Intelligent furniture collaborative design method

Abstract

The invention discloses an intelligent furniture collaborative design method, and relates to the technical field of furniture collaborative processing, and the method comprises the following steps: S1, collecting a collaborative operation data packet of to-be-processed furniture equipment; s2, calculating a plurality of importance scores according to the collaborative operation data packet of the furniture equipment to be processed, and generating a performance evaluation sequence based on the plurality of importance scores; and S3, according to the performance evaluation sequence of the to-be-processed furniture equipment, determining a collaborative encryption key, performing encryption, and storing an encrypted collaborative operation data packet to a cloud. According to the intelligent furniture collaborative design method designed by the invention, a plurality of importance scores in a collaborative operation data packet are calculated, so that the collaborative operation condition of furniture equipment can be finely evaluated; and the encrypted cooperative operation data packet is stored in the cloud, so that remote access and sharing of data are realized, and cross-device cooperative work is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of furniture collaborative processing, and in particular to a method for collaborative design of intelligent furniture. Background Art

[0002] With the rapid development of smart home technology, smart furniture, as an important part of smart home, has a direct impact on the user experience and the level of intelligence of the home environment through its collaborative working ability. However, when smart furniture devices are working in collaboration, a large amount of operating data will be generated. This data not only contains the status information and working parameters of the device, but may also involve the user's privacy and usage habits. Therefore, how to efficiently and securely process these collaborative operation data has become an urgent problem to be solved in the field of smart furniture design. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a smart furniture collaborative design method.

[0004] The technical solution of the present invention is: a smart furniture collaborative design method comprises the following steps:

[0005] S1. Collect collaborative operation data packets of furniture equipment to be processed;

[0006] S2. Calculate a number of importance scores according to the collaborative operation data packet of the furniture equipment to be processed, and generate a performance evaluation sequence based on the number of importance scores;

[0007] S3. Determine the collaborative encryption key according to the performance evaluation sequence of the furniture equipment to be processed, encrypt it, and store the encrypted collaborative operation data packet in the cloud.

[0008] Furthermore, in S1, the collaborative operation data packet of the furniture equipment to be processed includes user behavior and interaction data of the furniture equipment to be processed.

[0009] User behavior data reflects long-term habits and user personalization, while interaction data records users' real-time operations.

[0010] Furthermore, S2 includes the following sub-steps:

[0011] S21, converting the collaborative operation data package into JSON format as a standard collaborative operation data package;

[0012] S22, determining the importance score of each key-value pair in the standard collaborative operation data packet;

[0013] S23. Generate a performance evaluation sequence of a standard collaborative operation data packet based on the importance scores of all key-value pairs, the ID number of the furniture device to be processed, and the remaining ID numbers of the devices associated with the furniture device to be processed.

[0014] The beneficial effect of the above further scheme is: in the present invention, the collaborative operation data packet is converted into JSON format as a standard collaborative operation data packet, which can realize the unified representation and exchange of data and reduce the complexity of data processing. The importance score of each key-value pair in the standard collaborative operation data packet is determined to make the evaluation of the data more refined. By assigning an importance score to each key-value pair, its actual value in collaborative work can be more accurately reflected. A performance evaluation sequence is generated based on the importance scores of all key-value pairs, the ID number of the furniture equipment to be processed, and the remaining ID numbers of the associated equipment, taking into account information in multiple dimensions, making the performance evaluation more comprehensive and accurate.

[0015] Further, S22 includes the following sub-steps:

[0016] S221, extracting values ​​of several key-value pairs contained in the standard collaborative operation data, randomly selecting several values ​​from the values ​​of all key-value pairs, and splitting them into several sample subsets;

[0017] S222, constructing a decision tree for each sample subset to generate a random forest;

[0018] S223. Use random forest to extract the importance score of each key-value pair in the standard collaborative operation data packet.

[0019] The beneficial effect of the above further scheme is: in the present invention, several values ​​are randomly selected from the values ​​of all key-value pairs and split into several sample subsets to avoid data overfitting and improve the generalization ability of the random forest model. The randomness makes each sample subset have a unique feature combination, thereby increasing the robustness of the random forest model. Decision trees are constructed for each sample subset to generate a random forest, which takes advantage of ensemble learning. The random forest accurately obtains the importance score of each key-value pair by combining the prediction results of multiple decision trees.

