Data security communication method based on intelligent electric meter

Abstract

The invention discloses a data security communication method based on an intelligent electric meter, and relates to the technical field of communication control, and the method comprises the steps: obtaining the basic data information of the intelligent electric meter, carrying out the preliminary verification and inspection, and generating a to-be-processed queue; grading each intelligent electric meter according to the basic data information, setting a peer mechanism to check the intelligent electric meters in the to-be-processed queue, and generating a middle-term queue; setting difference verification according to the grade degrees of different intelligent electric meters in the middle-stage queue, and adding the intelligent electric meters passing the verification into a tail queue; acquiring data information corresponding to the intelligent electric meters in the last queue, analyzing the data information, and storing the analyzed electric meter data; through a multi-stage verification and verification mechanism, the authenticity and legality of the data of the intelligent electric meter are ensured, the data processing efficiency and flexibility are improved, and the effects of optimizing the data processing flow and improving the data processing efficiency and flexibility are achieved.

Description

Technical Field

[0001] The present invention relates to the field of communication control technology, and in particular to a data security communication method based on a smart meter. Background Art

[0002] At present, the data storage security of smart meters has always been an urgent problem to be solved. When smart meters monitor the user's electricity consumption data, they need to be transmitted to the meter data receiving platform for data analysis and storage. However, the data security protection of the meter data receiving platform is more based on judging the legitimacy of the data source. When judging the legitimacy of the data source, the traditional approach is to parse the data first, and then extract the data source information. If the data source information is consistent with the pre-stored information, the data source is considered to be legal. However, if the data is parsed first, if the data received by the meter data receiving platform itself is set with a monitoring program, once the data is parsed first, it may be forced to parse the monitoring program or illegally invade the data, resulting in the security of the meter data receiving platform cannot be guaranteed, and it may also cause the leakage of the data stored in the meter data receiving platform.

[0003] The Chinese invention patent with application number 202410979260.1 provides a communication control method based on smart meter data transmission. The corresponding first private key and second private key are obtained based on location information and bandwidth threshold information, and then the third private key is generated. When the meter data receiving platform receives the meter data transmitted by the smart meter, the first public key, the second public key and the third public key are called in sequence, and the first private key, the second private key and the third private key are used to determine whether to parse the received meter data, thereby ensuring the security when parsing the smart meter data.

[0004] However, in the existing patented technology, a triple comparison verification is set up to improve security, but it also increases the complexity of data processing, increases data processing time, reduces data processing efficiency, and uses a fixed verification level to deal with all meter data, resulting in reduced flexibility and waste of resources. Summary of the invention

[0005] The present application solves the problem of reduced data processing efficiency and flexibility in the prior art by providing a data security communication method based on a smart meter, thereby achieving the technical effect of improving data processing efficiency and flexibility.

[0006] The present application provides a data security communication method based on a smart meter, the method comprising: S100: Obtain basic data information, location information and bandwidth threshold information of the smart meter, perform preliminary verification and check, and generate a queue to be processed; classify each smart meter according to the basic data information, set a peer mechanism to verify the smart meters in the queue to be processed, and generate a mid-term queue; S200: setting difference verification according to the levels of different smart meters in the mid-term queue, and adding the smart meters that pass the verification to the final queue; S300: Obtain and parse the data information corresponding to the smart meter in the last queue, and store the parsed meter data; monitor steps S100 and S200 in real time, issue timely warnings for abnormal conditions that occur therein, and generate abnormal report information.

[0007] Furthermore, the basic data information includes the unique code of the smart meter, historical data records, and data importance; the levels include low level, medium level and high level, and the levels are divided according to dynamic verification based on the basic data information and real-time data information of data transmission.

[0008] Furthermore, the peer mechanism sends peer query requests to multiple storage lists, performs key verification, and determines whether there is permission to perform parsing and reception. If there is permission, it is added to the mid-term queue.

[0009] Furthermore, the verification and inspection generates a first private key and a second private key respectively based on the location information and bandwidth threshold information of the smart meter in the queue to be processed, and generates the corresponding first public key and second public key, generates a third private key of the smart meter based on the first private key and the second private key, and generates a third public key based on the third private key, obtains the real-time bandwidth information when the smart meter transmits data to the meter data receiving platform, and generates a fourth private key based on the real-time bandwidth information, and generates a fourth public key based on the fourth private key; calls the first public key, the second public key, the third public key and the fourth public key in sequence, and determines whether there is permission for parsing and receiving according to the first private key, the second private key, the third private key and the fourth private key in sequence.

