Method, system and device for controlling intelligent warmer

Abstract

The invention relates to the technical field of data transmission, in particular to a method, system and device for controlling an intelligent warmer, and the method comprises the steps: S1, distributing a first sequence number for each control data group, and generating feature control data according to generation information; s2, performing preset operation processing to obtain process data, and performing secret processing on the process data by using specific data to obtain secret data; s3, selecting one control data set, performing preset operation processing on the selected control data set and the secret data stored last time to obtain process data, continuously using the specific data to perform secret processing on the process data to obtain secret data, and repeating the processing when the control data set which is not selected exists; and S4, the final sending data are transmitted, the control data are recovered from the final sending data, and intelligent control over the warmer is achieved. According to the invention, the security of the control data can be ensured.

Description

Technical Field

[0001] This application relates to the technical field of data transmission, and particularly to a method, system and device for controlling an intelligent heater. Background Art

[0002] With the development of computer application technology, users can send control data to a controller to achieve remote automatic control of a heater, thereby making it more convenient to use the heater.

[0003] A patent application with publication number WO2016058366A1 discloses a control method for intelligent household appliances, which is applied to a home control center. The method includes: a routing module receives a control instruction and sends the control instruction to a smart home control module, where the control instruction carries an appliance device identifier; the smart home control module parses the control instruction to obtain the appliance device identifier carried by the control instruction, determines a target appliance according to the appliance device identifier, processes the control instruction into an appliance control signal adapted to the target appliance, and sends the appliance control signal and the appliance device identifier to the routing module; the routing module sends the received appliance control signal to the intelligent appliance represented by the appliance device identifier to achieve control of the intelligent appliance. In addition, a patent application with publication number WO2020052437A1 provides a control method and an appliance system for an appliance system. The appliance system includes an active appliance and a slave appliance. Both the active appliance and the slave appliance include a communication module, and the active appliance and the slave appliance are connected through their respective communication modules. The control method includes: the active appliance obtains a linkage information instruction; the active appliance generates a program instruction for the slave appliance according to the linkage information instruction; the active appliance sends the program instruction to the slave appliance, so that the user can complete the setting of the operation programs of multiple appliances by setting an operation instruction only once.

[0004] However, neither of the above two patent applications secretly sends the control data of the user, and there is a risk that the control data may be leaked. Summary of the Invention

[0005] This application sets a user module to divide control data into several control data groups, respectively convert the several control data groups into several secret data, generate final transmission data based on the several secret data, transmit the final transmission data, set a control module to receive the final transmission data, and recover the control data from the final transmission data to achieve intelligent control of the heater. This application aims to ensure the security of control data.

[0006] This application provides a method for controlling an intelligent heater, including the following steps:

[0007] S1. The user module collects control data, divides the control data into several control data groups, assigns a first sequence number to each control data group according to the division order, and the user module also generates characteristic control data from all control data groups except the control data group corresponding to the largest first sequence number according to the generation information;

[0008] S2. The user module performs a preset operation process on the characteristic control data and the control data group corresponding to the largest first sequence number to obtain process data, continues to perform a concealment process on the process data using the generated specific data to obtain concealed data, and performs a storage process on the concealed data;

[0009] S3. The user module selects a control data group with the smallest corresponding first sequence number that has not been selected in all control data groups except the control data group corresponding to the largest first sequence number, and the user module performs a preset operation process on the selected control data group and the previously stored concealed data to obtain process data, continues to perform a concealment process on the process data using the specific data to obtain concealed data, and performs a storage process on the concealed data. The user module also determines whether there are unselected control data groups. If so, this step is repeated;

[0010] S4. The user module sequentially connects the different concealed data in S3 according to the acquisition order to obtain intermediate transmission data, and then continues to connect the concealed data in S2 after the intermediate transmission data to obtain final transmission data. The user module sends the final transmission data to the control module through the network module. The control module recovers the control data from the final transmission data and realizes the intelligent control of the heater according to the control data.

[0011] As a preferred technical solution of this application, both the user module and the control module pre-store the generation information. The generation information includes a selection method for selecting several control data groups used to generate characteristic control data, and a generation algorithm for generating characteristic control data based on the selected several control data groups.

