the optimization design of the drying system can be effectively reduced, and the cost can be effectively reduced.

Description

Background Art

[0002] A drying system is a system device for dehydrating and drying slurries. For example, the drying system can be used to dehydrate and dry slurries such as cellulose, ammonium sulfate, ammonium chloride, and mirabilite. Currently, a relatively large number of temperature sensors are installed in the drying system to monitor the temperatures at multiple positions of the drying system, enabling timely detection of abnormal temperature conditions. On the one hand, in order to better fix the temperature sensors, holes or grooves are often drilled or formed on the equipment of the drying system, which also affects the operating stability of the drying system; on the other hand, when arranging temperature sensors at multiple positions, it may affect the optimal design of the drying system, and moreover, a relatively large number of temperature sensors will also increase a relatively large cost. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a temperature monitoring method, system, electronic device, and storage medium for a drying system in view of the deficiencies of the prior art, specifically as follows:

[0004] 1) In the first aspect, the present invention provides a temperature monitoring method for a drying system, and the specific technical solution is as follows:

[0005] Analyze the degree of association of the temperature values at each preset position of the reference drying system, and determine multiple target positions according to the analysis result of the degree of association;

[0006] Collect temperatures by setting temperature sensors at each target position of the target drying system, and reverse-infer the temperature values at each non-target position of the target drying system according to the temperature values at each target position;

[0007] Among them, the reference drying system and the target drying system are two identical drying systems.

[0008] The beneficial effects of the temperature monitoring method for a drying system provided by the present invention are as follows:

[0009] Only by installing temperature sensors at each target position can the temperature values at all preset positions be monitored. On the one hand, it can reduce operations such as drilling holes or forming grooves on the equipment of the drying system, ensuring the operating stability of the drying system; on the other hand, it can effectively reduce the interference caused to the optimal design of the drying system, and moreover, it can effectively reduce costs.

[0010] Based on the above solution, the temperature monitoring method for a drying system of the present invention can be further improved as follows.

[0011] Further, analyzing the degree of association of the temperature values at each preset position of the reference drying system and determining multiple target positions according to the analysis result of the degree of association includes:

[0012] Obtain the temperature values at each preset position of the reference drying system within a preset time period to obtain multiple time-temperature sequence data;

[0013] Perform similarity analysis on the multiple time-temperature sequence data. According to the similarity analysis results, divide all preset positions into multiple groups. Among them, the similarity between the time-temperature sequence data of every two preset positions within the same group exceeds a preset similarity threshold, and the similarity characterizes the degree of association;

[0014] Select one preset position in each group as the target position.

[0015] The beneficial effect of adopting the above further solution is that since the similarity between the time-temperature sequence data of every two preset positions within the same group exceeds the preset similarity threshold, it indicates that the degree of association between the temperature values of every two preset positions within the same group is high. Therefore, by selecting one preset position in each group as the target position, the temperature value of each non-target position in the group where the target position is located can be accurately deduced through any target position, ensuring the accuracy of temperature monitoring.

[0016] Further, according to the temperature value of each target position, deduce the temperature value of each non-target position of the target drying system, including:

[0017] Based on the time-temperature sequence data of each preset position in each group, deduce the temperature value of each non-target position of the target drying system.

[0018] The beneficial effect of adopting the above further solution is that based on the functional relationship between the time-temperature sequence data of each preset position in each group and the corresponding fitting curve, the accuracy of the temperature value of each non-target position of the target drying system calculated can be guaranteed.

[0019] Further, it further includes: determining whether to send a warning message according to the temperature value of each target position and the temperature value of each non-target position of the target drying system.

[0020] The beneficial effect of adopting the above further solution is that when a temperature anomaly occurs, a warning message can be sent in a timely manner for the operation and maintenance personnel to handle in a timely manner.

[0021] 2) In the second aspect, the present invention also provides a temperature monitoring system for a drying system, and the specific technical solution is as follows:

[0022] It includes a target position determination module and a temperature deduction module;

[0023] The target position determination module is used for: analyzing the degree of association of the temperature values of each preset position of the reference drying system, and determining multiple target positions according to the degree of association analysis results;

[0024] The temperature back - calculation module is used for: collecting temperature through temperature sensors set at each target position of the target drying system, and back - calculating the temperature values of each non - target position of the target drying system according to the temperature values of each target position;

[0025] Wherein, the reference drying system and the target drying system are two identical drying systems.

