A smart processing and storage system for solar screen thickness measurement data

Abstract

This invention relates to the field of industrial automation testing and data management technology, and discloses an intelligent processing and storage system for solar screen thickness measurement data. The system includes: a data acquisition module, a thickness intelligent analysis module, an intelligent judgment module, an early warning notification module, a data storage module, and a cloud platform interface module. The data acquisition module collects multi-point thickness values, thickness uniformity indicators, and measurement environment data of the solar screen in real time to ensure data comprehensiveness. Combined with the thickness intelligent analysis module, the thickness measurement data is cleaned, normalized, and feature extracted. Based on a time series analysis model, the thickness change trend is calculated, abnormal thickness fluctuations and degradation patterns are identified, and a thickness analysis report is generated, thereby improving the accuracy and reliability of thickness measurement and reducing errors caused by manual operation.

Description

Technical Field

[0001] This invention relates to the field of industrial automation testing and data management technology, specifically to an intelligent processing and storage system for solar screen thickness measurement data. Background Technology

[0002] Solar screen printing is a key precision component used for printing circuits in the manufacturing process of photovoltaic cells. The thickness and uniformity of the screen directly determine the forming quality of the printing paste for the solar cells, which in turn has a decisive impact on the final performance and yield of the photovoltaic cells. Therefore, accurately measuring and managing the thickness of the solar screen is a core link in ensuring the manufacturing quality of photovoltaic products. A dedicated thickness measurement and data processing system needs to be set up at the solar cell production site.

[0003] Currently, in the production process of solar cells, the screen thickness measurement relies on manual operation and decentralized data recording, making it impossible to collect thickness data, environmental parameters, and equipment status in real time. This leads to discrepancies between the thickness analysis results and the actual working conditions. When there are temperature fluctuations, mechanical vibrations, or sensor drift in the production environment, the existing system has difficulty in dynamically identifying and compensating for measurement errors, resulting in insufficient reliability of thickness judgments and an inability to support accurate quality control.

[0004] Therefore, a smart processing and storage system for solar screen thickness measurement data is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent processing and storage system for solar screen thickness measurement data, which solves the problem mentioned in the background that existing systems are unable to dynamically identify and compensate for measurement errors, resulting in insufficient reliability of thickness determination conclusions and an inability to support accurate quality control.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent processing and storage system for solar screen thickness measurement data, the system comprising the following modules: The system includes a data acquisition module, a thickness intelligent analysis module, an intelligent judgment module, an early warning notification module, a data storage module, and a cloud platform interface module. The data acquisition module is used to collect the thickness measurement data of the solar screen in real time through a sensor network. The thickness measurement data includes multi-point thickness values, thickness uniformity index and measurement environment data. The measurement environment data includes temperature, humidity and vibration interference. The sensor network covers the screen surface in a grid pattern. The thickness intelligent analysis module is used to clean, normalize and extract features from the collected thickness measurement data, calculate the thickness change trend based on the time series analysis model, identify abnormal thickness fluctuations and degradation patterns, and generate a thickness analysis report. The intelligent judgment module is used to automatically judge the thickness qualification of the solar screen based on the output results of the thickness intelligent analysis module and in combination with the preset thickness tolerance standard, and output the judgment result and confidence score. The early warning notification module is used to trigger a multi-level alarm mechanism when the intelligent judgment module outputs unqualified or critical results, and send real-time early warning information to the operator through sound and light alarms, mobile application push and email notification. The data storage module is used to store the original thickness data, analysis results, judgment records and early warning logs using a distributed database, and to perform data compression, encryption and indexing operations and establish an index structure. The cloud platform interface module is used to synchronize data to the cloud server through standardized protocols, provide a remote access interface, support multi-user concurrent operation and data visualization, and enable cross-platform data sharing.

[0007] Preferably, the data acquisition module includes a sensor deployment unit, a data verification unit, and a real-time transmission unit; The sensor deployment unit is used to deploy various sensors in key areas of the solar screen, including laser thickness sensors, infrared sensors and image sensors. The sensors are distributed in a grid pattern to cover the entire surface of the screen. The data verification unit is used to perform redundancy verification and outlier filtering on the raw thickness data collected by the sensor. The verification methods include threshold comparison, time sequence consistency analysis and noise filtering. The real-time transmission unit is used to encrypt and transmit the verified data to the local processor via a wireless communication protocol, and adaptively switch between Wi-Fi and Bluetooth communication modes according to the signal strength.