[0012] As a preferred technical solution of this application, the control module recovers the control data from the final transmission data, including the following steps:

[0013] S41. The control module divides the finally transmitted data into several data groups, assigns a second sequence number to each data group according to the division order, and the control module selects the data group with the smallest corresponding second sequence number among all the data groups. The control module also uses specific data to perform recovery processing on the selected data group to obtain intermediate data, and performs preset arithmetic processing on the intermediate data and the data group with the largest corresponding second sequence number among all the data groups to obtain partial control data;

[0014] S42. The control module selects an unselected data group with the smallest corresponding second sequence number among all the data groups except the data group with the smallest corresponding second sequence number, and the control module uses specific data to perform recovery processing on the selected data group to obtain intermediate data, and performs preset arithmetic processing on the intermediate data and the previously selected data group to obtain partial control data;

[0015] S43. The control module determines whether there are unselected data groups. If not, it sequentially connects all the partial control data in the obtained order to obtain control data, and ends all steps. If so, it continues to determine whether there is only one unselected data group. If not, it jumps to S42. If so, it uses specific data to perform recovery processing on the only unselected data group to obtain intermediate data, generates characteristic control data from all the obtained partial control data based on the generated information, performs preset arithmetic processing on the intermediate data and the characteristic control data to obtain partial control data, and repeats this step.

[0016] As a preferred technical solution of the present application, the user module generates specific data, including the following steps:

[0017] S21. The user module acquires several pictures, extracts non-key picture blocks from each picture, and the user module uses all the non-key picture blocks to form a characteristic picture;

[0018] S22. The user module divides the characteristic picture into several characteristic picture blocks, and for each characteristic picture block, the user module performs conversion arithmetic processing on each picture element in the characteristic picture block to obtain the converted picture element value;

[0019] S23. For each characteristic picture block, the user module calculates the sum of the converted picture element values of all the picture elements in the characteristic picture block to obtain a characteristic value, and the user module generates specific data based on all the characteristic values.

[0020] As a preferred technical solution of the present application, the user module extracts non-key picture blocks from pictures, including the following steps:

[0021] S211. The user module takes the upper left corner position of the picture as the origin, takes the horizontal right direction as the positive direction of the x-axis, and takes the vertical downward direction as the positive direction of the y-axis to establish a picture coordinate system, and the user module sets a range boundary in the picture coordinate system, and the upper left corner position of the range boundary coincides with the origin;

[0022] S212. The user module extracts the representative data of the partial picture corresponding to the range boundary, inputs the representative data into the trained analysis model, and the analysis model outputs the probability value that the target object is included in the partial picture. After moving the range boundary a preset length in the positive direction of the x-axis each time, the same analysis method is repeated until the upper right corner position of the range boundary coincides with the upper right corner position of the picture. After that, each time the range boundary is moved a preset length in the positive direction of the y-axis, this step is repeated until the lower left corner position of the range boundary coincides with the lower left corner position of the picture;

[0023] S213. The user module determines several partial pictures in all the partial pictures whose corresponding probability values are greater than or equal to a preset first probability value threshold, and the user module calculates an evaluation value based on the probability values of all the determined partial pictures, and judges whether the evaluation value is greater than or equal to a preset evaluation value threshold. In the case of no, continue to the next step. In the case of yes, remove several partial pictures in all the determined partial pictures whose corresponding probability values are less than a preset second probability value threshold, and continue to the next step;

[0024] S214. The user module performs clustering processing on all the remaining partial pictures, selects the partial picture with the largest corresponding probability value in each category, and the user module removes all the selected partial pictures from the picture to generate a non-key picture block.

[0025] As a preferred technical solution of the present application, the user module sets the second probability value threshold to be greater than the first probability value threshold.

[0026] The present application also provides a system for controlling an intelligent heater, including the following modules:

[0027] The user module is used to collect control data, divide the control data into several control data groups, assign a first sequence number to each control data group according to the division order, and generate characteristic control data from all control data groups except the control data group corresponding to the largest first sequence number according to the generated information; at the same time, it is used to perform a preset operation process on the characteristic control data and the control data group corresponding to the largest first sequence number to obtain process data, continue to perform a concealment process on the process data using the generated specific data to obtain concealed data, and perform a storage process on the concealed data; and it is used to select a control data group with the smallest corresponding first sequence number that has not been selected from all control data groups except the control data group corresponding to the largest first sequence number, perform a preset operation process on the selected control data group and the previously stored concealed data to obtain process data, continue to perform a concealment process on the process data using the specific data to obtain concealed data, perform a storage process on the concealed data, determine whether there are unselected control data groups, and if so, repeat this process; it is also used to sequentially connect different subsequently generated concealed data in the order of acquisition to obtain intermediate transmission data, and continue to connect the initially generated concealed data after the intermediate transmission data to obtain final transmission data, and send the final transmission data to the control module through the network module;

[0028] The network module is used to transmit the final transmission data generated by the user module to the control module;

[0029] The control module is used to recover the control data from the final transmission data and implement intelligent control of the heater according to the control data.

