A water meter metering data encryption storage method

Abstract

The application relates to the technical field of data storage, in particular to a water metering data encryption storage method, which is applied to an intelligent water meter management system, the intelligent water meter management system is in communication connection with a preset encryption storage center, and the method comprises the following steps: in response to a received water metering data uploading request, a feature retrieval library is created from a biological feature template obtained from the encryption storage center; the biological feature template comprises one of fingerprint features, face features or voiceprint features; and the feature retrieval library comprises a biological container, an element extraction model and a certificate index table. Through the feature retrieval library and the element extraction model, the water metering data can be subjected to metadata analysis, similarity matching between biological features and data is carried out, accurate identity authentication is realized, if the matching degree is low, the user can be requested to supplement information in time, and the accuracy and safety of data storage are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data storage, in particular to a water meter measurement data encryption storage method. BACKGROUND

[0002] Water meter measurement data usually contains sensitive information such as user water consumption and consumption records. If these data are not encrypted or protected, they may face security risks such as malicious access, leakage, tampering, etc. For example, hackers may steal these data through network attacks, resulting in personal privacy leakage, or more seriously, tamper with the measurement data to gain improper benefits. As information security problems become increasingly serious, water companies and users have higher requirements for data security.

[0003] Currently, water meter measurement data storage methods often rely on simple encryption or single identity verification mechanism, which is vulnerable to illegal access and tampering, lacks identity verification bound to biological characteristics, and cannot provide multi-level protection. In addition, traditional methods usually do not use double indexing mechanism, and data storage and retrieval may encounter bottlenecks, especially when the data volume is large, the retrieval efficiency is low, and encryption index and certificate fingerprint technologies are not fully utilized to optimize retrieval performance. SUMMARY

[0004] To achieve the above purpose, the present application provides the following technical scheme: a water meter measurement data encryption storage method, the method is applied to an intelligent water meter management system, the intelligent water meter management system is in communication connection with a preset encryption storage center, and the method comprises:

[0005] In response to the received water meter measurement data upload request, a feature retrieval library is created from the biological feature template obtained from the encryption storage center; the biological feature template includes one of fingerprint features, facial features or voiceprint features, the feature retrieval library includes a biological container, an element extraction model and a certificate index table, the biological container caches biological feature templates and feature vectors, the element extraction model is used to analyze the metadata elements of the data to be stored, and the certificate index table stores public key certificates and certificate fingerprints associated with biological features; the biological container includes a user identity and a feature sub-library, the feature sub-library includes a feature dimension array, and the feature dimension array includes biological feature templates and corresponding certificate index tables;

[0006] The metadata of the water meter measurement data to be stored is analyzed by the element extraction model to obtain first type elements and second type elements; the first type elements are data integrity verification elements, and the second type elements are encryption association elements; similarity matching is performed between the second type elements and the biological feature templates in the feature retrieval library based on the second type elements to determine the element matching degree;

[0007] In response to the element matching degree being greater than a preset threshold, a corresponding public key certificate in the certificate index table is called, an exclusive encryption key is determined through a key derivation algorithm according to the hash value of the first type of element, and a hash operation is performed on the exclusive encryption key to obtain an encryption index value;

[0008] The exclusive encryption key is used to encrypt the to-be-stored water meter measurement data to determine encrypted measurement data, and the encrypted measurement data, the encryption index value and the corresponding biometric feature certificate fingerprint are encapsulated as a storage request and sent to an encrypted storage center;

[0009] A double-index storage result returned by the encrypted storage center is received, wherein the double-index storage result includes a first-level index constructed based on the encryption index value and a second-level index constructed based on the certificate fingerprint, and data storage confirmation information is determined based on the double-index storage result.

[0010] Preferably, in response to a received water meter measurement data upload request, a feature retrieval library is created based on biometric templates obtained from the encrypted storage center, including:

[0011] In response to the upload request, biometric templates, user identity certificates and corresponding public key certificates are obtained from the encrypted storage center in batches based on the thread pool capacity of the intelligent water meter management system;

[0012] An independent thread is allocated for each batch of data, feature extraction of the biometric templates is processed in parallel through the thread pool, a feature vector is determined and the certificate fingerprint of the public key certificate is associated, and a feature retrieval library is created based on memory resources.