[0026] Based on the above - mentioned solution, the temperature monitoring system of a drying system according to the present invention can also be improved as follows.

[0027] Further, the target position determination module is specifically used for:

[0028] Obtaining the temperature values of each preset position of the reference drying system within a preset time period to obtain a plurality of time - temperature sequence data;

[0029] Performing similarity analysis on the plurality of time - temperature sequence data, and dividing all preset positions into multiple groups according to the similarity analysis result. Among them, the similarity between the time - temperature sequence data of every two preset positions within the same group exceeds a preset similarity threshold, and the similarity represents the degree of association;

[0030] Selecting one preset position from each group as the target position.

[0031] Further, the temperature back - calculation module is specifically used for: based on the time - temperature sequence data of each preset position in each group, back - calculating the temperature values of each non - target position of the target drying system.

[0032] Further, it further includes an early - warning module, and the early - warning module is used for: determining whether to send out an early - warning message according to the temperature values of each target position and each non - target position of the target drying system.

[0033] 3) In a third aspect, the present invention also provides an electronic device, which includes a processor. The processor is coupled to a memory, and at least one computer program is stored in the memory. The at least one computer program is loaded and executed by the processor so that the electronic device implements the temperature monitoring method of any one of the above - mentioned drying systems.

[0034] 4) In a fourth aspect, the present invention also provides a computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the temperature monitoring method of any one of the above - mentioned drying systems.

[0035] It should be noted that for the beneficial effects obtained by the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementation manners, reference can be made to the technical effects of the first aspect and its corresponding possible implementation manners described above, and details will not be repeated here. Description of the Drawings

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments of the present invention:

[0037] Figure 1 It is a schematic flowchart of a temperature monitoring method for a drying system according to an embodiment of the present invention;

[0038] Figure 2 It is a schematic structural diagram of a temperature monitoring system for a drying system according to an embodiment of the present invention;

[0039] Figure 3 It is a schematic structural diagram of an electronic device according to an embodiment of the present invention. Specific Embodiments

[0040] The following describes the principles and features of the present invention. The examples given are only used to explain the present invention and are not intended to limit the scope of the present invention.

[0041] The following uses specific embodiments to detail the technical solutions of the present invention and how the technical solutions of the present invention solve the above technical problems. These several specific embodiments can be combined with each other. For the same or similar concepts or processes, they may not be repeated in some embodiments. The following will describe the embodiments of the present invention in conjunction with the drawings.

[0042] As Figure 1 shown, a temperature monitoring method for a drying system according to an embodiment of the present invention includes the following steps:

[0043] S1. Analyze the correlation degree of the temperature values at each preset position of the reference drying system, and determine multiple target positions according to the correlation degree analysis results;

[0044] Among them, the drying system includes equipment such as a feeding device, an air chamber, a steam device, a heating device, a fan, a condenser, an evaporator, and a boiling bed or a fluidized bed arranged in the air chamber.

[0045] Among them, the heat transfer medium of the boiling bed in the drying system can be air, nitrogen, carbon dioxide, water, glycerol, ethylene glycol, insulating oil, or heat transfer oil, etc., and the heat transfer medium of the fluidized bed can be air, nitrogen, carbon dioxide, water, or heat transfer oil, etc.

[0046] Among them, the preset positions can be set according to experience. For example, multiple preset positions are determined on the boiling bed or the fluidized bed, multiple preset positions are determined at the center or each side of the air chamber, and multiple preset devices are determined on the heating device, etc.

[0047] S2. Collect the temperature by setting temperature sensors at each target position of the target drying system, and based on the temperature values of each target position, inversely deduce the temperature values of each non-target position of the target drying system;

[0048] Among them, the reference drying system and the target drying system are two identical drying systems. It should be noted that the difference between the reference drying system and the target drying system is the number of temperature sensors set. The reference drying system is: the drying system used for experiments to determine multiple target positions, and the target drying system is: the actually used drying system.

