Data transmission method and device of intelligent remote water meter

Abstract

The invention discloses a data transmission method and device of an intelligent remote water meter, and relates to the technical field of data transmission. Obtaining record data of all water meters in the target area, wherein the record data of each water meter carries a water meter ID; searching a preset database according to the ID of the target water meter to determine historical storage data, and determining effective uploading data according to the historical storage data and the target record data; obtaining all effective uploaded data, performing data integration on all the effective uploaded data to obtain target integrated data, and performing data encryption on the target integrated data through a preset encryption algorithm to obtain target encrypted data; and uploading the target encrypted data to the cloud. Through screening of the effectively uploaded data instead of regular batch uploading of all data, redundant data transmission can be reduced, network traffic can be remarkably reduced, encrypted transmission is carried out on the screened data, it is ensured that the uploaded data cannot be intercepted or tampered in the transmission process, and the security of the data is ensured while the system efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data transmission, and particularly relates to a data transmission method and device for an intelligent remote water meter. Background Art

[0002] With the continuous advancement of the construction of smart cities, the demand for digitalization and intelligentization of water resource management is increasing day by day. As a core device, intelligent water meters have gradually replaced traditional mechanical water meters, realizing functions such as remote monitoring, automatic metering, and intelligent analysis. However, at present, the data upload method of intelligent water meters mainly adopts periodic acquisition and upload, that is, batch transmission of data at fixed time intervals. Although this method can meet the basic monitoring requirements, when deployed on a large scale, it will lead to a sharp increase in data volume, occupy a large amount of network bandwidth, and increase storage and computing costs.

[0003] Patent CN117729241B discloses an intelligent water meter remote data acquisition method and platform. By obtaining the location of the installed intelligent water meters, constructing a water meter distribution map, and determining the movement path of the signal interaction end according to the water meter distribution map; regularly obtaining the location of the signal interaction end, and determining the data acquisition area according to the location of the signal interaction end; sending a trigger signal to all intelligent water meters in the data acquisition area, receiving the water consumption amount feedback by the intelligent water meters; merging the water consumption amounts according to the locations of the intelligent water meters, and sending the merged water consumption amounts to the cloud platform. By introducing a mobile transfer port between the intelligent water meters and the master control end, the data upload function of the intelligent water meters is in a sleep state for most of the time, and the data transmission distance is greatly reduced, effectively alleviating the data upload pressure of each intelligent water meter. However, this method still uploads a large amount of useless data to the cloud, increasing the storage and computing burden of the cloud, affecting the system efficiency, and the uploaded data is also easily intercepted and tampered with, affecting the confidentiality and integrity of the data. Summary of the Invention

[0004] The object of the present invention is to solve the above problems and propose a data transmission method and device for an intelligent remote water meter.

[0005] In the first aspect of the implementation of the present invention, a data transmission method for an intelligent remote water meter is first proposed. The method includes:

[0006] Obtain the recorded data of all water meters in the target area. The recorded data of each water meter carries a water meter ID; each water meter has a unique identity ID;

[0007] Find the historical saved data by looking up the preset database according to the ID of the target water meter, and determine the effective upload data based on the historical saved data and the target record data; the preset database is used to save all the record data uploaded to the cloud; the target water meter is any one of all water meters; the target record data is the record data of the target water meter;

[0008] Obtain all the effective upload data, perform data integration on all the effective upload data to obtain the target integrated data, and encrypt the target integrated data through a preset encryption algorithm to obtain the target encrypted data;

[0009] Upload the target encrypted data to the cloud.

[0010] Optionally, determining the effective upload data according to the historical saved data and the target record data includes:

[0011] Obtain the record data that was last uploaded to the cloud in the historical saved data to get the comparison data;

[0012] Calculate the difference between the target record data and the comparison data to get the target comparison value;

[0013] Determine the upload threshold corresponding to the target water meter according to the historical saved data;

[0014] If the target comparison value is greater than the upload threshold, record the upload data as effective upload data.