[0020] Furthermore, in S222, the maximum depth of the decision tree The calculation formula is:

[0021] ;

[0022] In the formula, represents the number of randomly picked values, Represents the mean number of child nodes generated after all nodes in the decision tree are split. represents a constant, represents the logarithmic function, Indicates rounding up.

[0023] That is, the ratio of the sum of all child nodes to the split nodes.

[0024] Further, S23 includes the following sub-steps:

[0025] S231, performing hash processing on the key of each key-value pair in the standard collaborative operation data packet to obtain a hash key value of each key-value pair;

[0026] S232. Generate a performance evaluation value for each key-value pair according to the importance score of each key-value pair and the hash key value to form a performance evaluation sequence.

[0027] The beneficial effect of the above further solution is that in the present invention, the key itself is used as an identifier to uniquely or non-uniquely identify the corresponding value in the key-value pair. In JSON data, the key is a string used to access a specific value; in a hash table, the key generates an index through a hash function, that is, the hash key value of the present invention.

[0028] Furthermore, the performance evaluation value of the key-value pair The calculation formula is:

[0029] ;

[0030] In the formula, Represents the importance score of the key-value pair, The hash key value representing the key-value pair. represents the out-of-bag error of the random forest, A hash value representing the ID number of the furniture device to be processed. Indicates the sum of the hash values ​​of the remaining ID numbers of the devices associated with the furniture device to be processed.

[0031] The out-of-bag error of a random forest is a value that measures the model's predictive performance on samples that were not trained on a single tree during training (out-of-bag samples).

[0032] Furthermore, S3 includes the following sub-steps:

[0033] S31, calculating characteristic parameters for the performance evaluation sequence;

[0034] S32, converting the timestamp of the collaborative operation data packet collected from the furniture device to be processed into a Unix timestamp, and performing hash processing on the Unix timestamp to obtain a time parameter;

[0035] S33. Generate a collaborative encryption key based on feature parameters and time parameters.

[0036] The beneficial effect of the above further scheme is: in the present invention, the timestamp of the collaborative operation data packet of the furniture equipment to be processed is converted into a Unix timestamp, so that the time format is unified. The Unix timestamp is a widely accepted standard time format, which is convenient for time comparison and calculation across systems and platforms, and the obtained time parameters increase the security of time data. The collaborative encryption key is generated based on the characteristic parameters and the time parameters, combining the information of the two dimensions of performance evaluation and time. This multi-dimensional combination makes the encryption key more complex and difficult to predict, and improves the security of encryption.

[0037] Shared encryption key The expression , represents the characteristic parameter, Represents the time parameter.

[0038] Furthermore, in S31, the characteristic parameter The calculation formula is:

[0039] ;

[0040] In the formula, Indicates the standard collaborative operation data package Performance evaluation value of key-value pairs, Indicates the standard collaborative operation data package The performance evaluation value of the key-value pairs, Indicates the standard collaborative operation data package The performance evaluation value of the key-value pairs, The total number of key-value pairs representing standard interoperability data packages.

[0041] The beneficial effects of the present invention are as follows: the smart furniture collaborative design method designed by the present invention can perform a refined evaluation of the collaborative operation of furniture equipment and generate a performance evaluation sequence by calculating several importance scores in the collaborative operation data packet; the collaborative encryption key is determined by the performance evaluation sequence and the timestamp of the collaborative operation data packet, thereby ensuring the close association between the encryption key and the device performance evaluation, improving the security and pertinence of the encryption, effectively protecting the confidentiality and integrity of the collaborative operation data of the furniture equipment, and preventing unauthorized access and tampering; and the present invention stores the encrypted collaborative operation data packet in the cloud, thereby realizing remote access and sharing of data, and facilitating collaborative work across devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Flowchart of collaborative design approach for smart furniture. DETAILED DESCRIPTION

[0043] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0044] like Figure 1 As shown, the present invention provides a smart furniture collaborative design method, comprising the following steps:

[0045] S1. Collect collaborative operation data packets of furniture equipment to be processed;

[0046] S2. Calculate a number of importance scores according to the collaborative operation data packet of the furniture equipment to be processed, and generate a performance evaluation sequence based on the number of importance scores;

[0047] S3. Determine the collaborative encryption key according to the performance evaluation sequence of the furniture equipment to be processed, encrypt it, and store the encrypted collaborative operation data packet in the cloud.

[0048] In the embodiment of the present invention, in S1, the collaborative operation data packet of the furniture equipment to be processed includes user behavior and interaction data of the furniture equipment to be processed.