[0010] Furthermore, the difference verification is to set different key verification methods and verification processes according to different levels, and set different encryption verification methods corresponding to different levels of smart meters.

[0011] Furthermore, the queue to be processed, the mid-term queue and the final queue respectively refer to a collection of smart meters that have passed verification at different times and their corresponding data information.

[0012] Furthermore, the peer mechanism also includes a cache management strategy, which is to obtain the frequency by real-time monitoring the query frequency and usage pattern of the public key, pre-set the frequency threshold, cache the public keys with a frequency greater than the frequency threshold, and set the cache area based on the level of the smart meter. At the same time, the size of the cache area is adjusted according to the real-time monitoring data, and the cache time is adjusted in real time; the cache management strategy also includes a pre-fetch mechanism, which is based on the basic prediction model to judge the usage trend of the public key, and pre-load the public keys required in the future time period into the cache.

[0013] Furthermore, the method also includes S400: setting a dynamic line structure mechanism, dynamically adjusting the priority of the verification task based on the real-time data flow and load conditions, sorting the verification tasks according to the frequency of public key queries, and dynamically adjusting the verification process in combination with the level of the smart meter.

[0014] Furthermore, the method also includes: S500: real-time monitoring of step S100 and step S200, and generating a stage trust value for the smart meter based on the monitoring data, obtaining a total trust value based on the stage trust value, and establishing a trust chain based on the total trust value, generating a trust code based on the trust chain, and directly adding the trust code to the last queue after verification.

[0015] Furthermore, the stage trust value is generated based on the data verification process of the smart meter in the initial queue, mid-term queue, and final queue. A trust threshold is pre-set in each stage. If the stage trust value of any stage is lower than the trust threshold, the subsequent trust judgment is cancelled.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: Through a multi-level verification and audit mechanism, the authenticity and legality of smart meter data are ensured, and the smart meter data are processed by level division and peer mechanism, which improves the efficiency and flexibility of data processing, and achieves the effect of optimizing the data processing process and improving data processing efficiency and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a schematic diagram of the overall process of a data security communication method based on a smart meter in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] To facilitate the understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0020] Embodiment 1: Figure 1 As shown, a data security communication method based on a smart meter, the method comprising: S100: Obtain basic data information, location information and bandwidth threshold information of the smart meter, perform preliminary verification and check, and generate a queue to be processed; classify each smart meter according to the basic data information, set up a peer mechanism to verify and examine the smart meters in the queue to be processed, and generate a mid-term queue.

[0021] The basic data information includes the unique code of the smart meter, historical data records, and data importance; the levels include low level, medium level and high level, and the levels are divided according to dynamic verification based on the basic data information and real-time data information of data transmission.

[0022] In some embodiments, the smart meter transmits the meter data, including basic data information, location information and bandwidth threshold information, to the meter data receiving platform after encryption. After receiving the data, the meter data receiving platform performs preliminary format verification and integrity check, generates a queue to be processed, and sets a peer mechanism to verify the smart meters in the queue to be processed. The peer mechanism sends a peer query request to multiple storage lists, performs key verification, and determines whether there is permission for parsing and receiving. If there is permission, it is added to the mid-term queue. The query request is obtained by obtaining the location information and bandwidth threshold information of the smart meter in the queue to be processed, and generating a first private key based on the location information, and generating a first public key based on the first private key; generating a second private key based on the bandwidth threshold information, and generating a second public key based on the second private key; generating a third private key of the smart meter based on the first private key and the second private key, and generating a third public key based on the third private key, obtaining the real-time bandwidth information when the smart meter transmits data to the meter data receiving platform, and generating a fourth private key based on the real-time bandwidth information, and generating a fourth public key based on the fourth private key; when the meter data receiving platform receives the meter data transmitted by the smart meter, it calls the first public key, the second public key, the third public key and the fourth public key in sequence, and judges whether there is permission for parsing and receiving according to the first private key, the second private key, the third private key and the fourth private key in sequence, and if there is permission, it joins the mid-term queue.