[0030] This application also provides a device, including a memory and a processor. The memory is used to store a computer program, and the processor is used to implement the method described in any one of the above when executing the computer program.

[0031] Compared with the prior art, the beneficial effects of this application are at least as follows:

[0032] In the technical solution provided by this application, first, the user module collects control data, divides the control data into several control data groups, and generates characteristic control data according to the generated information. Secondly, the user module performs a preset arithmetic process on the characteristic control data and the control data group corresponding to the largest first sequence number to obtain process data, and continues to perform a concealment process on the process data using the generated specific data to obtain concealed data. Thirdly, the user module selects a control data group, performs a preset arithmetic process on the selected control data group and the previously stored concealed data to obtain process data, and continues to perform a concealment process on the process data using the same specific data to obtain concealed data. In the case where there are unselected control data groups, the same process is repeated. Finally, the user module generates the final transmission data, sends the final transmission data to the control module through the network module, and the control module recovers the control data from the final transmission data, and realizes the intelligent control of the heater according to the control data. Through this application, even if the final transmission data is stolen during the transmission process, the control data set by the user cannot be obtained, which can not only well protect the privacy of the user, but also avoid the control data from being tampered with, ensuring the safe use of the heater. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0034] Figure 1 It is a flowchart of the method for controlling an intelligent heater in an embodiment of this application;

[0035] Figure 2 It is a schematic diagram of the system for controlling an intelligent heater in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The embodiments of the present application provide a method, a system and a device for controlling an intelligent heater. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the term "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0037] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 , the method for controlling an intelligent heater in the embodiments of the present application mainly includes the following steps:

[0038] S1. The user module collects control data, divides the control data into several control data groups, assigns a first sequence number to each control data group according to the division order, and the user module also generates characteristic control data from all control data groups except the control data group corresponding to the largest first sequence number according to the generated information;

[0039] S2. The user module performs a preset operation on the characteristic control data and the control data group corresponding to the largest first sequence number to obtain process data, continues to perform a concealment process on the process data using the generated specific data to obtain concealed data, and performs a storage process on the concealed data;

[0040] S3. The user module selects a control data group with the smallest corresponding first sequence number that has not been selected from all control data groups except the control data group corresponding to the largest first sequence number, and the user module performs a preset operation on the selected control data group and the previously stored concealed data to obtain process data, continues to perform a concealment process on the process data using the specific data to obtain concealed data, and performs a storage process on the concealed data. The user module also determines whether there is an unselected control data group. If so, this step is repeated;

[0041] S4. The user module sequentially connects the different concealed data in S3 according to the obtained order to obtain intermediate transmission data, continues to connect the concealed data in S2 after the intermediate transmission data to obtain final transmission data, and the user module sends the final transmission data to the control module through the network module. The control module recovers the control data from the final transmission data and realizes intelligent control of the heater according to the control data.

[0042] Specifically, considering that the control data set by the user has a certain degree of privacy, and if the control data is tampered with, it may cause safety problems in the use of the heater, it is necessary to ensure that the control data cannot be illegally obtained. Therefore, S1 to S4 are mainly proposed.

