Air conditioner energy-saving data calculation and transmission method and system based on association verification

Abstract

The invention discloses an air conditioner energy-saving data calculation and transmission method and system based on association verification, and relates to the field of air conditioner energy-saving data processing. An air conditioner energy-saving data calculation and transmission system based on association verification comprises a multi-source data acquisition module, a data verification module, an air conditioner energy-saving optimization module, a block chain storage module and a storage verification module. According to the method, the multi-source air conditioner energy-saving data are collected, the data are verified by using the incidence relation between different data and the time sequence change relation of the data, the accuracy of the collected data is ensured, and abnormal data caused by a certain data collection error is avoided; and the energy-saving data of the air conditioner are jointly calculated through the multi-dimensional data, so that the credibility of the energy-saving data is improved.

Description

Technical Field

[0001] The present invention relates to the field of air-conditioning energy-saving data processing, and in particular to an air-conditioning energy-saving data calculation and transmission method and system based on association verification. Background Art

[0002] As the world pays more and more attention to energy efficiency and sustainable development, the energy-saving potential of air-conditioning systems, as one of the main energy-consuming equipment, has gradually been recognized. With the popularization of smart home appliances and the development of Internet of Things technology, air-conditioning systems have been able to achieve energy conservation and emission reduction through intelligent control.

[0003] Intelligent control of air conditioners relies on data processing. Existing air conditioner energy-saving data processing methods lack data security and privacy. Traditional centralized data processing methods are vulnerable to external attacks, and data security and user privacy are difficult to guarantee. Data trust is insufficient. Since data may be tampered with during transmission and processing, the authenticity and accuracy of the final data are questioned. Inefficiency. The traditional data processing process is complex and involves multiple intermediaries, resulting in low processing efficiency and high costs. There is a lack of energy-saving optimization mechanism, and data processing cannot effectively feedback control air conditioning equipment and optimize the energy-saving performance of air conditioners.

[0004] The introduction of blockchain technology in air-conditioning energy-saving data processing can solve the problems of data security and data authenticity in traditional air-conditioning energy-saving data processing by virtue of its decentralized, tamper-proof and transparent characteristics. However, the existing blockchain technology has the problem of low data processing capacity. Summary of the invention

[0005] The present invention proposes an air-conditioning energy-saving data calculation and transmission method and system based on association verification to solve the problems of low blockchain data processing capability, data security and energy-saving optimization in the prior art.

[0006] In order to achieve the above object, the present invention is implemented by the following technical solutions: A method for calculating and transmitting air conditioning energy-saving data based on association verification, comprising: S1. Collect multi-source data of air-conditioning equipment, including sensor data of the air-conditioning outdoor unit, sensor data of the air-conditioning indoor unit and multiple temperature sensor data in the room where the air-conditioning is used, and the acquired data is a series of time series data; S2. Send the multi-source data of the air-conditioning equipment into the data verification model for correlation verification and time series change relationship verification, obtain the time series change relationship confidence and correlation relationship confidence of the energy-saving data to be processed, and obtain the energy-saving confidence of the multi-source data of the air-conditioning equipment after weighted calculation, screen the multi-source data of the air-conditioning equipment according to the energy-saving confidence, obtain credible energy-saving data, use the credible energy-saving data to calculate the energy-saving value of the air-conditioning equipment, and use the data eliminated in the screening process as energy-saving abnormal data, and mark and record the energy-saving abnormal data; The energy saving value is calculated as follows: ; In the formula, D j represents the jth energy saving data, C j represents the energy saving confidence of the j-th energy saving data, M j represents the maximum credible value of the jth energy-saving data, M li represents the i-th energy-saving data to be eliminated, and γ represents the weight of the eliminated data; S3. Send the credible energy-saving data to the energy-saving optimization model for optimization identification, obtain the optimization identification results, and make energy-saving suggestions and energy-saving optimization strategy adjustments for the air-conditioning equipment based on the optimization identification results; S3. Send the credible energy-saving data to the energy-saving optimization model for optimization identification, obtain the optimization identification results, and make energy-saving suggestions and energy-saving optimization strategy adjustments for the air-conditioning equipment based on the optimization identification results; S4. Split the multi-source data of the air-conditioning equipment using a random splitting method, encrypt the split data using a multi-layer encryption algorithm to obtain energy-saving data ciphertext, store different energy-saving data ciphertexts in different addresses, simplify the multi-source data of the air-conditioning equipment to obtain simplified identifications of different data of the air-conditioning equipment, associate all simplified identifications of the air-conditioning equipment with energy-saving values, obtain air-conditioning energy-saving monitoring data, upload the air-conditioning energy-saving monitoring data to the blockchain, verify the air-conditioning energy-saving monitoring data through the energy-saving smart contract of the blockchain, and store the verified air-conditioning energy-saving monitoring data in the blockchain; S5. Randomly obtain two energy-saving data ciphertexts of the energy-saving value of an air-conditioning device, decrypt and obtain the plaintext, and then verify the data by comparing the plaintext with the simplified identifier in the air-conditioning energy-saving monitoring data and comparing the plaintext with the plaintext.