[0023] In some embodiments, the peer mechanism utilizes a parallel processing verification method. The meter data receiving platform simultaneously sends query requests to multiple storage lists, queries them separately, and each storage list processes the query requests in parallel and returns the query results at the same time. The meter data receiving platform quickly determines the existence of the public key based on the query results. If all public keys exist, the corresponding confirmation instructions are prepared. If any public key does not exist, the instructions are not parsed and exception information is fed back. Specifically, a thread pool is used to manage parallel verification tasks, and the number of threads is dynamically adjusted according to the system load to avoid resource overload. For frequently queried public keys, in some embodiments, a temporary cache is established to reduce the number of repeated queries. At the same time, a timeout period for the query request is set, and the timed request is automatically retried or marked as an exception.

[0024] In some embodiments, the data transmission real-time data information refers to the real-time data generated during the data transmission process, and is combined with the basic data information to jointly adjust the verification division level. The basic data information includes the unique code of the smart meter, which is used to identify the smart meter and distinguish the characteristics of the data information; based on historical data records including historical security records and historical abnormality records, specifically including the number of abnormal records, causes, solutions and the history of attacks, etc.; the data importance refers to user type, data sensitivity, etc.; real-time data is the current resource status of the system, such as the current system usage rate, memory occupancy rate, etc.; the level includes low level, medium level and high level. When different smart meters and transmitted data information are divided into levels, on the one hand, it depends on the historical record information for judgment, and on the other hand, it is based on the comprehensive judgment of the importance of the current data. The importance of data is different in different actual scenarios, and the criteria for judging the importance of data are different. A comprehensive judgment can be made based on the actual situation and the data; the low level refers to a good historical record, for example, there are less than three historical abnormal records, the data importance is low, and when the system CPU usage exceeds 80% or the memory occupancy exceeds 70%, for low-level data information, the corresponding verification process is simplified and the verification steps are reduced. The low importance of data mentioned here is a comprehensive judgment based on the actual situation and the historical transmission data of the smart meter; the intermediate level is applicable to the intermediate standard, and the standard verification process is executed to ensure data security. For example, the corresponding historical abnormal records are between 3-10 times, the data importance is medium or high, and the system resource status is moderate; the high level is applicable to situations where the historical record is poor, the data is critical or the system resources are sufficient, and additional verification steps are added. For example, the number of historical abnormal records exceeds 10 times, the data importance is high, and the system resource status is good; according to the dynamically adjusted verification level, the corresponding confirmation instruction or non-analysis instruction is sent to the smart meter.

[0025] In some embodiments, since the location information of the smart meter is determined once it is installed, the location information can be manually updated during the normal operation of the smart meter. When the location information of the smart meter is adjusted, it is necessary to actively report the updated location information, regenerate the first private key, and use the regenerated first private key to regenerate the corresponding first public key; if there is an uncontrollable position change, the location information of the smart meter is regularly updated according to a pre-set period; that is, the location information of the smart meter is obtained at a first specified time interval, and if a position adjustment instruction is received, after the position of the smart meter is adjusted, the adjusted location information is actively obtained through the smart meter, and the location information of the smart meter is obtained at a second specified time interval; the first specified time is less than the second specified time; and the first specified time should not be too long, and is set according to actual needs, such as 12 hours, 8 hours or 6 hours, etc.; the second specified time can be set longer than the first specified time, such as 24 hours, 48 ​​hours or 72 hours, etc.

[0026] In some embodiments, after the smart meter is installed and connected to the user's electricity consumption data to be monitored, if the bandwidth threshold information when the smart meter transmits data to the meter data receiving platform is fixed, then when there is an illegal intrusion command to control the smart meter to send a higher bandwidth meter data to the meter data receiving platform, the smart meter can directly detect it. Therefore, in this embodiment, the bandwidth threshold information when the smart meter transmits data to the meter data receiving platform cannot be adjusted after the smart meter is installed. If you want to adjust the number and range of users monitored by the smart meter, a separate authorization instruction is required. This separate authorization instruction needs to be adjusted offline at the smart meter site, and remote operation is generally not allowed unless a separate instruction security plan is set to allow remote operation.