[0013] Preferably, the obtained to-be-stored water meter measurement data is subjected to metadata analysis through the element extraction model to obtain the first type of element and the second type of element, including:

[0014] The to-be-stored water meter measurement data and a preset element guide language are input into the element extraction model, and the element guide language includes a definition example of the first type of element and a clarification rule of the second type of element;

[0015] The first type of element and the second type of element are obtained through model analysis.

[0016] Preferably, similarity matching is performed between the second type of element and the biometric templates in the feature retrieval library to determine an element matching degree, including:

[0017] A feature keyword corresponding to a user-associated identifier is extracted from the second type of element; the feature sub-library in the feature retrieval library is searched based on the feature keyword to obtain a candidate biometric template;

[0018] performing cosine similarity calculation on the feature vector of the candidate biometric feature template and the feature value of the second type of element to obtain an element matching degree; the element matching degree is used to determine the relevance of the biometric feature and the data to be stored.

[0019] Preferably, after obtaining the element matching degree, the method further comprises:

[0020] in response to the element matching degree being less than or equal to a preset threshold, determining an element supplement request based on the element extraction model, the element supplement request including a missing item of the second type of element and example guidance;

[0021] receiving user inputted element supplement information, correcting the second type of element based on the element supplement information, and re-executing the similarity matching step with the biometric feature template.

[0022] Preferably, the feature extraction of the biometric feature template is processed in parallel through a thread pool, the feature vector is determined and the certificate fingerprint of the public key certificate is associated, and a feature retrieval library is created based on memory resources, including:

[0023] each thread extracts feature points from the assigned biometric feature template to determine the feature vector; calculates the certificate fingerprint of the corresponding public key certificate, and establishes a mapping relationship between the feature vector and the certificate fingerprint;

[0024] checking whether there is a historical biometric container with the same user identity in the memory; in response to the absence of a historical biometric container, applying for a memory block to create a new biometric container, and storing the feature vector, the certificate fingerprint and the mapping relationship into the feature sub-library.

[0025] Preferably, after checking whether there is a historical biometric container with the same user identity in the memory, the method further comprises:

[0026] in response to the presence of a historical biometric container, checking the storage state of the feature sub-library in the historical biometric container; determining a spare storage unit, and storing the current feature vector and the certificate fingerprint into the spare storage unit;

[0027] in response to the presence of a historical record with the same certificate fingerprint, updating and replacing the historical record based on the current public key certificate, and synchronously updating the mapping relationship in the feature dimension array.

[0028] Preferably, after the exclusive encryption key is used to encrypt the to-be-stored water meter measurement data to determine the encrypted measurement data, the method further comprises:

[0029] calling the private key in the user identity certificate to asymmetrically encrypt the key of the encrypted measurement data to obtain the asymmetrically encrypted encrypted measurement data.

[0030] Preferably, after the encrypted metering data, the encrypted index value and the corresponding biometric certificate fingerprint are packaged as a storage request and sent to the encrypted storage center, the storage request further comprises:

[0031] The storage request is sent to the encrypted storage center through a secure communication channel, and the encrypted storage center constructs a double-index storage structure based on the encrypted index value and the biometric certificate fingerprint.

[0032] Preferably, the double-index storage result returned by the encrypted storage center is received, and data storage confirmation information is determined based on the double-index storage result, comprising:

[0033] The double-index storage result returned by the encrypted storage center is received, and the double-index storage result is subjected to hash verification to confirm the data storage integrity.

[0034] Based on the double-index path that passes the verification, a visual storage map is determined, and the visual storage map and a storage timestamp are packaged as data storage confirmation information and fed back to the data uploading terminal.

[0035] Compared with the prior art, the present application has the following advantages:

[0036] The present application provides multiple identity verification and encryption protection by combining biometric templates with encrypted storage of water metering data, greatly improving the security of the data, and the storage of the water metering data is encrypted and bound to the user's biometric features, preventing illegal access and tampering of the data; and a double-index storage mechanism is adopted, on the one hand, a primary index is constructed by an encrypted index value, and on the other hand, a secondary index is constructed by a certificate fingerprint, which can effectively improve the data retrieval efficiency, and the identity authentication of the biometric features makes the storage, management and verification of the data more efficient and accurate.

[0037] The present application can analyze the metadata of the water metering data through the feature retrieval library and the feature extraction model, and perform similarity matching based on the biometric features and the data to achieve accurate identity authentication, and if the matching degree is low, the user can be prompted to supplement the information in time to ensure the accuracy and security of the data storage; and the extraction of the biometric template and the matching of the feature vector are processed in parallel through the thread pool, which not only improves the concurrent processing capability of the system, but also effectively utilizes the memory resources, improves the overall data processing speed and efficiency.