[0049] Optionally, in S1, perform an association degree analysis on the temperature values of each preset position of the reference drying system, and based on the association degree analysis result, determine multiple target positions, including:

[0050] S10. Obtain the temperature values of each preset position of the reference drying system within a preset time period to obtain multiple time-temperature sequence data;

[0051] Among them, specifically, by setting temperature sensors (denoted as the first temperature sensors) at each preset position of the reference drying system respectively, collect the temperature values of each preset position of the reference drying system within a preset time period. The collection frequencies of all the first temperature sensors are the same, and the specific value of the collection frequency can be set according to the actual situation.

[0052] Among them, the start time and end time of the preset time period can be set according to the actual situation, and the duration of the preset time period can also be set according to the actual situation. For example, the duration of the preset time period is 1 hour or 2 hours, etc.

[0053] Among them, the temperature values collected by each first temperature sensor within the preset time period respectively form a time-temperature sequence data.

[0054] S11. Perform a similarity analysis on the multiple time-temperature sequence data, and based on the similarity analysis result, divide all the preset positions into multiple groups. Among them, the similarity between the time-temperature sequence data of every two preset positions within the same group exceeds the preset similarity threshold, and the similarity characterizes the association degree;

[0055] Among them, the similarity analysis of any two time-temperature sequence data can be performed in the following way. For the convenience of description, any two time-temperature sequence data are respectively denoted as the first time-temperature sequence data and the second time-temperature sequence data, then:

[0056] 1) The first similarity calculation method:

[0057] Perform Fourier transforms on the first-time temperature sequence data and the second-time temperature sequence data respectively to obtain the Fourier transform curves corresponding to the first-time temperature sequence data and the Fourier transform curves corresponding to the second-time temperature sequence data. Select multiple peak points from the Fourier transform curves corresponding to the first-time temperature sequence data and the Fourier transform curves corresponding to the second-time temperature sequence data respectively, and obtain the abscissa and ordinate of each peak point. Then calculate the similarity of the two sets of peak points as the similarity between the first-time temperature sequence data and the second-time temperature sequence data. This similarity can specifically be the cosine similarity or other types of similarity. Through the two sets of peak points, the computational amount can be greatly reduced and the computational efficiency can be improved.

[0058] 2) The second similarity calculation method:

[0059] Perform curve fitting on the first-time temperature sequence data and the second-time temperature sequence data respectively. Place the curve corresponding to the first-time temperature sequence data and the curve corresponding to the second-time temperature sequence data in one layer respectively to obtain the first image and the second image. The first image includes the curve corresponding to the first-time temperature sequence data, and the second image includes the curve corresponding to the second-time temperature sequence data. Calculate the similarity between the first image and the second image, and use the similarity between the first image and the second image as the similarity between the first-time temperature sequence data and the second-time temperature sequence data.

[0060] It should be noted that the similarities calculated by the above first similarity calculation method and the second similarity calculation method refer to the similarity between the data change trends of the first-time temperature sequence data and the second-time temperature sequence data, rather than the similarity of the data sizes. The advantage is that the change trends of the temperature values at each preset position in each group tend to be consistent, so as to facilitate inferring the temperature values at other preset positions in the group where a preset position is located from the temperature value at any preset position.

[0061] Through the above first similarity calculation method or the second similarity calculation method, similarity analysis can be performed on every two time temperature sequence data to obtain a similarity analysis result. The similarity analysis result includes the similarity between every two time temperature sequence data.

[0062] Among them, the preset similarity threshold can be set according to the actual situation. For example, the preset similarity threshold can be 0.8, etc.

[0063] S12. Select a preset position in each group as the target position respectively.

[0064] Among them, a preset position can be randomly selected in each group as the target position, and the other preset positions are non-target positions.

[0065] Among S10 to S12, the degree of association is characterized by similarity. Then, performing similarity analysis on multiple time-temperature series data is to perform an analysis of the degree of association on the temperature values at each preset position of the reference drying system, and the similarity analysis result is the degree of association analysis result.