[0027] Specifically, for all private keys, they are stored in the smart meter body after being generated, while the public keys are stored in the meter data receiving platform, so as to realize normal communication between the smart meter and the meter data receiving platform. In this embodiment, since it is necessary to send and receive instructions between the smart meter and the meter data receiving platform, the first private key, the second private key and the third private key are all stored in the first storage list of the smart meter, the first public key is stored in the second storage list of the meter data receiving platform, the second public key is stored in the third storage list of the meter data receiving platform, and the third public key is stored in the fourth storage list of the meter data receiving platform.

[0028] S200: Setting difference verification according to the levels of different smart meters in the mid-term queue, and adding the smart meters that pass the verification to the final queue.

[0029] The difference verification is to set different key verification methods and verification processes according to different levels, and set different encryption verification methods for smart meters of different levels. Specifically, for low-level smart meters, a simplified verification process is adopted to reduce the verification steps and complexity and improve the verification speed; for mid-level smart meters, a standard verification process is implemented to ensure data security while maintaining verification efficiency; for high-level smart meters, additional verification steps and stricter verification methods are added, such as multiple encryption, two-way verification, etc., to ensure the high security of data. According to the level of the smart meter, the corresponding verification method and process are selected for verification, and the smart meter that has passed the verification is added to the final queue to wait for subsequent data parsing and storage. In this application, the queue to be processed, the mid-term queue and the final queue refer to the collection of smart meters that have passed the verification at different times and their corresponding data information. The final queue includes basic data information and verification information of smart meters, including verification-related information such as the public key, verification time, and verification method that have passed the verification, and also includes the keys and parsing rules corresponding to the parsing of data information of different levels.

[0030] S300: Obtain and parse the data information corresponding to the smart meter in the last queue, and store the parsed meter data; monitor steps S100 and S200 in real time, issue timely warnings for abnormal conditions that occur therein, and generate abnormal report information.

[0031] The abnormal report information includes the node where the abnormality occurs, the type of abnormality, the time of occurrence, the related smart meter indication and other information, and is sent to the alarm terminal or system management personnel for subsequent processing.

[0032] In this embodiment, each smart meter is graded according to basic data information, and a peer mechanism is set up to check and inspect the smart meters in the processing queue. Through grade classification, smart meters of different importance are processed differently; the peer mechanism increases the accuracy and reliability of data processing.

[0033] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: This application ensures the authenticity and legality of smart meter data through a multi-level verification and audit mechanism, divides the smart meter data into levels and processes them through a peer mechanism, thereby improving the efficiency and flexibility of data processing, and achieving the effect of optimizing the data processing process and improving data processing efficiency and flexibility.

[0034] Embodiment 2: In embodiment 1, level division and peer mechanism are set to further improve data processing efficiency and flexibility. This embodiment makes further improvements on the basis of the above embodiment.

[0035] When conducting legal verification, in order to further improve data processing efficiency, the public key to be verified needs to be cached by setting a cache area. During the verification process, for the frequently queried public key information, the meter data receiving platform caches it in a local or distributed cache system. In subsequent verification, first check whether the required public key information exists in the cache. If it exists, it is directly obtained from the cache to avoid database access. If it does not exist, query the database and update the cache. The database mentioned here is the total storage location of all storage lists in the above embodiment. At the same time, it is necessary to regularly clean up expired or invalid public key information in the cache to maintain the effectiveness and efficiency of the cache. This implementation makes further improvements on the basis of the above content, thereby further improving data processing efficiency.

[0036] The peer mechanism also includes a cache management strategy, which is to obtain the frequency by real-time monitoring of the query frequency and usage pattern of the public key, pre-set the frequency threshold, cache the public keys with a frequency greater than the frequency threshold, and set the cache area based on the level of the smart meter. At the same time, the size of the cache area is adjusted according to the real-time monitoring data, and the cache time is adjusted in real time.

[0037] The cache management strategy also includes a pre-fetch mechanism, which determines the usage trend of public keys based on a basic prediction model and pre-loads public keys required in a future time period into the cache.

[0038] In some embodiments, the cache management strategy refers to a strategy for temporarily storing and managing frequently queried public keys or verification results in order to improve verification efficiency and reduce the number of repeated queries during the peer verification of smart meter data. The frequency is obtained by real-time monitoring of the query frequency and usage pattern of the public key. The frequency does not only refer to the query frequency, but is a comprehensive value of the query frequency and usage pattern. The method for obtaining the comprehensive value can use a weighted summation method, first normalizing the two data to make them in the same dimension, and then performing weighted summation to obtain the frequency.