[0007] As a preferred technical solution of the present invention, the data verification model includes a data processing layer, a time series change relationship verification layer, an association relationship verification layer and a confidence acquisition layer; The data processing layer is used to perform preprocessing operations on the acquired multi-source data to obtain energy-saving data to be processed; The time series relationship verification layer is used to verify the time series change relationship of the energy-saving data to be processed. Through the pattern difference of the data in the time series change behavior, the confidence of the time series change relationship of the energy-saving data to be processed is obtained, and the time series feature data of the energy-saving data to be processed is generated; The correlation verification layer is used to verify the correlation between the time series feature data of different source data. The correlation confidence of the energy-saving data to be processed is obtained through the difference between the correlation between data and the standard correlation. The confidence acquisition layer is used to perform weighted calculations on the confidence of the temporal change relationship and the confidence of the association relationship to obtain the energy-saving confidence of each data in the multi-source data. The calculation formula of the energy-saving confidence is: ; in the formula They represent the confidence of temporal change relationship and the confidence of association relationship respectively. are the verification weights of the confidence of the temporal change relationship and the confidence of the association relationship, α a +α b =1, 0.6≥α a ≥0.2, 0.8≥α b ≥0.2.

[0008] As a preferred technical solution of the present invention, it also includes building a data verification model, and the specific steps are: A1. Obtain multi-source data of several air-conditioning equipment, perform preprocessing operations on the multi-source data and mark the time series change relationship confidence label and the correlation relationship confidence, obtain the time series feature data of the preprocessed multi-source data, form a data verification data set with the preprocessed and labeled multi-source data and time series feature data, and divide the data verification data set into a data verification training set and a data verification test set; A2. Send the data verification training set to the initial time series verification model and the initial relationship verification model built based on the deep neural network for training, with the time series change relationship confidence label and the association relationship confidence as the targets respectively, to obtain the optimized time series verification model and the optimized relationship verification model; A3. Send the output verification test set to the optimized time series verification model and the optimized relationship verification model for accuracy evaluation. Take the time series change relationship confidence label and the association relationship confidence as the targets respectively, adjust the hyperparameters of the optimized time series verification model and the optimized relationship verification model until the accuracy is within the error range. The final optimized time series verification model and the optimized relationship verification model are used as the time series change relationship verification layer and the association relationship verification layer of the data verification model. A4. The verification weights of the time series change relationship confidence labels and the association relationship confidence labels are obtained through the time series characteristics and data relationship quantities of multi-source data, and the time series change relationship confidence and the association relationship confidence are weightedly calculated according to the verification weights to obtain the energy-saving confidence of each data in the multi-source data.

[0009] As a preferred technical solution of the present invention, the energy-saving optimization model includes a data processing layer, an optimization identification layer and an optimization suggestion layer; The data processing layer is used to pre-process the trusted energy-saving data to obtain the energy-saving data to be identified; The optimization recognition layer is used to identify the energy-saving data to be identified and obtain the optimized recognition results; The optimization suggestion layer is used to match the optimization adjustment strategies in the optimization strategy library according to the optimization identification results and generate corresponding energy-saving suggestions.

[0010] As a preferred technical solution of the present invention, it also includes building an energy-saving optimization model, and the specific steps are: B1. Obtain multi-source data of several air-conditioning equipment, perform preprocessing operations on the multi-source data and mark the optimization recognition result labels, form the preprocessed and labeled multi-source data into an energy-saving optimization data set, and divide the energy-saving optimization data set into an energy-saving optimization training set and an energy-saving optimization test set; B2. Send the energy-saving optimization test set to the initial energy-saving optimization model built based on the SVM model for training, with the goal of optimizing the recognition result label to obtain an enhanced energy-saving optimization model; B3. Send the energy-saving optimization test set to the enhanced energy-saving optimization model for accuracy evaluation. With the goal of optimizing the recognition result label, adjust the hyperparameters of the enhanced energy-saving optimization model until the accuracy is within the error range. The final enhanced energy-saving optimization model is used as the optimized recognition layer of the energy-saving optimization model. B4. Match the standard optimization adjustment strategy in the optimization strategy library by optimizing the identification result label, use the matched standard optimization adjustment strategy as output, and output the corresponding energy-saving suggestion.