[0043] In S1, the user module collects the control data set by the user. The user module can be the user's mobile phone. The control data may include information such as the turning-on time and turning-on temperature of the heater. Subsequently, the user module divides the control data into several control data groups. It should be noted that the data lengths of different control data groups are the same, and the data length is specifically measured by the number of bits. Each control data group is assigned a first sequence number according to the division order. For the sake of easy understanding, for example, there is control data A. Control data A is divided into control data groups A1, A2, A3, and A4 in the order from left to right. Then the first sequence numbers of control data groups A1 to A4 are 1 to 4 in sequence. After that, the user module generates characteristic control data from all control data groups except the control data group corresponding to the largest first sequence number according to the generated information. The process will be described in detail below. It should be noted that the data length of the characteristic control data is the same as that of the control data group. In S2, the user module performs a preset operation process on the characteristic control data and the control data group corresponding to the largest first sequence number to obtain process data. It should be noted that the data length of the process data is the same as that of the control data group. For the sake of easy understanding, for example, performing a preset operation process on 0 and 1 can obtain 1, and performing a preset operation process on 0 and 0, or 1 and 1 can both obtain 0. Continuing to use the generated specific data to perform a concealment process on the process data to obtain concealed data, and performing a storage process on the concealed data. It should be noted that the data length of the concealed data is the same as that of the process data. How the specific data is generated will be described below. In fact, the concealment process can be equivalent to the encryption process using the AES algorithm, and the specific data can be equivalent to the key of the AES algorithm. In S3, among all control data groups except the control data group corresponding to the largest first sequence number, the user module selects a control data group whose corresponding first sequence number that has not been selected is the smallest. Subsequently, the user module performs a preset operation process on the selected control data group and the previously stored concealed data to obtain process data, continues to use the specific data to perform a concealment process on the process data to obtain concealed data, and performs a storage process on the concealed data. After that, the user module determines whether there are unselected control data groups. If so, this step is repeated. If not, S4 is continued. In S4, the user module sequentially connects the different concealed data obtained in S3 according to the acquisition order to generate intermediate transmission data, and then continues to connect the concealed data obtained in S2 after the intermediate transmission data to generate final transmission data. Subsequently, the user module sends the final transmission data to the control module through the network module. The control module recovers the control data from the final transmission data. The recovery process will be described in detail below, so as to realize the intelligent control of the heater according to the control data.

[0044] Furthermore, both the user module and the control module pre-store generation information, which includes a selection method for selecting several control data groups used to generate feature control data, and a generation algorithm for generating feature control data based on the selected several control data groups.

[0045] Specifically, an introduction is made to the generation information pre-stored in both the user module and the control module. The generation information includes a selection method for selecting several control data groups used to generate feature control data. For the sake of easy understanding, for example, the selection method is to select several control data groups at intervals starting from the control data group with the smallest corresponding first sequence number among all control data groups except the control data group corresponding to the largest first sequence number. The generation information also includes a generation algorithm for generating feature control data based on the selected several control data groups. For the sake of easy understanding, for example, the generation algorithm is MD5. By pre-storing generation information in the user module and the control module instead of directly pre-storing feature control data, this is done to further increase the difficulty of illegally obtaining control data. As can be known from the following text, even if the generation information is obtained, to finally obtain complete control data, several partial control data need to be obtained first, and to obtain several partial control data, specific data needs to be known first.

[0046] Furthermore, the control module recovers control data from the finally sent data, including the following steps:

[0047] S41. The control module divides the finally sent data into several data groups, assigns a second sequence number to each data group according to the division order, and the control module selects the data group with the smallest corresponding second sequence number among all data groups. The control module also uses specific data to perform recovery processing on the selected data group to obtain intermediate data, and performs a preset operation processing on the intermediate data and the data group with the largest corresponding second sequence number among all data groups to obtain partial control data;

[0048] S42. The control module selects an unselected data group with the smallest corresponding second sequence number among all data groups except the data group with the smallest corresponding second sequence number, and the control module uses specific data to perform recovery processing on the selected data group to obtain intermediate data, and performs a preset operation processing on the intermediate data and the data group selected last time to obtain partial control data;

[0049] S43. The control module determines whether there is an unselected data group. If not, it sequentially connects all partial control data in the obtained order to obtain control data, and ends all steps. If so, it continues to determine whether there is only one unselected data group. If not, it jumps to S42. If so, it uses specific data to perform a recovery process on the only unselected data group to obtain intermediate data, generates characteristic control data from all the obtained partial control data based on the generated information, performs a preset operation process on the intermediate data and the characteristic control data to obtain partial control data, and repeats this step.