[0039] In some embodiments, the usage mode refers to different encryption verification methods. In the above embodiments, different degrees of encryption methods are used for data of smart meters of different levels. For example, low-level ones use ordinary encryption, and high-level ones use double encryption to further improve encryption security. Different encryption verification methods have different usage modes of public keys. The frequency is obtained according to different usage modes and specific query frequencies. The frequency threshold is pre-set based on historical data analysis and system performance requirements. When the frequency of a public key exceeds the frequency threshold, the public key is considered to be a high-frequency public key and is stored in the cache area for cache management.

[0040] In some embodiments, the cache management strategy sets cache areas based on the level of smart meters, and also needs to adjust the size of the cache area according to the real-time monitoring data, and adjust the cache time in real time. For example, for high-frequency public keys, set a high-frequency cache area to store these public keys and their corresponding data or verification results; set a dedicated cache area for high-level smart meters to store their commonly used data and verification results, with a longer cache time; set a medium-sized cache area for medium-level smart meters, with a moderate cache time; set a smaller cache area for low-level smart meters, with a shorter cache time, or only cache the most recent query result; the specific cache time is set or adjusted according to the actual frequency of public key changes. Monitor the query frequency and usage pattern of the public key. When the frequency of a public key drops below the frequency threshold, move it from the high-frequency cache area to the cache area of ​​the corresponding level, and dynamically adjust the size and cache time of each cache area according to the system load and cache hit rate.

[0041] In this embodiment, when a data request from a smart meter is received, the smart cache is first queried. If there is no corresponding data in the cache, parallel verification is performed according to the peer mechanism, and the verification result is stored in the cache area of ​​the corresponding level. For high-frequency public keys, the high-frequency cache area is queried first to improve query efficiency. Key indicators such as cache hit rate, database access times, and system response time are monitored in real time. According to the monitoring results, parameters such as frequency threshold, cache area size, and cache time are regularly adjusted to optimize system performance.

[0042] In some embodiments, in order to further implement an efficient cache replacement strategy, a combination of LRU strategy and LFU strategy is selected. LRU (Least Recently Used) is the least recently used strategy. If a piece of data has been accessed recently, then the probability of being accessed in the future is also high; conversely, the least recently accessed data has a relatively low probability of being accessed in the future. Therefore, when the cache space is insufficient, the LRU algorithm will select the least recently accessed data for elimination, thereby making room for new data; and LFU (Least Frequently Used) is the least frequently used strategy. Unlike LRU, the LFU algorithm considers the frequency of data access, rather than the time when it was most recently accessed. If a piece of data is rarely accessed, then the probability of being accessed in the future is also low; conversely, the probability of frequently accessed data being accessed in the future is also relatively high. Therefore, when the cache space is insufficient, the LFU algorithm will select the data with the lowest access frequency for elimination.

[0043] In some embodiments, the cache management strategy also includes a pre-fetch mechanism, which determines the usage trend of the public key based on the basic prediction model and pre-loads the public keys required in the future time period into the cache; the basic prediction model is established based on the time series prediction algorithm, specifically: collecting historical query data of the public key, including query time, public key ID, etc., cleaning and pre-processing the data, such as removing outliers, filling missing values, etc., sorting the data according to the time series, and dividing it into a training set and a test set; extracting features of the time series, such as timestamps, query frequency, periodicity, etc., and using sliding window technology to construct features, that is, using query data within a period of time as input features and query data in the next period of time as output labels; building an LSTM neural network model, including an input layer , LSTM layer, fully connected layer and output layer. The input layer receives time series features, the LSTM layer is used to capture the long-term dependencies of the time series, the fully connected layer is used to map the output of the LSTM layer to the predicted value, and the output layer outputs the predicted result. The LSTM model is trained using the training set data, the mean square error is selected as the loss function, and the Adam optimization algorithm is used to optimize the model parameters. Through iterative training, the prediction error of the model on the training set is gradually reduced. The trained model is evaluated using the test set data, and indicators such as prediction accuracy and mean square error are calculated to evaluate the performance of the model. The trained LSTM model is deployed to the intelligent cache management for real-time prediction of public key query needs. According to the prediction results, the relevant public keys are queried and cached from the database in advance to reduce future query delays.