[0038] The application ensures the integrity and reliability of water meter measurement data in the storage process through hash value and key derivation algorithm and other means, meanwhile, through the double-index storage structure, the data integrity check can be conveniently carried out, and it is ensured that the data is not lost or tampered in the transmission and storage process; and the storage result is presented in the form of visual storage atlas, combined with the storage time stamp, the user can easily check the state and integrity of data storage, thereby improving the trust and transparency of the user to the data security. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 It is a schematic diagram of the step flow of the overall method in an embodiment of the application. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0041] Embodiment one, please refer to Figure 1 The application provides a technical solution: a water meter measurement data encryption storage method, the method is applied to intelligent water meter management, the intelligent water meter management and a preset encryption storage center exist communication connection, and the method comprises the following steps:

[0042] S1, in response to the received water meter measurement data upload request, a feature retrieval library is created from the biometric template obtained from the encryption storage center; the biometric template comprises one of fingerprint features, facial features or voiceprint features, the feature retrieval library comprises a biological container, an element extraction model and a certificate index table, the biological container caches the biometric template and the feature vector, the element extraction model is used to analyze the metadata elements of the data to be stored, and the certificate index table stores the public key certificate and the certificate fingerprint associated with the biometric feature; the biological container comprises a user identity and a feature sub-library, the feature sub-library comprises a feature dimension array, and the feature dimension array comprises the biometric template and the corresponding certificate index table;

[0043] S2, the metadata of the water meter measurement data to be stored is analyzed by the element extraction model to obtain first type elements and second type elements; the first type elements are data integrity check elements, and the second type elements are encryption association elements; the similarity between the second type elements and the biometric template in the feature retrieval library is matched based on the second type elements to determine the element matching degree;

[0044] S3, in response to the element matching degree being greater than a preset threshold, calling a corresponding public key certificate in a certificate index table, determining a dedicated encryption key through a key derivation algorithm according to the hash value of the first type of element; performing hash operation on the dedicated encryption key to obtain an encryption index value;

[0045] S4, encrypting the to-be-stored water meter measurement data according to the dedicated encryption key to determine encrypted measurement data; encapsulating the encrypted measurement data, the encryption index value and the corresponding biometric feature certificate fingerprint as a storage request and sending the storage request to an encryption storage center;

[0046] S5, receiving a double-index storage result returned by the encryption storage center; wherein the double-index storage result includes a first-level index constructed based on the encryption index value and a second-level index constructed based on the certificate fingerprint, and determining data storage confirmation information based on the double-index storage result.

[0047] It should be noted that when the smart water meter receives a request to upload water metering data, it first obtains a "biometric template" from the encryption storage center, which can be a fingerprint, a face, or a voiceprint, etc. These biometric features are used to confirm the user's identity and ensure data access permissions. For these biometric templates, a feature retrieval library is created, which contains the following: a biometric container that caches biometric templates and their associated feature vectors; an element extraction model that parses and extracts metadata from the data to be stored (such as the source of the data, timestamp, etc.); a certificate index table that stores public key certificates and certificate fingerprints associated with biometric features; the biometric container contains the user's identity and a "feature sub-library"; the feature sub-library is a structure that contains an array of feature dimensions, each item in the array includes a biometric template and its corresponding certificate index table; the water metering data is parsed using the element extraction model to obtain two types of elements: the first type of element is used to verify data integrity (e.g. data hash value, to ensure that the data has not been tampered with during storage); the second type of element is related to the encryption process (e.g. feature information used to generate an encryption key); based on the second type of element, a similarity match is made with the biometric templates in the feature retrieval library to determine the matching degree; if the matching degree exceeds a pre-set threshold, the public key certificate associated with the user's biometric features will be found from the certificate index table, and a special encryption key will be generated using the hash value of the first type of element through a key derivation algorithm; the key is hashed to generate an encryption index value; the water metering data is encrypted using the special encryption key; the encrypted data will be referred to as "encrypted metering data"; the encrypted metering data, the encryption index value, and the biometric certificate fingerprint are packaged into a storage request and sent to the encryption storage center; the encryption storage center returns a "double-index storage result", which includes: a primary index generated based on the encryption index value; a secondary index generated based on the certificate fingerprint; these indexes are used to ensure that the data can be correctly located and accessed; based on the returned double-index storage result, the data storage confirmation information will be determined to verify that the data has been successfully stored and can be accessed later.