[0015] Optionally, determining the upload threshold corresponding to the target water meter according to the historical saved data includes:

[0016] Obtain the target data set of the first preset number of record data before the current moment in the historical saved data;

[0017] Through the formula ΔV i =V i+1 -V i Calculate the difference between two adjacent record data in the target data set;

[0018] where V i +1 is the record data uploaded to the cloud for the (i + 1)-th time, V i is the record data uploaded to the cloud for the i-th time, and ΔV i is the difference between the (i + 1)-th upload and the i-th upload data;

[0019] Generate the weight of the difference between each upload data through w i =e -λ(N-i) where w i is for ΔV iThe corresponding weight, λ is the attenuation coefficient, and N is the number of the previous preset recorded data;

[0020] Through Obtain the weighted average change amount of the difference between the uploaded data;

[0021] Through Obtain the upload threshold;

[0022] Among them, k is the adjustment coefficient, α is the time influence factor, Vmin is the minimum upload threshold, T is the time since the last upload, Tmax is the maximum allowed non-upload time, and ΔV threshold Is the upload threshold.

[0023] Optionally, performing data integration on all valid uploaded data to obtain the target integrated data includes:

[0024] After aligning all the valid uploaded data by identity ID and time, convert all the data into JSON format and merge them to obtain the target integrated data.

[0025] Optionally, performing data encryption on the target integrated data through a preset encryption algorithm to obtain the target encrypted data includes:

[0026] After converting the target integrated data into ASCII values, convert the ASCII values into 8-bit binary data to obtain the data to be encrypted, and convert the preset private key into a binary value to obtain the target private key;

[0027] Perform an exclusive OR operation on the data to be encrypted and the target private key to obtain the initial encrypted data;

[0028] Convert the initial encrypted data into ASCII values to obtain the first encrypted data, and divide the first encrypted data into 256-bit blocks to obtain the first sub-encrypted data set;

[0029] Convert the first sub-encrypted data set into a DNA sequence through the ATCG supplementation rule to obtain the target encrypted data.

[0030] In the second aspect of the implementation of the present invention, a data transmission device for an intelligent remote water meter is proposed, including:

[0031] A water meter record data acquisition module, configured to acquire the record data of all water meters in the target area, and the record data of each water meter carries a water meter ID; each water meter has a unique identity ID;

[0032] An effective upload data determination module, configured to find historical saved data by querying a preset database according to the ID of a target water meter, and determine effective upload data based on the historical saved data and target record data; the preset database is used to save all record data uploaded to the cloud; the target water meter is any one of all water meters; the target record data is the record data of the target water meter;

[0033] A data encryption module, configured to obtain all effective upload data, perform data integration on all effective upload data to obtain target integrated data, and perform data encryption on the target integrated data through a preset encryption algorithm to obtain target encrypted data;

[0034] A data upload module, configured to upload the target encrypted data to the cloud.

[0035] Optionally, the effective upload data determination module includes:

[0036] A comparison data determination module, configured to obtain the record data that was last uploaded to the cloud in the historical saved data as comparison data;

[0037] A first difference calculation module, configured to calculate the difference between the target record data and the comparison data to obtain a target comparison value;

[0038] An upload threshold determination module, configured to determine the upload threshold corresponding to the target water meter according to the historical saved data;

[0039] An effective upload data judgment module, configured to, if the target comparison value is greater than the upload threshold, record the upload data as effective upload data.