[0044] In some embodiments, the future time period is pre-set based on historical data and experimental verification and adjusted according to real-time dynamic changes, indicating a time node at which the public key used within a certain period of time will change. The historical query data of the public key is analyzed according to the basic prediction model to predict possible query needs within a certain period of time in the future. It is easy to understand that the meaning represented by a period of time and the future time period is consistent, and the specific time range needs to be set according to actual conditions. Experiments have shown that the length of the time range should match the time node at which the public key undergoes a more obvious change.

[0045] In this embodiment, by caching frequently queried public key information, the number of database accesses is reduced and the query efficiency is significantly improved; setting a pre-fetch mechanism further reduces query delays and improves the response speed of the system; dynamically adjusting the size of the cache area and the cache time, and performing personalized management according to the frequency of the public key and the level of the smart meter, improves the efficiency of cache space utilization; and using LRU and LFU strategies in combination to achieve intelligent cache replacement and avoid waste of cache space.

[0046] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: This application further improves the efficiency of data access processing by setting up a cache area and a cache management strategy; by setting up a pre-fetch mechanism and dynamically adjusting the size of the cache area and the cache time, it achieves the effect of rapid verification and improving the efficiency of cache space utilization.

[0047] Embodiment 3: The above embodiment further improves the data access processing efficiency by setting a cache area and a cache management strategy. This embodiment makes further improvements on the basis of the above content.

[0048] The method also includes S400: setting a dynamic line structure mechanism, dynamically adjusting the priority of the verification task based on the real-time data flow and load conditions, sorting the verification tasks according to the frequency of public key queries, and dynamically adjusting the verification process in combination with the level of the smart meter.

[0049] In some embodiments, based on real-time data traffic and system load, the priority of the verification task is dynamically adjusted. The priority of the verification task is determined based on multiple factors such as real-time data traffic, system load, frequency of public key queries, and the level of smart meters, and is used to determine which verification task should be processed first. When the data traffic is large or the system load is high, key tasks such as data verification of high-level smart meters are prioritized to ensure data security and timeliness; verification tasks are sorted according to the frequency of public key queries. Verification tasks corresponding to high-frequency public keys are prioritized to reduce the number of database accesses and improve query efficiency. Combined with the level of smart meters, the verification process is dynamically adjusted, specifically including: first determining the level of the smart meter to be verified, setting a basic verification process for all meters according to the level, including necessary verification steps and verification methods, simplifying the verification process for low-level smart meters; adding additional verification steps and stricter verification methods for high-level smart meters, and dynamically adjusting the complexity and strictness of the verification process according to system load and real-time needs.

[0050] In some embodiments, when a data request from a smart meter is received, the priority and order of the verification tasks are first determined based on a dynamic line structure mechanism. For tasks with higher priority, such as verification corresponding to a high-frequency public key or data verification of a high-level smart meter, system resources are preferentially allocated for processing. For tasks with lower priority, such as data verification of a low-level smart meter or query of a low-frequency public key, processing can be postponed or merged to reduce the occupancy of system resources.

[0051] In some embodiments, the execution status of verification tasks and system performance indicators, such as verification speed, cache hit rate, number of database accesses, etc., are monitored in real time. According to the monitoring results, the parameters of the dynamic line structure mechanism, such as priority threshold, task sorting rules, etc., are dynamically adjusted to optimize system performance. The priority threshold is a boundary value used to divide tasks of different priorities. According to the system load, data traffic and the importance of the verification task, one or more priority thresholds are set to distinguish between high-priority and low-priority tasks; the task sorting rules are rules for determining the order of processing verification tasks. According to factors such as the frequency of public key queries, the level of smart meters, and the importance of data, the task sorting rules are set, such as sorting from high to low priority, sorting from high to low public key query frequency, etc.

[0052] In some embodiments, the dynamic configuration mechanism is combined with the cache management strategy to jointly improve data processing efficiency. The cache management strategy provides data support for the dynamic configuration mechanism on the frequency of public key queries, and obtains data on the frequency of public key queries from the cache management strategy. These data reflect the frequency of queries of different public keys. According to the public key query frequency data, a higher priority is set for the corresponding verification task to ensure that the verification tasks corresponding to high-frequency public keys are processed first; and the dynamic configuration mechanism prioritizes the verification tasks corresponding to high-frequency public keys based on these data; at the same time, the dynamic configuration mechanism can also predict and process the verification tasks corresponding to the public keys that may be needed in the future in advance according to the pre-fetch mechanism in the cache management strategy, thereby further reducing query delays. The cache management strategy and the dynamic configuration mechanism share data such as the frequency of public key queries and the processing status of verification tasks, and work together to dynamically adjust the parameters of the dynamic configuration mechanism according to the execution of the cache management strategy and the system performance monitoring results to optimize the overall system performance.