[0048] In an optional embodiment, in response to the received water metering data upload request, a feature retrieval library is created based on the biometric templates obtained from the encryption storage center, including:

[0049] In response to the upload request, the biometric templates, user identity certificates, and corresponding public key certificates are obtained from the encryption storage center in batches based on the thread pool capacity managed by the smart water meter;

[0050] An independent thread is assigned for each batch of data to process the feature extraction of the biometric templates in parallel through the thread pool, determine the feature vectors, and associate the certificate fingerprints of the public key certificates, and create a feature retrieval library based on memory resources.

[0051] It should be noted that when the intelligent water meter management receives an upload request, it needs to obtain the relevant biometric template, user identity certificate and public key certificate from the encrypted storage center; these data contain the user's identity authentication information and are used for encrypted storage data; the thread pool capacity refers to how many tasks (i.e. threads) the configured thread pool can handle at the same time; in order to improve efficiency, when obtaining data from the encrypted storage center, it will be processed in batches (i.e. the task is divided into multiple small parts); each batch contains a certain number of biometric templates, identity certificates and public key certificates; through this batch acquisition method, memory and computing resources can be effectively managed, avoiding resource overload when processing all data at once; an independent thread is allocated for each batch of data for processing; the advantage of this is that multiple batches of data can be processed in parallel, improving processing speed and reducing waiting time; each thread independently processes a batch of data; in each thread, feature extraction will be performed on the biometric template (such as fingerprint, face or voiceprint); the purpose of this step is to extract a feature vector that can represent the user's identity from the original biometric data; for example, extract the detailed information of the fingerprint from the fingerprint image, or extract the facial feature points from the face image; after extracting the feature vector, the features will be associated with the certificate fingerprint in the user's public key certificate; the certificate fingerprint is a unique identifier of the public key certificate, used to ensure the secure authentication of the user's identity; after processing each batch of data, a "feature retrieval library" will be created in memory; the feature retrieval library stores all extracted feature vectors and their association information with the certificate fingerprint; this library will be used for subsequent data access and identity verification.

[0052] In an optional embodiment, the metadata of the obtained to-be-stored water meter data is parsed by a feature extraction model to obtain first-type features and second-type features, including:

[0053] The to-be-stored water meter data and the preset feature guide language are input into the feature extraction model, and the feature guide language includes a definition example of the first-type features and a clarification rule of the second-type features;

[0054] The first-type features and the second-type features are obtained by model parsing.

[0055] It should be noted that the element guide language is a text information that helps the model to parse the data to be stored. It includes two parts: the definition of the first type of element: provides a specific definition or example of the first type of element, which helps the model to understand what content should be considered as the first type of element. The clarification rule of the second type of element: provides the rule of the second type of element, which is used to clarify or further define the specific content of the second type of element, to avoid confusion or misidentification. The element extraction model is a machine learning or artificial intelligence model used to parse input data and extract specific information. By inputting the water meter data to be stored and the element guide language, the model will automatically parse and identify the first type and the second type of elements in the data according to the definition and the rule. The model will analyze the water meter data to be stored according to the input element guide language, and extract the first type and the second type of elements. These extracted elements will be used for subsequent processing and storage.

[0056] In an optional embodiment, based on the similarity matching between the second type of element and the biometric feature template in the feature retrieval library, the element matching degree is determined, including:

[0057] Extracting the feature keywords corresponding to the user-associated identifier from the second type of element; retrieving the feature sub-library in the feature retrieval library based on the feature keywords to obtain the candidate biometric feature template;

[0058] Calculating the cosine similarity between the feature vector of the candidate biometric feature template and the feature value of the second type of element to obtain the element matching degree; the element matching degree is used to determine the relevance of the biometric feature and the data to be stored.