[0011] As a preferred technical solution of the present invention, it also includes splitting the multi-source data of the air-conditioning equipment, randomly selecting and removing part of the multi-source data, the amount of the part of the data is obtained by a random function, and the upper limit of the part of the data is less than half of the data amount.

[0012] As a preferred technical solution of the present invention, the split data is encrypted using a multi-layer encryption algorithm to obtain energy-saving data ciphertext, and the specific steps of encryption and decryption are: Data Encryption: C1. Use AES key A to encrypt the original data and obtain ciphertext C; C2. Use the RSA public key to encrypt the AES key A to obtain a fixed-length AES key hash value h; C3. Combine the ciphertext C and the AES key hash value h into a ciphertext file T; Data Decryption: C4. Extract the ciphertext C and AES key hash value h from the ciphertext file T; C5. Use the RSA private key to decrypt the AES key hash value h to obtain the AES key A; C6. Use AES key A to decrypt ciphertext C to obtain the original data.

[0013] As a preferred technical solution of the present invention, the energy-saving smart contract includes energy-saving data owner verification, data submission time verification and energy-saving location verification.

[0014] An air conditioning energy-saving data calculation and transmission system based on association verification, comprising: A multi-source data acquisition module is used to collect multi-source data of air-conditioning equipment, including sensor data of the air-conditioning outdoor unit, sensor data of the air-conditioning indoor unit, and multiple temperature sensor data in the room where the air-conditioning is used; A data verification module is used to send the multi-source data of the air-conditioning equipment into the data verification model for correlation verification and time series change relationship verification, obtain the energy-saving confidence of the multi-source data of the air-conditioning equipment, and screen the multi-source data of the air-conditioning equipment according to the energy-saving confidence to obtain credible energy-saving data, calculate the energy-saving value of the air-conditioning equipment using the credible energy-saving data, and use the data eliminated in the screening process as energy-saving abnormal data, and mark and record the energy-saving abnormal data; The air conditioning energy-saving optimization module is used to send reliable energy-saving data to the energy-saving optimization model for optimization identification, obtain optimization identification results, and make energy-saving recommendations and energy-saving optimization strategy adjustments for air conditioning equipment based on the optimization identification results; A blockchain storage module is used to split the multi-source data of the air-conditioning equipment using a random splitting method, encrypt the split data using a multi-layer encryption algorithm to obtain energy-saving data ciphertext, store different energy-saving data ciphertexts in different addresses, perform simplified identification processing on the multi-source data of the air-conditioning equipment, obtain simplified identifications of different data of the air-conditioning equipment, associate all simplified identifications of the air-conditioning equipment with energy-saving values, obtain air-conditioning energy-saving monitoring data, upload the air-conditioning energy-saving monitoring data to the blockchain, verify the air-conditioning energy-saving monitoring data through the energy-saving smart contract of the blockchain, and store the verified air-conditioning energy-saving monitoring data in the blockchain; The storage verification module is used to randomly obtain two energy-saving data ciphertexts of the energy-saving value of an air-conditioning device, decrypt and obtain the plaintext, and then verify the data by comparing the plaintext with the simplified identifier in the air-conditioning energy-saving monitoring data, and comparing the plaintext with the plaintext.

[0015] The present invention has the following advantages: 1. The present invention collects air-conditioning energy-saving data from multiple sources, obtains the confidence of the time-series change relationship of the energy-saving data to be processed according to the time-series change relationship of the data itself, obtains the confidence of the correlation relationship of the data according to the correlation relationship between different data, and obtains the energy-saving confidence of the multi-source data of the air-conditioning equipment after weighted calculation, thereby ensuring the accuracy of the collected data, avoiding abnormal data caused by errors in the collection of a certain data, and improving the credibility of the energy-saving data. The energy-saving value of the air conditioner is jointly calculated by the multi-source data and the corresponding energy-saving confidence, and the original data is weighted calculated by the energy-saving confidence. In the calculation process, the eliminated data is compound calculated by the energy-saving confidence. The energy-saving value obtained makes full use of all data on a reliable basis, thereby realizing the reliable calculation of the energy-saving value in the case of data damage.

[0016] 2. The present invention analyzes data from multiple sources, makes energy-saving recommendations and adjusts energy-saving optimization strategies for air conditioners based on the analysis results, thereby improving the dynamic adjustment capability of air conditioner energy-saving optimization.