[0050] Specifically, the process of the control module recovering control data from the finally transmitted data is described in detail. In S41, the control module divides the finally transmitted data into several data groups, and assigns a second sequence number to each data group according to the division order. For the sake of easy understanding, for example, in the order from left to right, the finally transmitted data B is divided into data group B1, data group B2, data group B3, and data group B4. The second sequence numbers of data group B1 to data group B4 are 1 to 4 respectively. According to the above, it can be known that data group B1 to data group B4 actually correspond to control data group A1 to control data group A4 respectively. Subsequently, the control module selects the data group with the smallest corresponding second sequence number among all the data groups, and uses specific data to perform recovery processing on the selected data group to obtain intermediate data. The recovery processing can be equivalent to the decryption processing using the AES algorithm. Perform a preset operation on the intermediate data and the data group with the largest corresponding second sequence number among all the data groups to obtain partial control data, and store the partial control data. In S42, the control module selects an unselected data group with the smallest corresponding second sequence number among all the data groups except the data group with the smallest corresponding second sequence number. For the sake of easy understanding, following the above example, it can be to select the unselected data group B2 among data group B2 to data group B4. Subsequently, the control module uses specific data to perform recovery processing on the selected data group to obtain intermediate data, and perform a preset operation on the intermediate data and the previously selected data group to obtain partial control data, and store the partial control data. In S43, the control module determines whether there are unselected data groups. If not, connect all the partial control data in the order of acquisition to obtain control data, and end all steps. If so, continue to determine whether there is only one unselected data group. If not, jump to S42. If so, select the only unselected data group, use specific data to perform recovery processing on the only unselected data group to obtain intermediate data, generate characteristic control data from all the obtained partial control data based on the generated information. It should be noted that all the obtained partial control data correspond to several control data groups divided in sequence. Perform a preset operation on the intermediate data and the characteristic control data to obtain partial control data, and repeat this step.

[0051] Through the above method, during the transmission of the finally transmitted data, even if the finally transmitted data is stolen, the control data set by the user cannot be obtained, ensuring the security of the control data.

[0052] Furthermore, the user module generates specific data, including the following steps:

[0053] S21. The user module obtains several pictures, extracts non-key picture blocks from each picture, and the user module uses all the non-key picture blocks to form a feature picture;

[0054] S22. The user module divides the feature picture into several feature picture blocks, and for each feature picture block, the user module performs a conversion operation on each picture element in the feature picture block to obtain a converted picture element value;

[0055] S23. For each feature picture block, the user module calculates the sum of the converted picture element values of all the picture elements in the feature picture block to obtain a feature value, and the user module generates specific data based on all the feature values.

[0056] Specifically, the process of the user module generating specific data is introduced. In S21, the user module obtains several pictures. For example, a mobile phone can directly obtain historical captured pictures stored in itself. Non-key picture blocks are extracted from each picture. For example, non-key picture blocks can be understood as picture blocks composed of parts in the picture that do not contain the target object, which has a certain degree of randomness. Subsequently, the user module uses all the non-key picture blocks to form a feature picture. In S22, the user module divides the feature picture into several feature picture blocks. The total number of feature picture blocks can correspond to the data length of the specific data. For the sake of understanding, for example, if the data length of the specific data is sixteen bytes, the total number of feature picture blocks can be sixteen. For each feature picture block, the user module performs a conversion operation on each picture element in the feature picture block to obtain a converted picture element value. The conversion operation can convert the RGB picture element value of the picture element into a grayscale picture element value. In S23, for each feature picture block, the user module calculates the sum of the converted picture element values of all the picture elements in the feature picture block to obtain a feature value. Subsequently, the user module generates specific data based on all the feature values. For example, for each feature picture block, its feature value is converted into a binary numerical string, and a partial binary numerical string of one byte is arbitrarily intercepted from the binary numerical string. On this basis, all the partial binary numerical strings are connected to form specific data.

[0057] Further, the user module extracts non-key picture blocks from the picture, including the following steps:

[0058] S211. The user module establishes a picture coordinate system with the upper left corner position of the picture as the origin, the positive direction of the x-axis horizontally to the right, and the positive direction of the y-axis vertically downward, and the user module sets a range boundary in the picture coordinate system, and the upper left corner position of the range boundary coincides with the origin;

[0059] S212. The user module extracts representative data of partial images corresponding to the range boundary, inputs the representative data into the trained analysis model, and the analysis model outputs the probability value that the target object is included in the partial images. After each time the range boundary is moved a preset length in the positive x-axis direction, the same analysis method is repeated until the upper right corner position of the range boundary coincides with the upper right corner position of the image. Then, after each time the range boundary is moved a preset length in the positive y-axis direction, this step is repeated until the lower left corner position of the range boundary coincides with the lower left corner position of the image;

[0060] S213. The user module determines several partial images in all partial images whose corresponding probability values are greater than or equal to a preset first probability value threshold, and the user module calculates an evaluation value based on the probability values of all determined partial images, and determines whether the evaluation value is greater than or equal to a preset evaluation value threshold. In the case of no, continue to the next step. In the case of yes, several partial images whose corresponding probability values are less than a preset second probability value threshold are removed from all determined partial images, and continue to the next step;

[0061] S214. The user module performs clustering processing on all remaining partial images, selects the partial image with the largest corresponding probability value in each category, and the user module removes all selected partial images from the image to generate non-key image blocks;

[0062] Further, the user module sets the second probability threshold to be greater than the first probability threshold, and the second probability threshold and the first probability threshold are set in the actual application scenario.