[0059] It should be noted that the second type of elements contains some information about the user (such as user ID, address, installation time, etc.); in this process, the feature keywords related to the user are extracted from these elements; these keywords help determine the characteristics of the user and provide a data basis for subsequent steps; through the extracted feature keywords, the corresponding feature sub-library is searched in the feature retrieval library; this retrieval library contains various templates related to biometric features, which may be related to the user's biometric features (such as fingerprints, iris, voice, etc.); according to the extracted keywords, candidate biometric feature templates matching these features are found from this library; the cosine similarity of the feature vectors of the candidate biometric feature templates and the feature values of the second type of elements is calculated to obtain the element matching degree; each candidate biometric feature template has a vector representing its features, and these vectors are obtained by analyzing the user's biometric features, such as the "shape" of the fingerprint, the "frequency" of the voice, etc.; The feature values of the second type of elements are feature values extracted from other information of the user, such as numerical features of user ID, address, etc.; the feature vectors of the biometric feature templates are compared with the feature values of the second type of elements by cosine similarity; cosine similarity is a method for measuring the similarity of two vectors, and its value is between -1 and 1, 1 means exactly the same, 0 means no similarity, and -1 means exactly opposite; the calculated similarity value is called element matching degree; the element matching degree is used to measure the relevance of biometric features and data to be stored (such as user information); if the matching degree is high, it indicates that there is a strong relevance between the biometric features and the user data, which may be the same user; if the matching degree is low, it indicates that the relevance is weak.

[0060] In an optional embodiment, after obtaining the element matching degree, the method further comprises:

[0061] In response to the element matching degree being less than or equal to a preset threshold, determining an element supplement request based on the element extraction model, the element supplement request including missing items of the second type of elements and example guidance;

[0062] Receiving user inputted element supplement information, correcting the second type of elements based on the element supplement information, and re-executing the similarity matching step with the biometric feature template.

[0063] It should be noted that when the matching degree of the elements obtained by calculation is less than or equal to the preset threshold value, it is considered that the current matching result is not accurate or strong enough, and more user information may be needed to improve the matching degree; the threshold value is a pre-set value for judging whether the matching is good enough; for example, if the matching degree is less than 0.8, it is considered that further supplementary information is needed; a feature extraction model is used to determine which second type of elements (such as user information, device information, etc.) are missing or insufficient; these missing elements may affect the matching of biological characteristics (such as fingerprints, irises, etc.) and data; the element supplement request includes two aspects: missing items: indicate which user information is not provided or insufficient, such as lacking the user's address, age or other important characteristics; example guidance: provide some examples or prompts to help users better understand how to supplement the information; such as prompting "Please provide your specific address" or "Please provide your date of birth"; the user provides the additional information required according to the supplement request; these information may be missing parts, such as the user's address, phone number, ID number, etc.; the supplementary information input by the user is collected and used to update or correct the original second type of elements; the information provided by the user is used to correct or supplement the originally missing or incomplete elements; this helps to ensure the completeness of the second type of elements and improves the accuracy of subsequent matching; the updated second type of elements are matched with the original biological characteristic template (such as fingerprint data, iris data, etc.); through updating the information, the matching degree may be improved, so that the user identity or data association can be more accurately judged.

[0064] In an optional embodiment, feature extraction of the biological characteristic template is processed in parallel through a thread pool, a feature vector is determined and a certificate fingerprint of a public key certificate is associated, a feature retrieval library is created based on memory resources, including:

[0065] Each thread extracts feature points from the assigned biological characteristic template, determines a feature vector, calculates a certificate fingerprint of a corresponding public key certificate, and establishes a mapping relationship between the feature vector and the certificate fingerprint.

[0066] It is determined whether a historical biological container with the same user identity exists in the memory; in response to the absence of the historical biological container, a memory block is applied to create a new biological container, and the feature vector, the certificate fingerprint and the mapping relationship are stored in the feature sub-library.

[0067] It should be noted that multi-threading is used to process biometric data in parallel; each thread is responsible for extracting feature points (such as ridge lines, textures, etc. of a fingerprint) in a user's biometric (such as fingerprint, iris, face recognition, etc.) template, and then determining a feature vector based on these feature points; each biometric data corresponds to a public key certificate, which can ensure the security of the data; the certificate fingerprint is the hash value of the certificate content, which is the unique identifier of the certificate; the fingerprint of each certificate is calculated and mapped to the feature vector; this mapping relationship can help associate a user's biometric with their identity information (through the public key certificate fingerprint) in subsequent processing; the historical biometric container is used to store user historical biometric features and related data; each user has a unique identifier, which is used to find whether there is a historical container of the user in memory; if the corresponding historical container is found, it means that the user has registered features before, and these stored feature data can be used for matching; if no historical container is found, it is considered that it is a new user, or the memory does not store the data of the user; at this time, a new memory block is applied to create a new biometric container; this new biometric container is used to store the biometric features, certificate fingerprint and other related data of the user; the newly created biometric container stores the feature vector (i.e. the extracted biometric data), the certificate fingerprint (the hash value of the identity certificate) and the mapping relationship (the correspondence between the feature vector and the certificate fingerprint) into the feature sub-library; the feature sub-library is a database used to store the biometric features and related information of all users.