[0017] 3. The present invention stores the energy-saving data of the air conditioner by using blockchain combined with distributed storage, prevents the security of the stored energy-saving data through the consensus mechanism of blockchain, and intelligently identifies the uploaded energy-saving data through the energy-saving smart contract of blockchain, thereby improving the standardization of energy-saving data.

[0018] 4. The present invention obtains air-conditioning energy-saving monitoring data by compressing and encrypting energy-saving data, thereby ensuring that a small amount of data such as air-conditioning energy-saving monitoring data can be efficiently stored in the blockchain, and verifies the air-conditioning energy-saving monitoring data by associating the energy-saving value of the air-conditioning equipment with a simplified identifier, thereby ensuring the credibility of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the structure of an air-conditioning energy-saving data calculation and transmission system based on association verification adopted in an embodiment of the present invention; Figure 2 The present invention is a flowchart of an air-conditioning energy-saving data calculation and transmission method based on association verification adopted in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable persons skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0021] Example 1, a method for calculating and transmitting air conditioning energy saving data based on association verification, see Figure 2 As shown, including: S1. Collect multi-source data of air-conditioning equipment, including sensor data of the air-conditioning outdoor unit, sensor data of the air-conditioning indoor unit and multiple temperature sensor data in the room where the air-conditioning is used, and the acquired data is a series of time series data; S2. Send the multi-source data of the air-conditioning equipment into the data verification model for correlation verification and time series change relationship verification, obtain the energy-saving confidence of the multi-source data of the air-conditioning equipment, and screen the multi-source data of the air-conditioning equipment according to the energy-saving confidence to obtain reliable energy-saving data, use the reliable energy-saving data to calculate the energy-saving value of the air-conditioning equipment, and use the data eliminated in the screening process as energy-saving abnormal data, and mark and record the energy-saving abnormal data; The purpose of marking and recording energy-saving abnormal data is to use these data for abnormal feedback; The data with energy-saving confidence less than the preset threshold in the multi-source data of air-conditioning equipment are removed. The preset threshold is configured according to the historical credit evaluation of the air-conditioning owner. The remaining data is used as credible energy-saving data. The energy-saving value is calculated as follows: ; In the formula, D j represents the jth energy saving data, C j represents the energy saving confidence of the j-th energy saving data, M j represents the maximum credible value of the jth energy-saving data, M li represents the i-th energy-saving data to be eliminated, γ represents the weight of the eliminated data, The value of decreases as the historical credit rating of the air conditioner owner decreases; this formula can fully combine the acquired full data to calculate the energy saving value, ensuring that the energy saving value obtained is still credible when the data is damaged; The data verification model includes a data processing layer, a time series change relationship verification layer, an association relationship verification layer, and a confidence acquisition layer; The data processing layer is used to perform preprocessing operations on the acquired multi-source data to obtain energy-saving data to be processed; The time series relationship verification layer is used to verify the time series change relationship of the energy-saving data to be processed. Through the pattern difference of the data in the time series change behavior, the confidence of the time series change relationship of the energy-saving data to be processed is obtained, and the time series feature data of the energy-saving data to be processed is generated; The correlation verification layer is used to verify the correlation between the time series feature data of different source data. The correlation confidence of the energy-saving data to be processed is obtained through the difference between the correlation between data and the standard correlation. The confidence acquisition layer is used to perform weighted calculations on the confidence of the temporal change relationship and the confidence of the association relationship to obtain the energy-saving confidence of each data in the multi-source data. The calculation formula of the energy-saving confidence is: ; in the formula They represent the confidence of temporal change relationship and the confidence of association relationship respectively. are the verification weights of the confidence of the temporal change relationship and the confidence of the association relationship, α a +α b =1, 0.6≥α a ≥0.2, 0.8≥α b ≥0.2; the values ​​of the two verification weights are determined by the time series data standardization and data standardization of the multi-source data respectively. The time series data standardization depends on the ratio of the frequency of the collected time series data to the standard frequency, and the data standardization depends on the ratio of the number of multi-source data to the number of standard multi-source data. The closer the value is to 100%, the larger it is. Specifically, when the ratio is 100%, the maximum value is taken. Through this dynamic weight setting, the energy-saving confidence calculation can be more flexibly applied to different data situations in the specific implementation process, thereby improving the robustness of the energy-saving confidence. It also includes building a data validation model, the specific steps are: A1. Obtain multi-source data of several air-conditioning equipment, perform preprocessing operations on the multi-source data and mark the time series change relationship confidence label and the correlation relationship confidence, obtain the time series feature data of the preprocessed multi-source data, form a data verification data set with the preprocessed and labeled multi-source data and time series feature data, and divide the data verification data set into a data verification training set and a data verification test set; A2. Send the data verification training set to the initial time series verification model and the initial relationship verification model built based on the deep neural network for training, with the time series change relationship confidence label and the association relationship confidence as the targets respectively, to obtain the optimized time series verification model and the optimized relationship verification model; A3. Send the output verification test set to the optimized time series verification model and the optimized relationship verification model for accuracy evaluation. Take the time series change relationship confidence label and the association relationship confidence as the targets respectively, adjust the hyperparameters of the optimized time series verification model and the optimized relationship verification model until the accuracy is within the error range. The final optimized time series verification model and the optimized relationship verification model are used as the time series change relationship verification layer and the association relationship verification layer of the data verification model. A4. The verification weights of the time series change relationship confidence labels and the association relationship confidence labels are obtained through the time series characteristics and data relationship quantities of multi-source data, and the time series change relationship confidence and the association relationship confidence are weightedly calculated according to the verification weights to obtain the energy-saving confidence of each data in the multi-source data.