[0066] Optionally, in S2, according to the temperature values at each target position, the temperature values at each non-target position of the target drying system are deduced, including:

[0067] Based on the time-temperature series data at each preset position in each group, the temperature values at each non-target position of the target drying system are deduced. The specific implementation process is as follows:

[0068] S20. Fit the time-temperature series data at each preset position in each group respectively to obtain the corresponding fitting curves for each group;

[0069] Among them, the time-temperature series data at each preset position in each group are subjected to data cleaning (removing noise and outliers), and then the polynomial regression method, nonlinear regression method, spline interpolation method or machine learning algorithm (such as support vector machine or neural network, etc.) is used to fit the time-temperature series data at each preset position in each group to obtain the corresponding fitting curves for each group.

[0070] S21. Construct the functional relationship between the temperature value at each preset position in each group and the temperature value in the corresponding fitting curve respectively according to the time-temperature series data at each preset position in each group and the corresponding fitting curve.

[0071] S22. Determine the temperature values at each non-target position of the target drying system according to the temperature values at each target position and in combination with the corresponding functional relationship and fitting curve. Specifically:

[0072] After obtaining the temperature value of any target position, according to the functional relationship between the temperature value of this target position and the temperature value in the corresponding fitting curve, the temperature value in the fitting curve corresponding to the temperature value of this target position can be calculated. Then, using the functional relationship between each non-target position in the group where this target position is located and the temperature value in the corresponding fitting curve respectively, the temperature values of each non-target position in the group where this target position is located can be calculated until the temperature values of each non-target position of the target drying system are calculated.

[0073] Optionally, in the above technical solution, it further includes:

[0074] S3. Determine whether to send a warning message according to the temperature values at each target position and each non-target position of the target drying system. The specific implementation can be carried out in the following way:

[0075] 1) The first implementation method:

[0076] Set a temperature range for each target position and each non-target position respectively. When the temperature value of any target position or non-target position is not within the corresponding temperature range, a warning message of abnormal temperature is issued.

[0077] 2) The second implementation method:

[0078] Set a temperature range for each target position and each non-target position respectively. When consecutive multiple temperature values of any target position or non-target position are no longer within the corresponding temperature range, a warning message of abnormal temperature is issued, which can effectively reduce the misjudgment rate.

[0079] 3) The third implementation method:

[0080] Set a temperature range for each target position and each non-target position respectively, and determine multiple sub-ranges outside each temperature range. Set a type of warning message for each sub-range. According to the range where the temperature value of each target position and the temperature value of each non-target position are located, determine the warning message to be issued.

[0081] Optionally, in the above technical solution, use each target position and each non-target position as nodes (for easy distinction, denoted as the first nodes). In each group, set an edge between the target position and each non-target position (for easy distinction, denoted as the first edge) to construct a topological structure, and associate the corresponding position information with each first node in the topological structure. The position information includes: three-dimensional structure diagram, material, etc. After the warning message of abnormal temperature is issued, in this atlas structure, distinguish and display the first node (target position or non-target position) corresponding to this warning message, and distinguish and display each node associated with this first node (if there is a first edge connected between two nodes, it means there is an association relationship between these two first nodes). Specifically, it can be highlighted or distinguished by color display. When clicking on this first node, the position information pops up to facilitate the operation and maintenance personnel to view and improve the processing effect.

[0082] Optionally, construct a three-dimensional structure model of the drying system, associate the temperature values of each target position and each non-target position collected with the corresponding positions in the three-dimensional structure model, and distinguish them with different colors according to the range to which the temperature values belong to obtain a three-dimensional temperature distribution cloud map, where multiple temperature value ranges are pre-divided and corresponding colors are set.