[0068] In an optional embodiment, after finding whether there is a historical biometric container with the same user identity identifier in memory, the method further comprises:

[0069] In response to the existence of the historical biometric container, checking the storage state of the feature sub-library in the historical biometric container; determining a free storage unit, and storing the current feature vector and certificate fingerprint into the free storage unit;

[0070] In response to the existence of the historical record with the same certificate fingerprint, updating and replacing the historical record based on the current public key certificate, and synchronously updating the mapping relationship in the feature dimension array.

[0071] It should be noted that the historical biological container is a container created for each user to store the user's biometric data and related information; whenever a user attempts to verify identity, it will first check if there is a historical container for the user; if there is a historical biological container, it will continue to check the storage status of the feature sub-library (i.e. the database storing the user's biometric features) in the container; the storage status refers to whether there is still free storage space in the feature sub-library to store new data; if there is free space in the feature sub-library, an available free storage unit (i.e. free location) will be found, and then the current extracted feature vector (user's biometric data) and certificate fingerprint (hash value of the user's public key certificate) will be stored in the storage unit; the feature vector and the certificate fingerprint are key data for identifying the user's identity, and they are stored in the feature sub-library for subsequent verification; in response to the existence of historical records with the same certificate fingerprint, the historical records are updated and replaced based on the current public key certificate, and the mapping relationship in the feature dimension array is updated synchronously: if it is detected that the same certificate fingerprint already exists in the historical biological container, it indicates that the user has previously registered and stored related biometric data; at this time, the historical records need to be updated or replaced; the existing historical records will be updated and replaced using the current public key certificate, i.e. the user's public key certificate information is updated to the current certificate; at the same time, the mapping relationship in the feature dimension array related to the feature vector is also updated synchronously; the feature dimension array is a data structure that stores the mapping relationship between features and certificate fingerprints, and when the biometric features are updated, the mapping relationship also needs to be updated synchronously to ensure that the relationship between the feature data and the certificate fingerprint is always consistent.

[0072] In an optional embodiment, the water meter data to be stored is encrypted according to the exclusive encryption key, and after the encrypted meter data is determined, the method further comprises:

[0073] The private key in the user identity certificate is called to asymmetrically encrypt the key of the encrypted meter data, to obtain the asymmetrically encrypted encrypted meter data.

[0074] It should be noted that symmetric encryption is an encryption method using the same key for encryption and decryption; in this case, a dedicated encryption key (for example, key K) is used to encrypt the data to be stored; after symmetric encryption, the original data is converted into first-level encrypted data, which is encrypted data, and only users with the same key can decrypt it; the advantage of symmetric encryption is fast speed, suitable for encrypting large amounts of data; asymmetric encryption is an encryption method using different keys for encryption and decryption; in this step, the key (i.e. K) of the first-level encrypted data is encrypted using the private key in the user's identity certificate; in this way, the key of the first-level encrypted data itself becomes more secure, because only the private key of the user can decrypt the key; the encrypted metering data is the data obtained through this encryption process, which includes the encrypted key, and the key is used to decrypt the first-level encrypted data; in this way, even if the first-level encrypted data is stolen, the original data cannot be recovered without the decryption key.

[0075] In an optional embodiment, the encrypted metering data, the encrypted index value and the corresponding biometric certificate fingerprint are packaged as a storage request and sent to an encrypted storage center, including:

[0076] The storage request is sent to the encrypted storage center through a secure communication channel; wherein the encrypted storage center constructs a double-index storage structure based on the encrypted index value and the biometric certificate fingerprint.

[0077] It should be noted that the encrypted measurement data refers to certain sensitive data (such as financial data, personal information, etc.), which is encrypted by encryption algorithm to ensure the security during storage and transmission; the encrypted index value is the identification value used for indexing after encryption, which is used to quickly find and locate the stored data; the encrypted index value also remains in an encrypted state to prevent unauthorized access; the biometric certificate fingerprint refers to the certificate or fingerprint data related to the user's biometric features (such as fingerprint, facial recognition, etc.), which is also encrypted to ensure the security verification of the user's identity; these data will be packaged according to the preset format, that is, the above-mentioned encrypted data (measurement data, index value and biometric certificate) is combined into a complete storage request; the packaged request guarantees the integrity and formatting of the data, making it suitable for subsequent transmission and storage; the storage request is sent to the encrypted storage center through a secure communication channel (such as HTTPS, SSL / TLS, etc.); this secure channel ensures the confidentiality and integrity of the data during transmission, preventing data from being stolen or tampered with; once the encrypted storage center receives the packaged request data, it will use the encrypted index value and certificate fingerprint to build a double-index storage structure; the double-index storage structure is a special storage mechanism that combines two different indexes (encrypted index value and certificate fingerprint) to improve the retrieval efficiency and security of data; through this double indexing, storage can quickly locate and access specific data, and ensure that only legitimate users can access the corresponding data.