[0022] S3. Send the credible energy-saving data to the energy-saving optimization model for optimization identification, obtain the optimization identification results, and make energy-saving suggestions and energy-saving optimization strategy adjustments for the air-conditioning equipment based on the optimization identification results; The energy-saving optimization model includes a data processing layer, an optimization identification layer, and an optimization suggestion layer; The data processing layer is used to pre-process the trusted energy-saving data to obtain the energy-saving data to be identified; The optimization recognition layer is used to identify the energy-saving data to be identified and obtain the optimized recognition results; The optimization suggestion layer is used to match the optimization adjustment strategies in the optimization strategy library according to the optimization identification results and generate corresponding energy-saving suggestions.

[0023] It also includes building an energy-saving optimization model, the specific steps are: B1. Obtain multi-source data of several air-conditioning equipment, perform preprocessing operations on the multi-source data and mark the optimization recognition result labels, form the preprocessed and labeled multi-source data into an energy-saving optimization data set, and divide the energy-saving optimization data set into an energy-saving optimization training set and an energy-saving optimization test set; B2. Send the energy-saving optimization test set to the initial energy-saving optimization model built based on the SVM model for training, with the goal of optimizing the recognition result label to obtain an enhanced energy-saving optimization model; B3. Send the energy-saving optimization test set to the enhanced energy-saving optimization model for accuracy evaluation. With the goal of optimizing the recognition result label, adjust the hyperparameters of the enhanced energy-saving optimization model until the accuracy is within the error range. The final enhanced energy-saving optimization model is used as the optimized recognition layer of the energy-saving optimization model. B4. Match the standard optimization adjustment strategy in the optimization strategy library by optimizing the identification result label, use the matched standard optimization adjustment strategy as output, and output the corresponding energy-saving suggestion.

[0024] S4. Split the multi-source data of the air-conditioning equipment using a random splitting method, encrypt the split data using a multi-layer encryption algorithm to obtain energy-saving data ciphertext, store different energy-saving data ciphertexts in different addresses, simplify the multi-source data of the air-conditioning equipment to obtain simplified identifications of different data of the air-conditioning equipment, associate all simplified identifications of the air-conditioning equipment with energy-saving values, obtain air-conditioning energy-saving monitoring data, upload the air-conditioning energy-saving monitoring data to the blockchain, verify the air-conditioning energy-saving monitoring data through the energy-saving smart contract of the blockchain, and store the verified air-conditioning energy-saving monitoring data in the blockchain; The multi-source data of the air-conditioning equipment is split, and part of the multi-source data is randomly selected and removed. The amount of the part of the data is obtained by a random function, and the upper limit of the part of the data is less than half of the data amount; The energy-saving smart contract includes verification of energy-saving data owner, data submission time and energy-saving location; Data owner verification is performed by comparing the submitter’s IP identity information with the identity information filled in by the submitter; Data submission time verification is verified by the maximum and average values ​​of the air-conditioning energy-saving data within the submission period; Energy-saving location verification is performed by verifying the submitter’s IP address information with the address information filled in by the submitter; The specific steps of data encryption and decryption in step S4 are: Data Encryption: C1. Use AES key A to encrypt the original data and obtain ciphertext C; C2. Use the RSA public key to encrypt the AES key A to obtain a fixed-length AES key hash value h; C3. Combine the ciphertext C and the AES key hash value h into a ciphertext file T; Data Decryption: C4. Extract the ciphertext C and AES key hash value h from the ciphertext file T; C5. Use the RSA private key to decrypt the AES key hash value h to obtain the AES key A; C6. Use AES key A to decrypt ciphertext C to obtain the original data.