[0083] Optionally, in the above technical solution, it further includes:

[0084] S101. When an operation and maintenance personnel views the three-dimensional temperature distribution cloud map and issues a request to obtain and save the three-dimensional temperature distribution cloud map, the coordinates (x-axis coordinate value, y-axis coordinate value, and z-axis coordinate value) of any preset position in the three-dimensional structure model and the temperature value of the preset position are combined into an array. The format of the array is: (x-axis coordinate value, y-axis coordinate value, z-axis coordinate value, temperature value). Until the arrays corresponding to each preset position are obtained, the arrays corresponding to the target positions in each group and the arrays corresponding to each non-target position are used as nodes (denoted as the second nodes). An edge (denoted as the second edge) is connected between every two second nodes corresponding to each group. Thus, an undirected graph corresponding to each group (for easy distinction, denoted as the first undirected graph) is obtained, and the adjacency matrix of each first undirected graph is generated.

[0085] S102. Use the adjacency matrix corresponding to each group as the perturbation of each array in the corresponding group. Specifically, all elements in each adjacency matrix are sequentially divided into 4 parts. The number of elements in the 4 parts can be the same or different. The 4 parts correspond one by one to each data (x-axis coordinate value, y-axis coordinate value, z-axis coordinate value, and temperature value) in each corresponding array. The sum of the products of each part and the corresponding data is used as the new data until the new data of each data in each array is obtained.

[0086] S103. Send each new data in each array to the cloud, and send the three-dimensional temperature distribution cloud map to the cloud (specifically, it can be a cloud server). The cloud verifies the identity information provided by the operation and maintenance personnel. When the verification is passed, the cloud sends each new data in each array to the intelligent terminal of the operation and maintenance personnel, and sends the three-dimensional temperature distribution cloud map to the intelligent terminal of the operation and maintenance personnel.

[0087] S104. Send the adjacency matrix corresponding to each group and the corresponding relationship between the adjacency matrix and the group to the intelligent terminal of the operation and maintenance personnel, so that the intelligent terminal of the operation and maintenance personnel constructs equations according to the received adjacency matrix corresponding to each group and each new data in each array, calculates each data (x-axis coordinate value, y-axis coordinate value, z-axis coordinate value, and temperature value) in each array, and then associates each data (x-axis coordinate value, y-axis coordinate value, z-axis coordinate value, and temperature value) in each array to the three-dimensional temperature distribution cloud map, and reconstructs the three-dimensional temperature distribution cloud map.

[0088] Through S101 to S104, without uploading real data to the cloud, data leakage can be effectively prevented and data security can be guaranteed. Moreover, in the form of an adjacency matrix, each data in each array is encrypted. The data calculation amount is small, and the calculation amount for reverse inferring each data in each array is small. And there has never been a technology using an adjacency matrix for data encryption in the prior art, which can further improve data security. It should also be noted that by sending each new data in each array and the three-dimensional temperature distribution cloud map to the intelligent terminal of the operation and maintenance personnel through the cloud, the data transmission amount between the intelligent terminal of the operation and maintenance personnel and the execution entity (such as a computer device, etc.) of a temperature monitoring method of a drying system of the present invention can be effectively reduced, ensuring that the execution entity of a temperature monitoring method of a drying system of the present invention is in a good operating state.

[0089] Optionally, in the above technical solution, it further includes:

[0090] S201. Send a feedback request to the intelligent terminal of the operation and maintenance personnel. The intelligent terminal of the operation and maintenance personnel uses a preset hash function to perform hash calculation on the array sequence formed by all the calculated arrays in a preset order to obtain a first hash calculation result;

[0091] S202. The execution entity (such as a computer device, etc.) of a temperature monitoring method of a drying system of the present invention uses a preset hash function to perform hash calculation on the array sequence formed by all the arrays in a preset order to obtain a second hash calculation result, and receives the first hash calculation result returned by the intelligent terminal of the operation and maintenance personnel, and determines whether the first hash calculation result is the same as the second hash calculation result. If they are the same, it indicates that all the arrays calculated by the intelligent terminal of the operation and maintenance personnel are accurate. If they are not the same, it indicates that all the arrays calculated by the intelligent terminal of the operation and maintenance personnel are inaccurate, and a reminder is sent to the intelligent terminal of the operation and maintenance personnel in a timely manner.

[0092] Among them, the preset hash function and the preset order can be set according to the actual situation.