[0040] Optionally, the upload threshold determination module includes:

[0041] A target data set determination module, configured to obtain the target data set by acquiring the previous preset number of record data in the historical saved data at the current moment;

[0042] A second difference calculation module, configured to calculate the difference between adjacent two record data in the target data set through the formula ΔV i =V i+1 -V i ;

[0043] where V i+1 is the record data uploaded to the cloud for the (i + 1)-th time, V i is the record data uploaded to the cloud for the i-th time, and ΔV i is the difference between the (i + 1)-th upload and the i-th upload data;

[0044] A difference weight determination module, configured to determine through w i =e-λ(N-i) Generate the weights of the differences between each uploaded data, where w i is the weight corresponding to ΔV i , λ is the attenuation coefficient, and N is the number of the previous preset recorded data;

[0045] Weighted average change amount determination module, which is used to obtain the weighted average change amount of the differences between the uploaded data;

[0046] Upload threshold generation module, which is used to

[0047] obtain the upload threshold;

[0048] where k is the adjustment coefficient, α is the time influence factor, Vmin is the minimum upload threshold, T is the time since the last upload, Tmax is the maximum allowed non-upload time, and ΔV threshold is the upload threshold.

[0049] Optionally, the data encryption module includes:

[0050] Data integration module, which is used to align all valid uploaded data by identity ID and time, and then convert all data into JSON format for merging to obtain the target integrated data.

[0051] Optionally, the data encryption module further includes:

[0052] Binary conversion module, which is used to convert the target integrated data into ASCII values, then convert the ASCII values into 8-bit binary data to obtain the data to be encrypted, and convert the preset private key into a binary value to obtain the target private key;

[0053] Exclusive OR operation module, which is used to perform an exclusive OR operation on the data to be encrypted and the target private key to obtain the initial encrypted data;

[0054] Encrypted data splitting module, which is used to convert the initial encrypted data into ASCII values to obtain the first encrypted data, and divide the first encrypted data into 256-bit blocks to obtain the first sub-encrypted data set;

[0055] Target encrypted data generation module, which is used to convert the first sub-encrypted data set into a DNA sequence through the ATCG supplementation rule to obtain the target encrypted data.

[0056] Advantages of the present invention:

[0057] The present invention provides a data transmission method for an intelligent remote water meter, which acquires the recorded data of all water meters in a target area, and the recorded data of each water meter carries a water meter ID; determines the historically saved data by looking up a preset database according to the ID of the target water meter, and determines the effectively uploadable data based on the historically saved data and the target recorded data; acquires all the effectively uploadable data, integrates all the effectively uploadable data to obtain target integrated data, and encrypts the target integrated data through a preset encryption algorithm to obtain target encrypted data; uploads the target encrypted data to the cloud. By screening the effectively uploadable data instead of regularly uploading all data in batches, redundant data transmission can be reduced, and network traffic can be significantly reduced. Then, the screened data is encrypted and transmitted to ensure that the uploaded data will not be intercepted or tampered with during the transmission process, which improves the system efficiency and ensures the security of the data at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described below with reference to the accompanying drawings.

[0059] Figure 1 It is a flowchart of a data transmission method for an intelligent remote water meter provided by an embodiment of the present invention;

[0060] Figure 2 It is a schematic structural diagram of a data transmission device for an intelligent remote water meter provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The term "and / or" in this article is only a description of the associated relationship of the associated objects, indicating that there can be three relationships. For example, A and B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the descriptions such as "first" and "second" in the present invention are only for the purpose of description, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the fact that those skilled in the art can implement them. When the combination of the technical solutions appears to be contradictory or unable to be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0062] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0063] An embodiment of the present invention provides a data transmission method for an intelligent remote water meter. Refer to Figure 1 , Figure 1 which is a flowchart of a data transmission method for an intelligent remote water meter provided by an embodiment of the present invention. The method includes the following steps:

[0064] S101, obtain the record data of all water meters in the target area, and the record data of each water meter carries a water meter ID;

[0065] S102, find the historical saved data in the preset database according to the ID of the target water meter, and determine the valid upload data according to the historical saved data and the target record data;

[0066] S103, obtain all valid upload data, perform data integration on all valid upload data to obtain target integrated data, and perform data encryption on the target integrated data through a preset encryption algorithm to obtain target encrypted data;

[0067] S104, upload the target encrypted data to the cloud.

[0068] Among them, each water meter has a unique identity ID; the preset database is used to save all the record data uploaded to the cloud; the target water meter is any one of all the water meters; the target record data is the record data of the target water meter.