[0025] The specific steps for obtaining the RSA public key and RSA private key are as follows: the system randomly generates two unequal prime numbers p and q; calculates the Euler function of p and q as the RSA public key parameter n, calculates the RSA public key parameter e through the formula gcd(e,𝜑(n))=1, and uses (e,n) as the RSA public key; uses the Euclid algorithm to calculate the d value of the RSA private key, and refers to the formula d∗e=𝑚𝑜d[(p−1)(q−1)], and uses (d,n) as the RSA private key; S5. Randomly obtain two energy-saving data ciphertexts of the energy-saving value of an air-conditioning device, decrypt and obtain the plaintext, and verify the data by comparing the plaintext with the simplified identifier in the air-conditioning energy-saving monitoring data and the plaintext with the plaintext; If the results are consistent, it means that the data is safe and has not been tampered with. If the ciphertext is inconsistent with the simplified identifier in the air-conditioning energy-saving monitoring data, it means that the data has been tampered with, and the tampering location can be obtained by comparing the plaintext with the plaintext.

[0026] Example 2, an air conditioning energy saving data calculation and transmission system based on association verification, see Figure 1 As shown, including: A multi-source data acquisition module is used to collect multi-source data of air-conditioning equipment, including sensor data of the air-conditioning outdoor unit, sensor data of the air-conditioning indoor unit, and multiple temperature sensor data in the room where the air-conditioning is used; A data verification module is used to send the multi-source data of the air-conditioning equipment into the data verification model for correlation verification and time series change relationship verification, obtain the energy-saving confidence of the multi-source data of the air-conditioning equipment, and screen the multi-source data of the air-conditioning equipment according to the energy-saving confidence to obtain credible energy-saving data, calculate the energy-saving value of the air-conditioning equipment using the credible energy-saving data, and use the data eliminated in the screening process as energy-saving abnormal data, and mark and record the energy-saving abnormal data; The air conditioning energy-saving optimization module is used to send reliable energy-saving data to the energy-saving optimization model for optimization identification, obtain optimization identification results, and make energy-saving recommendations and energy-saving optimization strategy adjustments for air conditioning equipment based on the optimization identification results; A blockchain storage module is used to split the multi-source data of the air-conditioning equipment using a random splitting method, encrypt the split data using a multi-layer encryption algorithm to obtain energy-saving data ciphertext, store different energy-saving data ciphertexts in different addresses, perform simplified identification processing on the multi-source data of the air-conditioning equipment, obtain simplified identifications of different data of the air-conditioning equipment, associate all simplified identifications of the air-conditioning equipment with energy-saving values, obtain air-conditioning energy-saving monitoring data, upload the air-conditioning energy-saving monitoring data to the blockchain, verify the air-conditioning energy-saving monitoring data through the energy-saving smart contract of the blockchain, and store the verified air-conditioning energy-saving monitoring data in the blockchain; The storage verification module is used to randomly obtain two energy-saving data ciphertexts of the energy-saving value of an air-conditioning device, decrypt and obtain the plaintext, and then verify the data by comparing the plaintext with the simplified identifier in the air-conditioning energy-saving monitoring data, and comparing the plaintext with the plaintext.

[0027] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A method for calculating and transmitting air conditioning energy-saving data based on association verification, characterized in that: include: S1. Collect multi-source data of air-conditioning equipment, including sensor data of the air-conditioning outdoor unit, sensor data of the air-conditioning indoor unit and multiple temperature sensor data in the room where the air-conditioning is used, and the acquired data is a series of time series data; S2. Send the multi-source data of the air-conditioning equipment into the data verification model for correlation verification and time series change relationship verification, obtain the time series change relationship confidence and correlation relationship confidence of the energy-saving data to be processed, and obtain the energy-saving confidence of the multi-source data of the air-conditioning equipment after weighted calculation, screen the multi-source data of the air-conditioning equipment according to the energy-saving confidence, obtain credible energy-saving data, use the credible energy-saving data to calculate the energy-saving value of the air-conditioning equipment, and use the data eliminated in the screening process as energy-saving abnormal data, and mark and record the energy-saving abnormal data; The energy saving value is calculated as follows: ； In the formula, D j represents the jth energy saving data, C j represents the energy saving confidence of the j-th energy saving data, M j represents the maximum credible value of the jth energy-saving data, M li represents the i-th energy-saving data to be eliminated, and γ represents the weight of the eliminated data; S3. Send the credible energy-saving data to the energy-saving optimization model for optimization identification, obtain the optimization identification results, and make energy-saving suggestions and energy-saving optimization strategy adjustments for the air-conditioning equipment based on the optimization identification results; S4. Split the multi-source data of the air-conditioning equipment using a random splitting method, encrypt the split data using a multi-layer encryption algorithm to obtain energy-saving data ciphertext, store different energy-saving data ciphertexts in different addresses, simplify the multi-source data of the air-conditioning equipment to obtain simplified identifications of different data of the air-conditioning equipment, associate all simplified identifications of the air-conditioning equipment with energy-saving values, obtain air-conditioning energy-saving monitoring data, upload the air-conditioning energy-saving monitoring data to the blockchain, verify the air-conditioning energy-saving monitoring data through the energy-saving smart contract of the blockchain, and store the verified air-conditioning energy-saving monitoring data in the blockchain; S5. Randomly obtain two energy-saving data ciphertexts of the energy-saving value of an air-conditioning device, decrypt and obtain the plaintext, and then verify the data by comparing the plaintext with the simplified identifier in the air-conditioning energy-saving monitoring data and comparing the plaintext with the plaintext.