[0093] Through S201 to S202, it can effectively determine whether all the arrays calculated by the intelligent terminal of the operation and maintenance personnel are correct. And through hash calculation, both data security can be guaranteed and the data transmission amount between the intelligent terminal of the operation and maintenance personnel and the execution entity of a temperature monitoring method of a drying system of the present invention can be reduced.

[0094] Optionally, in the above technical solution, it further includes: when the execution subject of a temperature monitoring method of a drying system of the present invention receives a sharing request sent by an intelligent terminal, it determines whether the to-be-shared intelligent terminal involved in the sharing request and the user of the to-be-shared intelligent terminal are trustworthy. If so, it issues an allow-sharing permission to the intelligent terminal; if not, it prohibits issuing an allow-sharing permission to the intelligent terminal. Among them, the sharing request refers to a request to share a three-dimensional temperature distribution cloud map generated by the intelligent terminal, and the allow-sharing permission refers to allowing the intelligent terminal to send the generated three-dimensional temperature distribution cloud map to the to-be-shared intelligent terminal. The sharing request includes the unique identification code of the to-be-shared intelligent terminal (specifically, it can be the MAC address, mobile phone number, etc.) and the identity information of the user of the to-be-shared intelligent terminal (including name, face image, fingerprint, etc.).

[0095] Among them, determining whether the to-be-shared intelligent terminal involved in the sharing request and the user of the to-be-shared intelligent terminal are trustworthy is specifically implemented in the following manner:

[0096] S301. Predetermine a whitelist and a blacklist. The whitelist records multiple unique identification codes, and the intelligent terminals corresponding to the unique identification codes in the whitelist and their corresponding users are defaulted to be trustworthy. The blacklist also records multiple unique identification codes, and the intelligent terminals corresponding to the unique identification codes in the blacklist are defaulted to be untrustworthy.

[0097] S302. Determine whether the unique identification code of the to-be-shared intelligent terminal is on the blacklist or the whitelist. Then:

[0098] 1) When the unique identification code of the to-be-shared intelligent terminal is on the whitelist, it is determined that the to-be-shared intelligent terminal is trustworthy.

[0099] 2) When the unique identification code of the to-be-shared intelligent terminal is on the blacklist, it is determined that the to-be-shared intelligent terminal is untrustworthy, and the corresponding user is defaulted to be untrustworthy. At this time, S303 does not need to be executed.

[0100] 3) When the unique identification code of the to-be-shared intelligent terminal is not on both the whitelist and the blacklist, S303 is executed.

[0101] S303. Obtain the communication record of the to-be-shared intelligent terminal, determine the intelligent terminals having a communication relationship with the to-be-shared intelligent terminal. For the sake of distinction, the intelligent terminals having a communication relationship with the to-be-shared intelligent terminal are all recorded as the first intelligent terminals. Then, based on the communication record, construct an undirected graph (for the sake of distinction, denoted as the second undirected graph). In the second undirected graph, the to-be-shared intelligent terminal and each first intelligent terminal are all used as nodes (for the sake of distinction, denoted as the third nodes), and there is an edge (for the sake of distinction, denoted as the third edge) connected between two third nodes having a communication relationship.

[0102] A complete graph including the node corresponding to the to-be-shared intelligent terminal is obtained from the second undirected graph. In the complete graph, every two third nodes are connected by a third edge, that is to say, there is a communication relationship between every two third nodes.

[0103] In the second undirected graph, each first intelligent terminal corresponding to each third node except for all the nodes included in the complete graph is denoted as a second intelligent terminal. A second intelligent terminal is randomly selected, and a first intelligent terminal is randomly selected from all the first intelligent terminals in the complete graph. The name and / or face information of the to-be-shared intelligent terminal is assistedly authenticated by using the randomly selected first intelligent terminal and the randomly selected second intelligent terminal, and the execution subject of a temperature monitoring method of a drying system of the present invention verifies the identity information of the user of the to-be-shared intelligent terminal. After both authentications are passed, it is determined that the user and the unique identifier of the shared intelligent terminal are trustworthy, and the unique identifier of the to-be-shared intelligent terminal and the user of the to-be-shared intelligent terminal are added to the white list.