[0069] Based on the data transmission method for an intelligent remote water meter provided by an embodiment of the present invention, by screening the valid upload data instead of regularly uploading all data in batches, redundant data transmission can be reduced, the network traffic can be significantly reduced, and then the screened data is encrypted and transmitted to ensure that the uploaded data will not be intercepted or tampered with during the transmission process, which can improve the system efficiency while ensuring the security of the data.

[0070] In one implementation, by comparing the historical data of the water meter with the new records, it is ensured that only valid data is uploaded, avoiding redundant data occupying storage space; the preset database stores the uploaded data, which can effectively avoid repeated uploading of the same data and improve the system efficiency;

[0071] In one implementation, the target area is the area where water flow statistics need to be carried out; the historical saved data is the historical valid upload data recorded by the current ID.

[0072] In one implementation, a public key and a corresponding private key are generated through a commonly used key generation method on the market, the target integrated data is encrypted with the private key to obtain the target encrypted data, and after the cloud obtains the target encrypted data, it is decrypted with the public key to obtain the uploaded water meter record data.

[0073] In one implementation, a preset encryption algorithm is used to encrypt the data to ensure that the water meter data will not be tampered with or leaked during transmission, improving data security; all valid data is integrated to optimize the data before uploading, improving the efficiency of subsequent data analysis and processing.

[0074] In one implementation, only valid data is uploaded, reducing the data transmission volume, lowering the cloud storage cost, and improving the system resource utilization rate.

[0075] In one embodiment, determining the valid upload data according to the historical saved data and the target record data includes:

[0076] Obtaining the comparison data by getting the record data that was last uploaded to the cloud in the historical saved data;

[0077] Calculating the difference between the target record data and the comparison data to obtain the target comparison value;

[0078] Determining the upload threshold corresponding to the target water meter according to the historical saved data;

[0079] If the target comparison value is greater than the upload threshold, the upload data is recorded as valid upload data.

[0080] In one implementation, only when the change in the water meter data exceeds the set upload threshold will the data be recognized as valid upload data, thus avoiding frequent uploads caused by minor changes and reducing redundant data; by comparing the last uploaded data, it is ensured that the uploaded data is meaningful, reducing the invalid data stored in the cloud and improving the storage utilization rate.

[0081] In one implementation, it is avoided that frequent uploads are caused by minor changes in the data, reducing unnecessary network traffic consumption and improving the overall performance of the system; reducing the processing volume of invalid data, making the data upload and processing more efficient, and improving the operating efficiency of the overall system.

[0082] In one implementation, if the target comparison value is less than or equal to the preset threshold, the data will not be uploaded, but this collection is within a cycle (small cycle). When it reaches a large cycle (the large cycle should be at least greater than 5 small cycles), all data needs to be uploaded; the time lengths of the large cycle and the small cycle are determined by technical personnel.

[0083] In one implementation, the upload threshold is dynamically adjusted according to the historical data of different water meters to ensure that different types of water meters can upload data within a reasonable change range, enhancing the adaptability of the system, reducing the consumption of storage and computing resources, lowering the operating cost of the cloud server, and at the same time reducing the workload of maintenance personnel in processing redundant data.

[0084] In one embodiment, determining the upload threshold corresponding to the target water meter according to the historical saved data includes:

[0085] Obtain the target data set by acquiring the previous preset number of recorded data in the historical saved data at the current moment;

[0086] Through the formula ΔV i =V i+1 -V i Calculate the difference between two adjacent recorded data in the target data set;

[0087] Where V i+1 is the recorded data uploaded to the cloud for the (i + 1)-th time, V i is the recorded data uploaded to the cloud for the i-th time, and ΔV i is the difference between the (i + 1)-th upload and the i-th upload data;

[0088] Generate the weight of the difference between each upload data through w i =e -λ(N-i) where w i is the weight corresponding to ΔV i , λ is the attenuation coefficient, and N is the number of the previous preset number of recorded data;

[0089] Through Obtain the weighted average change amount of the difference between upload data;

[0090] Through Obtain the upload threshold;

[0091] Where k is the adjustment coefficient, α is the time influence factor, Vmin is the minimum upload threshold, T is the time since the last upload, Tmax is the maximum allowable non-upload time, and ΔV threshold is the upload threshold.