[0078] In an optional embodiment, the double-index storage result returned by the encrypted storage center is received, and data storage confirmation information is determined based on the double-index storage result, including:

[0079] The double-index storage result returned by the encrypted storage center is received, including a primary index path and a secondary index path; the double-index storage result is subjected to hash verification to confirm the integrity of the data storage;

[0080] Based on the double-index path that passes the verification, a visual storage map is determined, and the visual storage map and the storage timestamp are packaged as data storage confirmation information, which is fed back to the data upload terminal.

[0081] It is necessary to explain that the double-index storage result returned by the encrypted storage center: after receiving the storage request and processing the data, the storage center will return a storage result, which contains the index path of the data; here refers to the first-level index path and the second-level index path: the first-level index path refers to the main index used to locate the data, usually it points to the approximate location of the data in the storage; the second-level index path is a further refinement of the first-level index path, which can more accurately point to the specific location of the data storage; the two paths together constitute a double-index storage structure, which is used to improve the efficiency of data access and retrieval in storage; after receiving the double-index storage result, the storage result will be subjected to a hash check; the hash check is to generate the hash value (such as MD5, SHA, etc.) of the data to ensure that the data has not been tampered with or lost during transmission and storage; if the calculated hash value is consistent with the expected value, it means that the data has not been modified or damaged, so the storage of the data can be confirmed to be complete; once the hash check is passed, the double-index path that passes the check will be used to construct a visual storage map; this map is a graphical representation that shows the organizational structure of the data in the storage, helping users understand the storage location and relationship of the data; the double-index path is used to determine the nodes and paths in the map, so as to accurately present the hierarchical structure and location of the data storage; the visual storage map and the storage timestamp are packaged as data storage confirmation information and fed back to the data upload terminal: storage timestamp: the timestamp records the specific time of data storage, ensuring that the storage time of the data can be traced and confirmed; the data storage confirmation information is an information package containing the visual storage map and the storage timestamp; it confirms that the data has been successfully stored and provides detailed information about the data storage process; finally, these confirmation information will be returned to the data upload terminal, i.e. the client that initiates the storage request; the upload terminal can confirm the integrity, storage location and storage time of the data through these information, ensuring the accurate storage of the data.

[0082] The embodiments of the present application are described in detail above in combination with the drawings, but the present application is not limited thereto, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present application.

Claims

1. A water meter data encryption storage method, characterized by, The method is applied to a smart water meter management system in communication connection with a preset encrypted storage center, and the method comprises: In response to the received water metering data upload request, a feature retrieval library is created from the biometric template obtained from the encrypted storage center; the biometric template comprises one of a fingerprint feature, a facial feature or a voiceprint feature, the feature retrieval library comprises a biometric container, an element extraction model and a certificate index table, the biometric container caches the biometric template and the feature vector, the element extraction model is used to analyze the metadata elements of the data to be stored, and the certificate index table stores the public key certificate and the certificate fingerprint associated with the biometric feature; the biometric container comprises a user identity and a feature sub-library, and the feature sub-library comprises a feature dimension array, which comprises the biometric template and the corresponding certificate index table; The metadata of the water metering data to be stored is analyzed by the element extraction model to obtain first-type elements and second-type elements; the first-type elements are data integrity check elements, and the second-type elements are encryption association elements; similarity matching is performed between the second-type elements and the biometric template in the feature retrieval library based on the second-type elements to determine the element matching degree; In response to the element matching degree being greater than a preset threshold, the corresponding public key certificate in the certificate index table is called, and an exclusive encryption key is determined by a key derivation algorithm based on the hash value of the first-type element; the exclusive encryption key is subjected to hash operation to obtain an encryption index value; The water metering data to be stored is encrypted based on the exclusive encryption key to determine encrypted metering data; the encrypted metering data, the encryption index value and the corresponding biometric feature certificate fingerprint are packaged as a storage request and sent to the encrypted storage center; A double-index storage result returned by the encrypted storage center is received; wherein the double-index storage result comprises a first-level index constructed based on the encryption index value and a second-level index constructed based on the certificate fingerprint, and data storage confirmation information is determined based on the double-index storage result; The metadata of the water metering data to be stored is analyzed by the element extraction model to obtain first-type elements and second-type elements, comprising: The water metering data to be stored and a preset element guide language are input into the element extraction model, and the element guide language comprises a definition example of the first-type elements and a clarification rule of the second-type elements; The first-type elements and the second-type elements are obtained by model analysis; Similarity matching is performed between the second-type elements and the biometric template in the feature retrieval library based on the second-type elements to determine the element matching degree, comprising: The feature key words corresponding to the user association identifier are extracted from the second-type elements; the feature sub-library in the feature retrieval library is searched based on the feature key words to obtain a candidate biometric template; The feature vector of the candidate biometric template and the feature value of the second-type elements are subjected to cosine similarity calculation to obtain the element matching degree; the element matching degree is used to determine the association between the biometric feature and the data to be stored.