[0053] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: This application effectively improves the efficiency and speed of data processing by dynamically adjusting the priority and order of verification tasks and simplifying or increasing verification steps, giving priority to critical and high-frequency tasks, delaying or merging low-priority tasks, reducing the occupation and waste of system resources, achieving the effect of dynamically adjusting the order of tasks, and further improving data processing efficiency.

[0054] Embodiment 4: This embodiment is a further improvement on the basis of the above embodiment.

[0055] The method also includes: S500: real-time monitoring of step S100 and step S200, and generating a stage trust value for the smart meter according to the monitoring data, obtaining a total trust value according to the stage trust value, and establishing a trust chain according to the total trust value, generating a trust code based on the trust chain, and directly adding the trust code to the last queue after verification.

[0056] In some embodiments, the stage trust value is generated based on the data verification process in the initial queue, mid-term queue, and final queue. A trust threshold is pre-set in each stage. If the stage trust value of any stage is lower than the trust threshold, the subsequent trust judgment is canceled. The steps for generating the stage trust value are as follows: obtaining the smart meter code, querying the historical data records of the meter, analyzing indicators such as data accuracy and abnormal frequency, assigning an initial trust value (such as a value between 0 and 1) to the meter based on historical performance, storing the smart meter code and the initial trust value in a trust value database in correspondence, monitoring the status of data in the transmission and processing process in real time, such as transmission delay, data integrity, etc., adjusting the initial trust value based on the data monitoring results, if the data performance is stable, the trust value can be appropriately increased; if an abnormality occurs, the trust value is lowered, a fixed floating value is set, such as 0.05, and the fixed floating value is used as the minimum unit for adjusting the trust value, judging the stage trust value and the trust threshold of the corresponding stage, if it is greater than the trust threshold, updating the adjusted trust value to the trust value database, performing final verification on the smart meter in the last queue, and determining the stage trust value based on the verification result and the trust threshold of the last queue; and obtaining the total trust value through comprehensive calculation based on the three stage trust values.

[0057] In some embodiments, the total trust values ​​generated by all smart meters are sorted in descending order, and the smart meters corresponding to the total trust values ​​above the median are selected to generate a trust chain. The selection can be set according to the actual situation, or a threshold or a fixed selection ratio can be set for selection. The three-stage trust values ​​corresponding to the finally selected smart meter are connected in series in order to form a trust chain, and the trust chain is stored in a trust chain database, associated with the smart meter code, and the trust chain is hashed or otherwise encrypted to generate a trust code, and the trust code is associated with the smart meter code and stored in the trust code database. The trust chain and trust code generated by each smart meter are different. The trust chain forms a trust transmission mechanism by connecting the trust values ​​of different stages in series, ensuring that the trust of the data can be continuously transmitted during the entire processing interaction process. The trust source of the data can be traced through the trust chain. In the case of a trust code verification failure, the problem can be quickly located and solved through the trust chain; the trust code is a unique identifier obtained after hashing or other encryption processing of the trust chain. By verifying the trust code, the trust of the data can be quickly confirmed without recalculating or verifying the entire trust chain. In some embodiments, when a meter data request is received, the trust code database is first queried, the hash value of the trust chain of the requested data is recalculated or decrypted, and compared with the stored trust code. If the trust code is consistent, the data is legal and is directly added to the last queue waiting for parsing; if the trust code is inconsistent, it is rejected.

[0058] In some embodiments, a time limit is set for each trust code, and the time limit should be set according to the characteristics of the data and the application scenario. For data that changes frequently or is of high importance, the trust period can be set shorter; otherwise, it can be set longer. Preferably, it should be set between 2 and 7 days. The specific period needs to be adjusted according to the actual situation. When anomalies or signs of tampering are detected, the trust value should be updated immediately and the trust chain should be regenerated. The data should be reviewed regularly and the trust value should be updated according to the review results. The audit cycle is set according to the trust time limit. For example, when the trust period is one month, the audit cycle can be set to half a month or one month.