[0092] In one implementation, by calculating the weighted average change amount of historical data, the upload threshold is dynamically adjusted instead of being a fixed value, improving the adaptability of the system to different water usage patterns; considering the time factor, when there is no upload for a long time, the threshold is automatically reduced to ensure that key data is not missed.

[0093] In one implementation, through weighted calculation, frequent uploads caused by minor data fluctuations in a short period are reduced, and unnecessary data transmission is decreased; only when the data change reaches a certain degree will the upload be triggered, thereby reducing storage pressure and bandwidth consumption.

[0094] In one implementation, the data in the target dataset is numbered, where the value range of i is [1, N), and the value of λ is between 0.45 and 0.55; the larger λ is, the more emphasis is placed on the most recent data; the smaller λ is, the greater the influence of historical data; the adjustment coefficient K (range 1 to 1.8), the time influence factor α, and the minimum upload threshold Vmin are all determined by the technical personnel; the following is a specific example. The data recorded in the target dataset are 100 (number 1), 102 (number 2), 105 (number 3), and 107 (number 4) respectively. Taking λ as 0.5, then through the formula ΔV i =V i+1 -V i to obtain ΔV 1＝2 , ΔV 2＝3 , ΔV 3＝2 , and then through w i =e -λ(N-i) formula to obtain the weights, w 1 =e -0.5(4-1) =0.223, w 2 =e -0.5(4-2) =0.368, and w 3 =e -0.5(4-3) =0.607, and then through calculate to get 2.02. At this time, if the value of K is 1.1, the upload threshold is 2.222. At this time, if the difference between the currently uploaded record data and 107 is greater than 2.222, then the currently uploaded data is recorded as valid upload data; if the data remains unchanged for a long time, the change amount is always 0. At this time, the determination of the upload threshold is through to determine. When T is equal to Tmax, it is directly uploaded. Here, Tmax is the maximum allowable non-upload time (large cycle), and Vmin is the minimum upload threshold in the range of (0 to 1), which is specifically determined by the technical personnel.

[0095] In one implementation, through the Tmax (maximum non-upload time) mechanism, even if the data changes slightly, it can ensure that it is forced to be uploaded once within the set time, preventing the situation of no data update for a long time. Vmin (minimum upload threshold) ensures that even if the water consumption is small, it can be triggered to upload within a reasonable range, avoiding the omission of abnormal data.

[0096] In one implementation, through dynamic threshold adjustment, abnormal water use behaviors (such as sudden increases or decreases) can be captured more accurately, improving the abnormal recognition ability of the water meter monitoring system.

[0097] In one embodiment, data integration is performed on all valid upload data to obtain the target integrated data including:

[0098] After aligning all valid uploaded data by identity ID and time, all the data is converted into JSON format and merged to obtain the target integrated data.

[0099] In one implementation, the JSON format is a lightweight and structured data exchange format with strong compatibility, facilitating data transmission and parsing between different systems; alignment by identity ID ensures that data of the same user or device will not be lost or confused during merging; alignment by time guarantees the continuity of the data time series and avoids data analysis errors caused by time disorder.

[0100] In one embodiment, data encryption of the target integrated data is performed through a preset encryption algorithm to obtain the target encrypted data, including:

[0101] After converting the target integrated data into ASCII values, the ASCII values are converted into 8-bit binary data to obtain the data to be encrypted, and the preset private key is converted into a binary value to obtain the target private key;

[0102] An exclusive OR operation is performed on the data to be encrypted and the target private key to obtain the initial encrypted data;

[0103] The initial encrypted data is converted into ASCII values to obtain the first encrypted data, and the first encrypted data is divided into 256-bit blocks to obtain the first sub-encrypted data set;

[0104] The first sub-encrypted data set is converted into a DNA sequence through the ATCG supplementation rule to obtain the target encrypted data.