[0063] Specifically, continue to introduce the process of the user module extracting non-key image blocks from the image. In S211, the user module takes the upper left corner position of the image as the origin, takes the horizontal right direction as the positive x-axis direction, and takes the vertical downward direction as the positive y-axis direction to establish an image coordinate system. Subsequently, the user module sets a range boundary in the image coordinate system. The upper left corner position of the range boundary coincides with the origin, and records the current position of the range boundary as the first position. The shape of the range boundary can be understood as a rectangle. The upper left corner position of the rectangle coincides with the origin, the width of the rectangle coincides with the x-axis of the image coordinate system, and the length of the rectangle coincides with the y-axis of the image coordinate system.

[0064] In S212, the user module extracts representative data of partial images corresponding to the range boundary. It should be noted that the partial images include an integer number of image elements. For example, they include 6*7 image elements, where 6 and 7 correspond to the width and length of a rectangle respectively. The representative data can be the HOG data of the partial images. The representative data is input into a trained analysis model, which can be a machine learning model in the prior art and will not be elaborated here. The analysis model outputs the probability value that the target object is included in the partial images. The target object can be a person. Subsequently, each time the range boundary is moved a preset length in the positive x-axis direction and the analysis method is repeated to obtain the probability value until the upper-right corner position of the range boundary coincides with the upper-right corner position of the image. The preset length is an integer multiple of the length of the image element in the image coordinate system. Then, each time the range boundary is moved a preset length in the positive y-axis direction and the above method is repeated until the lower-left corner position of the range boundary coincides with the lower-left corner position of the image. For the sake of understanding, this process is described as follows: First, place the range boundary back at the first position, move the range boundary a preset length in the positive y-axis direction, and record the position of the range boundary at this time as the second position. Each time the range boundary is moved a preset length in the positive x-axis direction and the analysis method is repeated until the upper-right corner position of the range boundary coincides with the upper-right corner position of the image. Then, place the range boundary back at the second position, move the range boundary a preset length in the positive y-axis direction, and record the position of the range boundary at this time as the third position. Each time the range boundary is moved a preset length in the positive x-axis direction and the analysis method is repeated until the upper-right corner position of the range boundary coincides with the upper-right corner position of the image. And so on for subsequent processing until the last time the range boundary is moved a preset length in the positive y-axis direction and the lower-left corner position of the range boundary coincides with the lower-left corner position of the image. Each time the range boundary is moved a preset length in the positive x-axis direction and the analysis method is repeated until the upper-right corner position of the range boundary coincides with the upper-right corner position of the image. After that, the range boundary is not moved anymore.

[0065] In S213, the user module determines several partial images among all the partial images whose corresponding probability values are greater than or equal to a preset first probability value threshold. These several partial images are considered to possibly contain the target object. Subsequently, the user module calculates an evaluation value based on the probability values of all the determined partial images. For example, the variance of all the probability values is calculated as the evaluation value. It is judged whether the evaluation value is greater than or equal to a preset evaluation value threshold. If it is less, the next step is continued. If it is greater than or equal, the fluctuations of different probability values are relatively large, and there may be errors in different probability values. Among all the determined partial images, several partial images whose corresponding probability values are less than a preset second probability value threshold are removed, and the next step is continued. In S214, since there are many partial images with overlapping parts among all the remaining partial images, the user module performs clustering processing on all the remaining partial images, selects the partial image with the largest corresponding probability value in each category. Subsequently, the user module removes all the selected partial images from the picture to generate a non-key picture block. Specifically, all the remaining parts in the picture can be stitched together into a complete picture as the non-key picture block through the existing technology.