[0049] User behavior data reflects long-term habits and user personalization, while interaction data records users' real-time operations.

[0050] In this embodiment of the present invention, S2 includes the following sub-steps:

[0051] S21, converting the collaborative operation data package into JSON format as a standard collaborative operation data package;

[0052] S22, determining the importance score of each key-value pair in the standard collaborative operation data packet;

[0053] S23. Generate a performance evaluation sequence of a standard collaborative operation data packet based on the importance scores of all key-value pairs, the ID number of the furniture device to be processed, and the remaining ID numbers of the devices associated with the furniture device to be processed.

[0054] In the present invention, the collaborative operation data packet is converted into JSON format as a standard collaborative operation data packet, which can realize the unified representation and exchange of data and reduce the complexity of data processing. The importance score of each key-value pair in the standard collaborative operation data packet is determined to make the evaluation of the data more refined. By assigning an importance score to each key-value pair, its actual value in collaborative work can be more accurately reflected. A performance evaluation sequence is generated based on the importance scores of all key-value pairs, the ID number of the furniture equipment to be processed, and the remaining ID numbers of the associated equipment, taking into account information in multiple dimensions, making the performance evaluation more comprehensive and accurate.

[0055] In this embodiment of the present invention, S22 includes the following sub-steps:

[0056] S221, extracting values ​​of several key-value pairs contained in the standard collaborative operation data, randomly selecting several values ​​from the values ​​of all key-value pairs, and splitting them into several sample subsets;

[0057] S222, constructing a decision tree for each sample subset to generate a random forest;

[0058] S223. Use random forest to extract the importance score of each key-value pair in the standard collaborative operation data packet.

[0059] In the present invention, several values ​​are randomly selected from the values ​​of all key-value pairs and split into several sample subsets to avoid data overfitting and improve the generalization ability of the random forest model. The randomness makes each sample subset have a unique feature combination, thereby increasing the robustness of the random forest model. A decision tree is constructed for each sample subset to generate a random forest, which takes advantage of ensemble learning. The random forest accurately obtains the importance score of each key-value pair by combining the prediction results of multiple decision trees.

[0060] In the embodiment of the present invention, in S222, the maximum depth of the decision tree The calculation formula is:

[0061] ;

[0062] In the formula, represents the number of randomly picked values, Represents the mean number of child nodes generated after all nodes in the decision tree are split. represents a constant, represents the logarithmic function, Indicates rounding up.

[0063] That is, the ratio of the sum of all child nodes to the split nodes.

[0064] In this embodiment of the present invention, S23 includes the following sub-steps:

[0065] S231, performing hash processing on the key of each key-value pair in the standard collaborative operation data packet to obtain a hash key value of each key-value pair;

[0066] S232. Generate a performance evaluation value for each key-value pair according to the importance score of each key-value pair and the hash key value to form a performance evaluation sequence.

[0067] In the present invention, the key itself is used as an identifier to uniquely or non-uniquely identify the corresponding value in the key-value pair. In JSON data, the key is a string used to access a specific value; in a hash table, the key generates an index through a hash function, i.e., the hash key value of the present invention.

[0068] In the embodiment of the present invention, the performance evaluation value of the key-value pair The calculation formula is:

[0069] ;

[0070] In the formula, Represents the importance score of the key-value pair, A hash key representing a key-value pair. represents the out-of-bag error of the random forest, A hash value representing the ID number of the furniture device to be processed. Indicates the sum of the hash values ​​of the remaining ID numbers of the devices associated with the furniture device to be processed.

[0071] The out-of-bag error of a random forest is a value that measures the model's predictive performance on samples that did not participate in the training of a single tree during training (out-of-bag samples).

[0072] In this embodiment of the present invention, S3 includes the following sub-steps:

[0073] S31, calculating characteristic parameters for the performance evaluation sequence;

[0074] S32, converting the timestamp of the collaborative operation data packet collected from the furniture device to be processed into a Unix timestamp, and performing hash processing on the Unix timestamp to obtain a time parameter;

[0075] S33. Generate a collaborative encryption key based on feature parameters and time parameters.