[0105] In one implementation, through multiple steps of conversion (ASCII → binary → XOR → DNA sequence), even if a certain layer is cracked, other layers still need to be decrypted, improving security.

[0106] In one implementation, after the cloud obtains the target encrypted data, decryption is required. The decryption process is the reverse of the encryption process, and the steps are as follows: unlocking the DNA data with the key, converting the DNA sequence back into binary data, performing the inverse exclusive OR operation, restoring the ASCII code, and obtaining the original data.

[0107] In one implementation, using the four bases A, T, C, and G to represent data can store more information than binary (0 and 1), which is suitable for large-scale data storage; DNA storage is a cutting-edge data storage technology. Compared with traditional hard disk storage, it can store a large amount of data in an extremely small physical space and is stable for a long time.

[0108] Based on the same inventive concept, the embodiments of the present invention also provide a data transmission device for an intelligent remote water meter. See Figure 2 , Figure 2The structural schematic diagram of a data transmission device for an intelligent remote water meter provided by an embodiment of the present invention includes:

[0109] A water meter record data acquisition module, configured to acquire the record data of all water meters in the target area, and the record data of each water meter carries a water meter ID; each water meter has a unique identity ID;

[0110] An effective upload data determination module, configured to find the historically saved data according to the ID of the target water meter in a preset database, and determine the effective upload data according to the historically saved data and the target record data; the preset database is used to save all the record data uploaded to the cloud; the target water meter is any one of all water meters; the target record data is the record data of the target water meter;

[0111] A data encryption module, configured to acquire all the effective upload data, integrate all the effective upload data to obtain target integrated data, and encrypt the target integrated data through a preset encryption algorithm to obtain target encrypted data;

[0112] A data upload module, configured to upload the target encrypted data to the cloud.

[0113] Based on the data transmission device for an intelligent remote water meter provided by an embodiment of the present invention, by screening the effective upload data instead of regularly uploading all data in batches, redundant data transmission can be reduced, the network traffic can be significantly reduced, and then the screened data is encrypted and transmitted to ensure that the uploaded data will not be intercepted or tampered with during the transmission process, which not only improves the system efficiency but also ensures the security of the data.

[0114] In one embodiment, the effective upload data determination module includes:

[0115] A comparison data determination module, configured to acquire the record data that was last uploaded to the cloud in the historically saved data to obtain comparison data;

[0116] A first difference calculation module, configured to calculate the difference between the target record data and the comparison data to obtain a target comparison value;

[0117] An upload threshold determination module, configured to determine the upload threshold corresponding to the target water meter according to the historically saved data;

[0118] An effective upload data judgment module, configured to, if the target comparison value is greater than the upload threshold, record the upload data as effective upload data.

[0119] In one embodiment, the upload threshold determination module includes:

[0120] A target data set determination module, configured to acquire the target data set by obtaining the preset number of record data before the current moment in the historically saved data;

[0121] A second difference calculation module, configured to calculate the difference between adjacent two record data in a target dataset through the formula ΔV i =V i+1 -V i ;

[0122] where V i+1 is the record data uploaded to the cloud for the (i + 1)-th time, V i is the record data uploaded to the cloud for the i-th time, and ΔV i is the difference between the data uploaded for the (i + 1)-th time and the data uploaded for the i-th time;

[0123] A difference weight determination module, configured to generate the weight of the difference between each pair of uploaded data through w i =e -λ(N-i) ; where w i is the weight corresponding to ΔV i , λ is the attenuation coefficient, and N is the number of the previous preset record data;

[0124] A weighted average change amount determination module, configured to obtain the weighted average change amount of the difference between the uploaded data through ;

[0125] An upload threshold generation module, configured to obtain the upload threshold through

[0126] ;

[0127] where k is an adjustment coefficient, α is a time influence factor, Vmin is the minimum upload threshold, T is the time since the last upload, Tmax is the maximum allowed non-upload time, and ΔV threshold is the upload threshold.