[0104] In S303, by classifying the intelligent terminals having a communication relationship with the to-be-shared intelligent terminal (each first intelligent terminal corresponding to each third node included in the complete graph is one category, and all the second intelligent terminals are another category), and then randomly selecting a first intelligent terminal and a second intelligent terminal from each category for assisted authentication, the reliability of the identity verification of the to-be-shared intelligent terminal can be enhanced, and data security can be further ensured.

[0105] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention. It can be understood that in some embodiments, it may include some or all of the above embodiments.

[0106] As Figure 2 shown, a temperature monitoring system 200 of a drying system according to an embodiment of the present invention includes a target position determination module 201 and a temperature back-calculation module 202;

[0107] The target position determination module 201 is configured to: analyze the degree of association of the temperature values of each preset position of the reference drying system, and determine a plurality of target positions according to the result of the degree of association analysis;

[0108] The temperature back-calculation module 202 is configured to: collect temperature through temperature sensors arranged at each target position of the target drying system, and back-calculate the temperature values of each non-target position of the target drying system according to the temperature values of each target position;

[0109] Wherein, the reference drying system and the target drying system are two identical drying systems.

[0110] Optionally, in the above technical solution, the target position determination module 201 is specifically configured to:

[0111] Obtain the temperature values of each preset position of the reference drying system within a preset time period to obtain a plurality of time-temperature sequence data;

[0112] Perform similarity analysis on the plurality of time-temperature sequence data, and divide all the preset positions into multiple groups according to the similarity analysis result. Among them, the similarity between the time-temperature sequence data of every two preset positions in the same group exceeds a preset similarity threshold, and the similarity characterizes the degree of association;

[0113] Select one preset position from each group as the target position.

[0114] Optionally, in the above technical solution, the temperature back-calculation module 202 is specifically configured to: based on the time-temperature sequence data of each preset position in each group, back-calculate the temperature values of each non-target position of the target drying system.

[0115] Optionally, in the above technical solution, it further includes an early warning module, and the early warning module is used to: determine whether to send out an early warning message according to the temperature values of each target position and each non-target position of the target drying system.

[0116] It should be noted that the beneficial effects of the temperature monitoring system 200 of a drying system provided in the above embodiment are the same as those of the temperature monitoring method of a drying system provided above, and will not be elaborated here. In addition, when the system provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used as an example for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system provided in the above embodiment and the method embodiment belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be elaborated here.

[0117] Among them, the temperature monitoring system of the drying system of the present invention can be a computer program (including program code) running in a computer device. For example, the temperature monitoring system of the drying system of the present invention is an application software, which can be used to execute the corresponding steps in the temperature monitoring method of the drying system of the present invention.

[0118] In some embodiments, the temperature monitoring system of the drying system of the present invention can be implemented in a combination of software and hardware. As an example, the temperature monitoring system of the drying system of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the temperature monitoring method of the drying system of the present invention. For example, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0119] Among them, the modules involved in the embodiments of the present invention can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the module itself in some cases.

[0120] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the temperature monitoring method of any one of the above drying systems. That is to say, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the temperature monitoring method of the drying system shown in any one of the embodiments of the present invention by calling the computer program.

[0121] In an alternative embodiment, an electronic device is provided, as Figure 3 shown, Figure 3 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. Among them, the processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, and the transceiver 4004 can be used for data interaction between the electronic device and other electronic devices, such as data sending and / or data receiving, etc. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation to the embodiments of the present invention.

[0122] The processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of the present invention. The processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0123] The bus 4002 may include a path for transmitting information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a thick line is used to represent the bus 4002 in the figure, but it does not mean that there is only one bus or one type of bus.

[0124] The memory 4003 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or it may also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0125] The memory 4003 is used to store the application program code (computer program) for implementing the solution of the present invention, and is controlled by the processor 4001 for execution. The processor 4001 is used to execute the application program code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0126] Among them, the electronic device may also be a terminal device, and the terminal device may be any device that can install applications, including at least one of a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart vehicle-mounted device.