[0066] According to another aspect of the embodiments of the present application, as shown in Figure 2 the present application also provides a system for controlling an intelligent heater, including a user module, a network module, and a control module, to implement the method for controlling an intelligent heater described above. Among them, the functions of each module are as follows:

[0067] The user module is used to collect control data, divide the control data into several control data groups, assign a first serial number to each control data group according to the division order, and generate characteristic control data from all the control data groups except the control data group corresponding to the largest first serial number according to the generated information; at the same time, it is used to perform a preset operation process on the characteristic control data and the control data group corresponding to the largest first serial number to obtain process data, continue to perform a concealment process on the process data using the generated specific data to obtain concealed data, and perform a storage process on the concealed data; and it is used to select a control data group with the smallest corresponding first serial number that has not been selected among all the control data groups except the control data group corresponding to the largest first serial number, perform a preset operation process on the selected control data group and the previously stored concealed data to obtain process data, continue to perform a concealment process on the process data using the specific data to obtain concealed data, perform a storage process on the concealed data, judge whether there is an unselected control data group, and if so, repeat this process; it is also used to sequentially connect different concealed data generated subsequently in the obtained order to obtain intermediate transmission data, and continue to connect the concealed data initially generated after the intermediate transmission data to obtain final transmission data, and send the final transmission data to the control module through the network module;

[0068] A network module for transmitting the final transmitted data generated by the user module to the control module;

[0069] A control module for recovering control data from the final transmitted data and realizing intelligent control of the heater according to the control data.

[0070] According to another aspect of the embodiments of the present application, there is also provided a device including a memory and a processor. The memory is used to store a computer program, and the processor is used to implement the method of any one of the above when executing the computer program.

[0071] In summary, first, the user module collects control data, divides the control data into several control data groups, and generates characteristic control data according to the generated information; second, the user module performs a preset arithmetic process on the characteristic control data and the control data group corresponding to the largest first sequence number to obtain process data, and continues to perform a concealment process on the process data using the generated specific data to obtain concealed data; third, the user module selects a control data group, performs a preset arithmetic process on the selected control data group and the previously stored concealed data to obtain process data, and continues to perform a concealment process on the process data using the same specific data to obtain concealed data. In the case where there are unselected control data groups, the same process is repeated; finally, the user module generates the final transmitted data, sends the final transmitted data to the control module through the network module, and the control module recovers the control data from the final transmitted data and realizes intelligent control of the heater according to the control data. Through the present application, even if the final transmitted data is stolen during the transmission process, the control data set by the user cannot be obtained, which can not only well protect the privacy of the user, but also avoid the control data from being tampered with and ensure the safe use of the heater.

[0072] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0073] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0074] As described above, the above embodiments are only used to illustrate the technical solution of this application, rather than to limit it; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of various embodiments of this application.

Claims

1. A method for controlling an intelligent heater, characterized in that, The method includes the following steps: S1. The user module collects control data, and the user module divides the control data into several control data groups, assigns a first sequence number to each control data group according to the division order, and the user module also generates characteristic control data from all control data groups except the control data group corresponding to the largest first sequence number according to the generated information; S2. The user module performs a preset operation process on the characteristic control data and the control data group corresponding to the largest first sequence number to obtain process data, continues to perform a concealment process on the process data using the generated specific data to obtain concealed data, and performs a storage process on the concealed data; S3. The user module selects a control data group with the smallest corresponding first sequence number that has not been selected in all control data groups except the control data group corresponding to the largest first sequence number, and the user module performs a preset operation process on the selected control data group and the previously stored concealed data to obtain process data, continues to perform a concealment process on the process data using the specific data to obtain concealed data, and performs a storage process on the concealed data. The user module also determines whether there are unselected control data groups. If so, this step is repeated; S4. The user module sequentially connects the different concealed data in S3 according to the obtained order to obtain intermediate transmission data, and then continues to connect the concealed data in S2 after the intermediate transmission data to obtain final transmission data. And the user module sends the final transmission data to the control module through the network module. The control module recovers the control data from the final transmission data and realizes intelligent control of the heater according to the control data.

2. The method according to claim 1, wherein Both the user module and the control module pre-store the generated information. The generated information includes the selection method for selecting several control data groups used to generate the characteristic control data, and the generation algorithm for generating the characteristic control data based on the selected several control data groups.