[0128] In one embodiment, the data encryption module includes:

[0129] A data integration module, configured to align all valid uploaded data according to the identity ID and time, and then convert all the data into the JSON format and merge them to obtain the target integrated data.

[0130] In one embodiment, the data encryption module further includes:

[0131] A binary conversion module, configured to convert the target integrated data into ASCII values, then convert the ASCII values into 8-bit binary data to obtain the data to be encrypted, and convert the preset private key into a binary value to obtain the target private key;

[0132] An exclusive OR operation module, configured to perform an exclusive OR operation on the data to be encrypted and the target private key to obtain the initial encrypted data;

[0133] An encrypted data splitting module, configured to convert initial encrypted data into ASCII values to obtain first encrypted data, and divide the first encrypted data into 256-bit blocks to obtain a first sub-encrypted data set;

[0134] A target encrypted data generation module, configured to convert the first sub-encrypted data set into a DNA sequence through an ATCG supplementation rule to obtain target encrypted data.

[0135] The above has described an embodiment of the present invention in detail, but the content is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A data transmission method for a smart remote water meter, characterized in that: The method comprises: Obtain the record data of all water meters in the target area. The record data of each water meter carries the water meter ID; each water meter has a unique identity ID; Searching a preset database according to the ID of the target water meter to determine the historically saved data, and determining the valid uploaded data according to the historically saved data and the target recorded data; The preset database is used to store all recorded data uploaded to the cloud; the target water meter is any one of all water meters; the target recorded data is the recorded data of the target water meter; Acquire all valid uploaded data, perform data integration on all valid uploaded data to obtain target integrated data, and perform data encryption on the target integrated data using a preset encryption algorithm to obtain target encrypted data; The target encrypted data is uploaded to the cloud.

2. The data transmission method of a smart remote water meter according to claim 1, characterized in that: Determining the valid uploaded data according to the historical saved data and the target recorded data includes: Obtaining the most recent record data uploaded to the cloud from the historically saved data to obtain comparison data; Calculating the difference between the target recorded data and the comparison data to obtain a target comparison value; Determine an upload threshold corresponding to the target water meter according to the historically saved data; If the target comparison value is greater than the upload threshold, the uploaded data is recorded as valid uploaded data.

3. The data transmission method of a smart remote water meter according to claim 2, characterized in that: Determining the upload threshold corresponding to the target water meter according to the historically saved data includes: Obtaining a preset number of record data at the current moment in the historically saved data to obtain a target data set; By the formula ΔV i =V i+1 -V i Calculate the difference between two adjacent record data in the target data set; Where V i+1 is the recorded data uploaded to the cloud for the i+1th time, V i is the recorded data uploaded to the cloud for the i-th time, ΔV i is the difference between the data uploaded at the i+1th time and the data uploaded at the ith time; By w i =e -λ(N-i) Generate the weight of the difference between each uploaded data, where w i =ΔV i The corresponding weight, λ is the attenuation coefficient, and N is the number of previously preset recorded data; pass Obtain the weighted average change of the difference between the uploaded data; pass Get the upload threshold; Among them, k is the adjustment coefficient, α is the time impact factor, Vmin is the minimum upload threshold, T is the time since the last upload, Tmax is the maximum allowed non-upload time, ΔV threshold is the upload threshold.

4. The data transmission method of a smart remote water meter according to claim 1, characterized in that: The target integrated data obtained by integrating all valid uploaded data includes: After aligning all valid uploaded data by identity ID and time, all data are converted into JSON format and merged to obtain the target integrated data.