[0059] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: This application builds a trust chain and generates trust codes through real-time monitoring and stage trust value generation, achieving rapid trust verification and further improving data processing speed; the trust chain records the changes in the trust level of the data during the entire processing process, making it easier to trace the trusted source of the data and achieving the effect of quickly locating fault problems.

[0060] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data security communication method based on a smart meter, characterized in that: The method comprises: S100: Obtain basic data information, location information and bandwidth threshold information of the smart meter, perform preliminary verification and check, and generate a queue to be processed; classify each smart meter according to the basic data information, set a peer mechanism to verify the smart meters in the queue to be processed, and generate a mid-term queue; S200: setting difference verification according to the levels of different smart meters in the mid-term queue, and adding the smart meters that pass the verification to the final queue; S300: Obtain and parse the data information corresponding to the smart meter in the last queue, and store the parsed meter data; monitor steps S100 and S200 in real time, issue timely warnings for abnormal conditions that occur therein, and generate abnormal report information.

2. A data security communication method based on a smart meter as claimed in claim 1, characterized in that: The basic data information includes the unique code of the smart meter, historical data records, and data importance; the levels include low level, medium level and high level, and the levels are divided according to dynamic verification based on the basic data information and real-time data information of data transmission.

3. A data security communication method based on a smart meter as claimed in claim 1, characterized in that: The peer mechanism is to send peer query requests to multiple storage lists, perform key verification, and determine whether there is permission to perform parsing and reception. If there is permission, it will be added to the mid-term queue.

4. A data security communication method based on a smart meter as claimed in claim 1, characterized in that: The verification and inspection is to generate a first private key and a second private key based on the location information and bandwidth threshold information of the smart meter in the queue to be processed, and generate the corresponding first public key and second public key, generate a third private key of the smart meter based on the first private key and the second private key, and generate a third public key based on the third private key, obtain the real-time bandwidth information when the smart meter transmits data to the meter data receiving platform, and generate a fourth private key based on the real-time bandwidth information, and generate a fourth public key based on the fourth private key; call the first public key, the second public key, the third public key and the fourth public key in sequence, and judge whether there is permission for parsing and receiving according to the first private key, the second private key, the third private key and the fourth private key in sequence.

5. A data security communication method based on a smart meter as claimed in claim 1, characterized in that: The difference verification is to set different key verification methods and verification processes according to different levels, and set different encryption verification methods corresponding to different levels of smart meters.

6. A data security communication method based on a smart meter as claimed in claim 1, characterized in that: The pending queue, mid-term queue and final queue refer to sets of smart meters that have passed verification at different times and their corresponding data information respectively.

7. A data security communication method based on a smart meter as claimed in claim 1, characterized in that: The peer mechanism also includes a cache management strategy, which is to obtain the frequency by real-time monitoring of the query frequency and usage pattern of the public key, pre-set the frequency threshold, cache the public keys with a frequency greater than the frequency threshold, and set the cache area based on the level of the smart meter. At the same time, the size of the cache area is adjusted according to the real-time monitoring data, and the cache time is adjusted in real time; the cache management strategy also includes a pre-fetch mechanism, which is based on the basic prediction model to judge the usage trend of the public key, and pre-load the public keys required in the future time period into the cache.

8. A data security communication method based on a smart meter as claimed in claim 1, characterized in that: The method also includes S400: setting a dynamic line structure mechanism, dynamically adjusting the priority of the verification task based on the real-time data flow and load conditions, sorting the verification tasks according to the frequency of public key queries, and dynamically adjusting the verification process in combination with the level of the smart meter.

9. A data security communication method based on a smart meter as claimed in claim 1, characterized in that: The method also includes: S500: real-time monitoring of step S100 and step S200, and generating a stage trust value for the smart meter according to the monitoring data, obtaining a total trust value according to the stage trust value, and establishing a trust chain according to the total trust value, generating a trust code based on the trust chain, and directly adding the trust code to the last queue after verification.

10. A data security communication method based on a smart meter as claimed in claim 9, characterized in that: The stage trust value is generated based on the data verification process of the smart meter in the initial queue, mid-term queue and final queue. A trust threshold is pre-set in each stage. If the stage trust value of any stage is lower than the trust threshold, the subsequent trust judgment is cancelled.

Images