2. The method for calculating and transmitting air-conditioning energy-saving data based on association verification according to claim 1, characterized in that: The data verification model includes a data processing layer, a time series change relationship verification layer, an association relationship verification layer, and a confidence acquisition layer; The data processing layer is used to perform preprocessing operations on the acquired multi-source data to obtain energy-saving data to be processed; The time series relationship verification layer is used to verify the time series change relationship of the energy-saving data to be processed. Through the pattern difference of the data in the time series change behavior, the confidence of the time series change relationship of the energy-saving data to be processed is obtained, and the time series feature data of the energy-saving data to be processed is generated; The correlation verification layer is used to verify the correlation between the time series feature data of different source data. The correlation confidence of the energy-saving data to be processed is obtained through the difference between the correlation between data and the standard correlation. The confidence acquisition layer is used to perform weighted calculations on the confidence of the temporal change relationship and the confidence of the association relationship to obtain the energy-saving confidence of each data in the multi-source data. The calculation formula of the energy-saving confidence is: ； in the formula They represent the confidence of temporal change relationship and the confidence of association relationship respectively. are the verification weights of the confidence of the temporal change relationship and the confidence of the association relationship, α a +α b =1, 0.6≥α a ≥0.2, 0.8≥α b ≥0.

2.

3. The method for calculating and transmitting air-conditioning energy-saving data based on association verification according to claim 2, characterized in that: It also includes building a data validation model, the specific steps are: A1. Obtain multi-source data of several air-conditioning equipment, perform preprocessing operations on the multi-source data and mark the time series change relationship confidence label and the correlation relationship confidence, obtain the time series feature data of the preprocessed multi-source data, form a data verification data set with the preprocessed and labeled multi-source data and time series feature data, and divide the data verification data set into a data verification training set and a data verification test set; A2. Send the data verification training set to the initial time series verification model and the initial relationship verification model built based on the deep neural network for training, with the time series change relationship confidence label and the association relationship confidence as the targets respectively, to obtain the optimized time series verification model and the optimized relationship verification model; A3. Send the output verification test set to the optimized time series verification model and the optimized relationship verification model for accuracy evaluation. Take the time series change relationship confidence label and the association relationship confidence as the targets respectively, adjust the hyperparameters of the optimized time series verification model and the optimized relationship verification model until the accuracy is within the error range. The final optimized time series verification model and the optimized relationship verification model are used as the time series change relationship verification layer and the association relationship verification layer of the data verification model. A4. The verification weights of the time series change relationship confidence labels and the association relationship confidence labels are obtained through the time series characteristics and data relationship quantities of multi-source data, and the time series change relationship confidence and the association relationship confidence are weightedly calculated according to the verification weights to obtain the energy-saving confidence of each data in the multi-source data.

4. The method for calculating and transmitting air conditioning energy-saving data based on association verification according to claim 1, characterized in that: The energy-saving optimization model includes a data processing layer, an optimization identification layer, and an optimization suggestion layer; The data processing layer is used to pre-process the trusted energy-saving data to obtain the energy-saving data to be identified; The optimization recognition layer is used to identify the energy-saving data to be identified and obtain the optimized recognition results; The optimization suggestion layer is used to match the optimization adjustment strategies in the optimization strategy library according to the optimization identification results and generate corresponding energy-saving suggestions.