5. The data transmission method of a smart remote water meter according to claim 1, characterized in that: Encrypting the target integrated data by a preset encryption algorithm to obtain target encrypted data includes: After converting the target integrated data into ASCII values, converting the ASCII values ​​into 8-bit binary data to obtain the data to be encrypted, and converting the preset private key into a binary value to obtain the target private key; Performing an XOR operation on the data to be encrypted and the target private key to obtain initial encrypted data; Converting the initial encrypted data into ASCII values ​​to obtain first encrypted data, dividing the first encrypted data into 256-bit blocks to obtain a first sub-encrypted data set; The first sub-encrypted data set is converted into a DNA sequence through the ATCG supplementary rule to obtain the target encrypted data.

6. A data transmission device for an intelligent remote water meter, characterized in that: The device comprises: The water meter record data acquisition module is used to obtain the record data of all water meters in the target area. The record data of each water meter carries a water meter ID; each water meter has a unique identity ID; The valid uploaded data determination module is used to search the preset database to determine the historical saved data according to the ID of the target water meter, and determine the valid uploaded data according to the historical saved data and the target recorded data; the preset database is used to save all the recorded data uploaded to the cloud; the target water meter is any one of all the water meters; the target recorded data is the recorded data of the target water meter; A data encryption module is used to obtain all valid uploaded data, perform data integration on all valid uploaded data to obtain target integrated data, and perform data encryption on the target integrated data using a preset encryption algorithm to obtain target encrypted data; The data uploading module is used to upload the target encrypted data to the cloud.

7. The data transmission device of a smart remote water meter according to claim 6, characterized in that: The valid uploaded data determination module comprises: A comparison data determination module is used to obtain the record data most recently uploaded to the cloud from the historically saved data to obtain comparison data; A first difference calculation module, used for calculating the difference between the target recorded data and the comparison data to obtain a target comparison value; An upload threshold determination module, used to determine the upload threshold corresponding to the target water meter according to the historically saved data; The valid uploaded data judging module is used for recording the uploaded data as valid uploaded data if the target comparison value is greater than the upload threshold.

8. The data transmission device of a smart remote water meter according to claim 7, characterized in that: The upload threshold determination module includes: A target data set determination module is used to obtain a preset number of record data at the current moment in the historically saved data to obtain a target data set; The second difference calculation module is used to calculate the difference value by the formula ΔV i =V i+1 -V i Calculate the difference between two adjacent record data in the target data set; Where V i+1 is the recorded data uploaded to the cloud for the i+1th time, V i is the recorded data uploaded to the cloud for the i-th time, ΔV i is the difference between the data uploaded at the i+1th time and the data uploaded at the ith time; The difference weight determination module is used to determine the difference weight through w i =e -λ(N-i) Generate the weight of the difference between each uploaded data, where w i =ΔV i The corresponding weight, λ is the attenuation coefficient, and N is the number of previously preset recorded data; The weighted average change determination module is used to Obtain the weighted average change of the difference between the uploaded data; Upload threshold generation module for Get the upload threshold; Among them, k is the adjustment coefficient, α is the time impact factor, Vmin is the minimum upload threshold, T is the time since the last upload, Tmax is the maximum allowed non-upload time, ΔV threshold is the upload threshold.

9. The data transmission device of a smart remote water meter according to claim 6, characterized in that: The data encryption module comprises: The data integration module is used to align all valid uploaded data by identity ID and time, convert all data into JSON format and merge them to obtain the target integrated data.

10. The data transmission device of a smart remote water meter according to claim 6, characterized in that: The data encryption module also includes: A binary conversion module is used to convert the target integrated data into an ASCII value, convert the ASCII value into 8-bit binary data to obtain the data to be encrypted, and convert the preset private key into a binary value to obtain the target private key; An XOR operation module, used for performing an XOR operation on the data to be encrypted and the target private key to obtain initial encrypted data; An encrypted data splitting module, used for converting the initial encrypted data into ASCII values ​​to obtain first encrypted data, and dividing the first encrypted data into 256-bit blocks to obtain a first sub-encrypted data set; The target encrypted data generation module is used to convert the first sub-encrypted data set into a DNA sequence according to the ATCG supplementary rule to obtain the target encrypted data.

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