2. The water meter data encryption storage method of claim 1, wherein, In response to the received water meter data upload request, a feature retrieval library is created based on the biometric template obtained from the encryption storage center, comprising: In response to the upload request, the biometric template, user identity certificate and corresponding public key certificate are obtained from the encryption storage center in batches based on the thread pool capacity of the smart water meter management system; An independent thread is allocated for each batch of data, and the feature extraction of the biometric template is processed in parallel through the thread pool, the feature vector is determined and the certificate fingerprint of the public key certificate is associated, and the feature retrieval library is created based on the memory resources.

3. The water meter data encryption storage method of claim 2, wherein, After the element matching degree is obtained, the method further comprises: In response to the element matching degree being less than or equal to a preset threshold, an element supplement request is determined based on the element extraction model, the element supplement request including missing items of the second type of elements and example guidance; Receiving user input element supplement information, modifying the second type of elements based on the element supplement information, and re-executing the similarity matching step with the biometric template.

4. The water meter data encryption storage method of claim 3, wherein, Parallel processing of feature extraction of biometric templates through a thread pool, determination of feature vectors and association of certificate fingerprints of public key certificates, and creation of a feature retrieval library based on memory resources, comprising: Each thread extracts feature points from the allocated biometric template to determine the feature vector; calculate the certificate fingerprint of the corresponding public key certificate, and establish the mapping relationship between the feature vector and the certificate fingerprint; Looking up whether there is a historical biometric container with the same user identity in the memory; in response to the absence of a historical biometric container, applying for a memory block to create a new biometric container, and storing the feature vector, certificate fingerprint and mapping relationship into the feature sub-library.

5. The method for encrypting and storing water metering data according to claim 4, wherein, After looking up whether there is a historical biometric container with the same user identity in the memory, the method further comprises: In response to the existence of a historical biometric container, checking the storage state of the feature sub-library in the historical biometric container; determining the empty storage unit, and storing the current feature vector and certificate fingerprint in the empty storage unit; In response to the existence of a historical record with the same certificate fingerprint, updating and replacing the historical record based on the current public key certificate, and synchronously updating the mapping relationship in the feature dimension array.

6. The water meter data encryption storage method of claim 5, wherein, After encrypting the to-be-stored water meter data according to the exclusive encryption key and determining the encrypted metering data, the method further comprises: Calling the private key in the user identity certificate to asymmetrically encrypt the key of the encrypted metering data to obtain the asymmetrically encrypted encrypted metering data.

7. The water meter data encryption storage method of claim 6, wherein, After encapsulating the encrypted metering data, the encrypted index value and the corresponding biometric certificate fingerprint into a storage request and sending it to the encryption storage center, the method further comprises: Sending the storage request to the encryption storage center through a secure communication channel; wherein the encryption storage center constructs a double-index storage structure based on the encrypted index value and the biometric certificate fingerprint.

8. The water meter data encryption storage method of claim 7, wherein, Receiving the double-index storage result returned by the encryption storage center, determining the data storage confirmation information based on the double-index storage result, comprising: Receiving the double-index storage result returned by the encryption storage center, including a primary index path and a secondary index path; performing a hash check on the double-index storage result to confirm the data storage integrity; Based on the double index path determined by the check, a visual storage map is determined, the visual storage map and a storage timestamp are packaged as data storage confirmation information, and the data storage confirmation information is fed back to the data uploading terminal.

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