5. The method for calculating and transmitting air-conditioning energy-saving data based on association verification according to claim 4, characterized in that: It also includes building an energy-saving optimization model, the specific steps are: B1. Obtain multi-source data of several air-conditioning equipment, perform preprocessing operations on the multi-source data and mark the optimization recognition result labels, form the preprocessed and labeled multi-source data into an energy-saving optimization data set, and divide the energy-saving optimization data set into an energy-saving optimization training set and an energy-saving optimization test set; B2. Send the energy-saving optimization test set to the initial energy-saving optimization model built based on the SVM model for training, with the goal of optimizing the recognition result label to obtain an enhanced energy-saving optimization model; B3. Send the energy-saving optimization test set to the enhanced energy-saving optimization model for accuracy evaluation. With the goal of optimizing the recognition result label, adjust the hyperparameters of the enhanced energy-saving optimization model until the accuracy is within the error range. The final enhanced energy-saving optimization model is used as the optimized recognition layer of the energy-saving optimization model. B4. Match the standard optimization adjustment strategy in the optimization strategy library by optimizing the identification result label, use the matched standard optimization adjustment strategy as output, and output the corresponding energy-saving suggestion.

6. The method for calculating and transmitting air-conditioning energy-saving data based on association verification according to claim 1, characterized in that: It also includes splitting the multi-source data of the air-conditioning equipment, randomly selecting and removing part of the multi-source data, the amount of the part of the data is obtained by a random function, and the upper limit of the part of the data is less than half of the data amount.

7. The method for calculating and transmitting air-conditioning energy-saving data based on association verification according to claim 1, characterized in that: It also includes encrypting the split data using a multi-layer encryption algorithm to obtain energy-saving data ciphertext. The specific steps of encryption and decryption are: Data Encryption: C1. Use AES key A to encrypt the original data and obtain ciphertext C; C2. Use the RSA public key to encrypt the AES key A to obtain a fixed-length AES key hash value h; C3. Combine the ciphertext C and the AES key hash value h into a ciphertext file T; Data Decryption: C4. Extract the ciphertext C and AES key hash value h from the ciphertext file T; C5. Use the RSA private key to decrypt the AES key hash value h to obtain the AES key A; C6. Use AES key A to decrypt ciphertext C to obtain the original data.

8. The method for calculating and transmitting air-conditioning energy-saving data based on association verification according to claim 1, characterized in that: The energy-saving smart contract includes energy-saving data owner verification, data submission time verification and energy-saving location verification.

9. An air conditioning energy-saving data calculation and transmission system based on association verification, characterized in that The system applies the air conditioning energy-saving data calculation and transmission method based on association verification as described in any one of claims 1 to 8, including: A multi-source data acquisition module is used to collect multi-source data of air-conditioning equipment, including sensor data of the air-conditioning outdoor unit, sensor data of the air-conditioning indoor unit, and multiple temperature sensor data in the room where the air-conditioning is used; A data verification module is used to send the multi-source data of the air-conditioning equipment into the data verification model for correlation verification and time series change relationship verification, obtain the energy-saving confidence of the multi-source data of the air-conditioning equipment, and screen the multi-source data of the air-conditioning equipment according to the energy-saving confidence to obtain credible energy-saving data, calculate the energy-saving value of the air-conditioning equipment using the credible energy-saving data, and use the data eliminated in the screening process as energy-saving abnormal data, and mark and record the energy-saving abnormal data; The air conditioning energy-saving optimization module is used to send reliable energy-saving data to the energy-saving optimization model for optimization identification, obtain optimization identification results, and make energy-saving recommendations and energy-saving optimization strategy adjustments for air conditioning equipment based on the optimization identification results; A blockchain storage module is used to split the multi-source data of the air-conditioning equipment using a random splitting method, encrypt the split data using a multi-layer encryption algorithm to obtain energy-saving data ciphertext, store different energy-saving data ciphertexts in different addresses, perform simplified identification processing on the multi-source data of the air-conditioning equipment, obtain simplified identifications of different data of the air-conditioning equipment, associate all simplified identifications of the air-conditioning equipment with energy-saving values, obtain air-conditioning energy-saving monitoring data, upload the air-conditioning energy-saving monitoring data to the blockchain, verify the air-conditioning energy-saving monitoring data through the energy-saving smart contract of the blockchain, and store the verified air-conditioning energy-saving monitoring data in the blockchain; The storage verification module is used to randomly obtain two energy-saving data ciphertexts of the energy-saving value of an air-conditioning device, decrypt and obtain the plaintext, and then verify the data by comparing the plaintext with the simplified identifier in the air-conditioning energy-saving monitoring data, and comparing the plaintext with the plaintext.

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