[0076] In the present invention, the timestamp of the collaborative operation data packet of the furniture equipment to be processed is converted into a Unix timestamp, thereby achieving the unification of the time format. The Unix timestamp is a widely accepted standard time format, which is convenient for time comparison and calculation across systems and platforms, and the obtained time parameters increase the security of time data. The collaborative encryption key is generated based on the characteristic parameters and the time parameters, combining the information of the two dimensions of performance evaluation and time. This multi-dimensional combination makes the encryption key more complex and difficult to predict, thereby improving the security of encryption.

[0077] Shared encryption key The expression , represents the characteristic parameter, Represents the time parameter.

[0078] In the embodiment of the present invention, in S31, the characteristic parameter The calculation formula is:

[0079] ;

[0080] In the formula, Indicates the standard collaborative operation data package The performance evaluation value of the key-value pairs, Indicates the standard collaborative operation data package The performance evaluation value of the key-value pairs, Indicates the standard collaborative operation data package The performance evaluation value of the key-value pairs, The total number of key-value pairs representing standard interoperability data packages.

[0081] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A smart furniture collaborative design method, characterized in that: The following steps are involved: S1. Collect collaborative operation data packets of furniture equipment to be processed; S2. Calculate a number of importance scores according to the collaborative operation data packet of the furniture equipment to be processed, and generate a performance evaluation sequence based on the number of importance scores; S3. Determine the collaborative encryption key according to the performance evaluation sequence of the furniture equipment to be processed, encrypt it, and store the encrypted collaborative operation data packet in the cloud.

2. The intelligent furniture collaborative design method according to claim 1, characterized in that: In S1, the collaborative operation data packet of the furniture equipment to be processed includes user behavior and interaction data of the furniture equipment to be processed.

3. The intelligent furniture collaborative design method according to claim 1, characterized in that: The S2 comprises the following sub-steps: S21, converting the collaborative operation data package into JSON format as a standard collaborative operation data package; S22, determining the importance score of each key-value pair in the standard collaborative operation data packet; S23. Generate a performance evaluation sequence of a standard collaborative operation data packet based on the importance scores of all key-value pairs, the ID number of the furniture device to be processed, and the remaining ID numbers of the devices associated with the furniture device to be processed.

4. The intelligent furniture collaborative design method according to claim 3, characterized in that: The S22 comprises the following sub-steps: S221, extracting values ​​of several key-value pairs contained in the standard collaborative operation data, randomly selecting several values ​​from the values ​​of all key-value pairs, and splitting them into several sample subsets; S222, constructing a decision tree for each sample subset to generate a random forest; S223. Use random forest to extract the importance score of each key-value pair in the standard collaborative operation data packet.

5. The intelligent furniture collaborative design method according to claim 4, characterized in that: In S222, the maximum depth of the decision tree The calculation formula is: ； In the formula, represents the number of randomly picked values, Represents the mean number of child nodes generated after all nodes in the decision tree are split. represents a constant, represents the logarithmic function, Indicates rounding up.

6. The intelligent furniture collaborative design method according to claim 3, characterized in that: The S23 comprises the following sub-steps: S231, performing hash processing on the key of each key-value pair in the standard collaborative operation data packet to obtain a hash key value of each key-value pair; S232. Generate a performance evaluation value for each key-value pair according to the importance score of each key-value pair and the hash key value to form a performance evaluation sequence.

7. The intelligent furniture collaborative design method according to claim 6, characterized in that: The performance evaluation value of the key-value pair The calculation formula is: ； In the formula, Represents the importance score of the key-value pair, The hash key value representing the key-value pair. represents the out-of-bag error of the random forest, A hash value representing the ID number of the furniture device to be processed. Indicates the sum of the hash values ​​of the remaining ID numbers of the devices associated with the furniture device to be processed.

8. The intelligent furniture collaborative design method according to claim 1, characterized in that: The S3 comprises the following sub-steps: S31, calculating characteristic parameters for the performance evaluation sequence; S32, converting the timestamp of the collaborative operation data packet collected from the furniture device to be processed into a Unix timestamp, and performing hash processing on the Unix timestamp to obtain a time parameter; S33. Generate a collaborative encryption key based on feature parameters and time parameters.

9. The intelligent furniture collaborative design method according to claim 8, characterized in that: In S31, the characteristic parameters The calculation formula is: ； In the formula, Indicates the standard collaborative operation data package The performance evaluation value of the key-value pairs, Indicates the standard collaborative operation data package Performance evaluation value of key-value pairs, Indicates the standard collaborative operation data package The performance evaluation value of the key-value pairs, The total number of key-value pairs representing standard interoperability data packages.

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