[0127] It should be noted that Figure 3 The electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0128] A computer-readable storage medium according to an embodiment of the present invention has a computer program stored thereon. When the computer program is executed by a processor, the temperature monitoring method of any one of the above drying systems is implemented.

[0129] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0130] In an exemplary embodiment, there is also provided a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes the temperature monitoring method of any one of the above drying systems.

[0131] Computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0132] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0133] The computer-readable storage medium provided by the embodiments of the present invention may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0134] The above computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to execute the method shown in the above embodiments.

[0135] The above description is only a preferred embodiment of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but also covers other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present invention.

[0136] It should be noted that the terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, and represent a limitation on a specific order or sequence. In appropriate cases, the order of use of similar objects can be interchanged so that the embodiments of this application described here can be implemented in an order other than the illustrated or described order.

[0137] Those skilled in the art know that the present invention can be implemented as a system, a method, or a computer program product. Therefore, the present invention can be specifically implemented in the following forms, that is: it can be completely hardware, can also be completely software (including firmware, resident software, microcode, etc.), and can also be in the form of a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" in this article. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable media contains computer-readable program code.

[0138] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A temperature monitoring method for a drying system, characterized in that, including: Analyze the degree of correlation of the temperature values at each preset position of the reference drying system, and determine multiple target positions according to the analysis results of the degree of correlation; Collect temperature through temperature sensors set at each target position of the target drying system, and infer the temperature values of each non-target position of the target drying system according to the temperature values of each target position; Wherein, the reference drying system and the target drying system are two identical drying systems.

2. The temperature monitoring method of a drying system according to claim 1, characterized in that, Analyze the degree of correlation of the temperature values at each preset position of the reference drying system, and determine multiple target positions according to the analysis results of the degree of correlation, including: Obtain the temperature values at each preset position of the reference drying system within a preset time period to obtain multiple time-temperature sequence data; Conduct similarity analysis on the multiple time-temperature sequence data, and divide all preset positions into multiple groups according to the analysis results of similarity. Among them, the similarity between the time-temperature sequence data of every two preset positions in the same group exceeds the preset similarity threshold, and the similarity represents the degree of correlation; Select one preset position from each group as the target position.

3. The temperature monitoring method of a drying system according to claim 2, wherein Infer the temperature values of each non-target position of the target drying system according to the temperature values of each target position, including: Infer the temperature values of each non-target position of the target drying system based on the time-temperature sequence data of each preset position in each group.

4. The temperature monitoring method of a drying system according to claim 3, characterized in that, It also includes: Determine whether to send a warning message according to the temperature values of each target position and each non-target position of the target drying system.

5. A temperature monitoring system for a drying system, characterized in that, including a target position determination module and a temperature inference module; The target position determination module is used to: analyze the degree of correlation of the temperature values at each preset position of the reference drying system, and determine multiple target positions according to the analysis results of the degree of correlation; The temperature inference module is used to: collect temperature through temperature sensors set at each target position of the target drying system, and infer the temperature values of each non-target position of the target drying system according to the temperature values of each target position; Wherein, the reference drying system and the target drying system are two identical drying systems.

6. The temperature monitoring system of a drying system according to claim 5, characterized in that, Specifically, the target position determination module is used to: Obtain the temperature values at each preset position of the reference drying system within a preset time period to obtain multiple time-temperature sequence data; Conduct similarity analysis on the multiple time-temperature sequence data, and divide all preset positions into multiple groups according to the analysis results of similarity. Among them, the similarity between the time-temperature sequence data of every two preset positions in the same group exceeds the preset similarity threshold, and the similarity represents the degree of correlation; Select one preset position from each group as the target position.

7. The temperature monitoring system of a drying system according to claim 6, characterized in that, Specifically, the temperature inference module is used to: infer the temperature values of each non-target position of the target drying system based on the time-temperature sequence data of each preset position in each group.

8. The temperature monitoring system of a drying system according to claim 7, characterized in that, It also includes a warning module, and the warning module is used to: determine whether to send a warning message according to the temperature values of each target position and each non-target position of the target drying system.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the temperature monitoring method of a drying system according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, it implements the temperature monitoring method of a drying system according to any one of claims 1 to 4.

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