3. The method according to claim 2, characterized in that The control module recovers the control data from the final transmission data, including the following steps: S41. The control module divides the final transmission data into several data groups, assigns a second sequence number to each data group according to the division order, and the control module selects the data group with the smallest corresponding second sequence number in all data groups. The control module also performs a recovery process on the selected data group using the specific data to obtain intermediate data, and performs a preset operation process on the intermediate data and the data group with the largest corresponding second sequence number in all data groups to obtain partial control data; S42. The control module selects a data group with the smallest corresponding second sequence number that has not been selected in all data groups except the data group with the smallest corresponding second sequence number, and the control module performs a recovery process on the selected data group using the specific data to obtain intermediate data, and performs a preset operation process on the intermediate data and the previously selected data group to obtain partial control data; S43. The control module determines whether there is an unselected data group. If not, it sequentially connects all partial control data in the obtained order to obtain control data, and ends all steps. If so, it continues to determine whether there is only one unselected data group. If not, it jumps to S42. If so, it uses specific data to perform a recovery process on the only unselected data group to obtain intermediate data, generates characteristic control data from all the obtained partial control data based on the generated information, performs a preset operation process on the intermediate data and the characteristic control data to obtain partial control data, and repeats this step.

4. The method according to claim 1, wherein The user module generates specific data, including the following steps: S21. The user module acquires a plurality of pictures, extracts non-key picture blocks from each picture, and the user module uses all the non-key picture blocks to form a characteristic picture. S22. The user module divides the characteristic picture into a plurality of characteristic picture blocks, and for each characteristic picture block, the user module performs a conversion operation process on each picture element value in the characteristic picture block to obtain a converted picture element value. S23. For each characteristic picture block, the user module calculates the sum of the converted picture element values of all the picture elements in the characteristic picture block to obtain a characteristic value, and the user module generates specific data based on all the characteristic values.

5. The method according to claim 4, wherein The user module extracts non-key picture blocks from a picture, including the following steps: S211. The user module establishes a picture coordinate system with the upper left corner position of the picture as the origin, the positive direction of the x-axis horizontally to the right, and the positive direction of the y-axis vertically downward, and the user module sets a range boundary in the picture coordinate system, and the upper left corner position of the range boundary coincides with the origin. S212. The user module extracts representative data of a partial picture corresponding to the range boundary, inputs the representative data into a trained analysis model, and the analysis model outputs a probability value that the partial picture contains a target object. After each time moving the range boundary a preset length in the positive direction of the x-axis, the same analysis method is repeated until the upper right corner position of the range boundary coincides with the upper right corner position of the picture. Then, after each time moving the range boundary a preset length in the positive direction of the y-axis, this step is repeated until the lower left corner position of the range boundary coincides with the lower left corner position of the picture. S213. The user module determines a plurality of partial pictures corresponding to probability values greater than or equal to a preset first probability value threshold among all the partial pictures, and the user module calculates an evaluation value based on the probability values of all the determined partial pictures, and determines whether the evaluation value is greater than or equal to a preset evaluation value threshold. If not, it continues to the next step. If so, it removes a plurality of partial pictures corresponding to probability values less than a preset second probability value threshold among all the determined partial pictures, and continues to the next step. S214. The user module performs clustering processing on all the remaining partial images, selects the partial images with the maximum corresponding probability value in each category, and the user module removes all the selected partial images from the images to generate non-key image blocks.

6. The method according to claim 5, wherein The user module sets the second probability value threshold to be greater than the first probability value threshold.

7. A system for controlling an intelligent heater, for implementing the method according to any one of claims 1 to 6, characterized in that, It includes the following modules: The user module is used to collect control data, divide the control data into several control data groups, assign a first sequence number to each control data group according to the division order, and generate characteristic control data from all the control data groups except the control data group corresponding to the largest first sequence number according to the generated information; at the same time, it is used to perform a preset operation process on the characteristic control data and the control data group corresponding to the largest first sequence number to obtain process data, and continue to perform a concealment process on the process data using the generated specific data to obtain concealed data, and perform a storage process on the concealed data; and it is used to select a control data group with the smallest corresponding first sequence number that has not been selected in all the control data groups except the control data group corresponding to the largest first sequence number, perform a preset operation process on the selected control data group and the previously stored concealed data to obtain process data, continue to perform a concealment process on the process data using the specific data to obtain concealed data, perform a storage process on the concealed data, determine whether there is an unselected control data group, and if so, repeat this process; it is also used to sequentially connect different subsequently generated concealed data in the order of acquisition to obtain intermediate transmission data, and continue to connect the initially generated concealed data after the intermediate transmission data to obtain final transmission data, and send the final transmission data to the control module through the network module; The network module is used to transmit the final transmission data generated by the user module to the control module; The control module is used to recover the control data from the final transmission data and implement intelligent control of the heater according to the control data.

8. A device, characterized in that, It includes a memory and a processor. The memory is used to store a computer program, and the processor is used to implement the method described in any one of claims 1 to 6 when executing the computer program.

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