[0008] Preferably, the thickness intelligent analysis module includes a data preprocessing unit, a trend analysis unit, and a report generation unit; The data preprocessing unit is used to normalize, imputate missing values, and extract features from the input thickness measurement data. The features include short-term thickness fluctuations, long-term thickness degradation rates, and environmentally relevant indicators. The trend analysis unit is used to train a thickness prediction model using machine learning algorithms. The model types include linear regression models and recurrent neural networks. The model parameters are optimized using historical data to predict future changes in thickness. The report generation unit is used to generate a thickness analysis report based on the trend analysis results. The report includes a thickness distribution map, outlier markers, and trend prediction curves, and the report is converted into PDF and image formats for output.

[0009] Preferably, the intelligent judgment module includes a rule engine unit, a judgment logic unit, and a result output unit; The rule engine unit is used to load preset thickness tolerance rules, which are based on the solar screen industry standard and include upper and lower limits for thickness and uniformity requirements. The judgment logic unit is used to match the thickness analysis data with the rule engine, calculate the thickness deviation score, and apply the decision tree algorithm to automatically classify and judge the results. The result output unit is used to generate a judgment report, which includes the judgment level, detailed reasons and improvement suggestions, and is stored in a structured format.

[0010] Preferably, the early warning notification module includes an alarm classification unit, a notification sending unit, and a log recording unit; The alarm classification unit is used to classify alarm levels according to the severity of the judgment result, including green normal, yellow alert and red emergency, with different levels corresponding to different response protocols; The notification sending unit is used to send warning information through channels such as SMS gateway, mail server and mobile application API; The log recording unit is used to store all early warning events and related data, generate audit trails, and establish indexes according to timestamps and event types.

[0011] Preferably, the alarm grading unit calculates a comparison result between the real-time thickness deviation value and a preset threshold, and redetermines the alarm level based on the comparison result; When the notification sending unit fails to send, it switches the communication channel and retryes sending according to a preset retry strategy. The log recording unit uses a time-series database to encrypt and store early warning logs, and establishes a joint index based on time range, alarm level, and device ID.

[0012] Preferably, the data storage module includes a database management unit, a backup and recovery unit, and a data retrieval unit; The database management unit is used to manage the distributed database cluster and perform data sharding and load balancing operations. The backup and recovery unit is used to periodically and automatically back up data to off-site storage and perform data recovery operations in case of system failure; The data retrieval unit is used to provide SQL and NoSQL query interfaces, parse and execute the input composite query conditions, and establish a streaming data processing channel.

[0013] Preferably, the database management unit uses LZ77 and Huffman coding compression algorithms to compress the stored data; The backup and recovery unit executes an incremental backup strategy, which compares the current data with the previous backup data and backs up the differences. The data retrieval unit integrates a full-text search engine, establishes an inverted index, and executes a keyword matching algorithm.

[0014] Preferably, the cloud platform interface module includes a data conversion unit, a transmission security unit, and an interface management unit; The data conversion unit is used to uniformly encapsulate multi-source heterogeneous data into JSON and XML formats; The transmission security unit is used to encrypt transmitted data using the SSL / TLS protocol; The interface management unit is used to monitor interface traffic and execute load balancing and rate limiting strategies.

[0015] Preferably, the data conversion unit loads a user-preset data pattern mapping configuration and executes user-defined conversion rules to process the data; The transmission security unit integrates a digital signature mechanism to verify the authenticity of the data source; The interface management unit is used to maintain API interface definitions of different versions, and to perform format conversion and adaptation for received old version API requests, and to execute API interface update and deployment according to a predetermined process.

[0016] Compared with existing technologies, this invention provides an intelligent processing and storage system for solar screen thickness measurement data, which has the following advantages: 1. In this invention, the data acquisition module collects multi-point thickness values, thickness uniformity indicators, and measurement environment data of the solar grid in real time to ensure data comprehensiveness. Combined with the thickness intelligent analysis module, the thickness measurement data is cleaned, normalized, and feature extracted. Based on the time series analysis model, the thickness change trend is calculated, abnormal thickness fluctuations and degradation patterns are identified, and a thickness analysis report is generated, thereby improving the accuracy and reliability of thickness measurement and reducing errors caused by manual operation.

[0017] 2. In this invention, the intelligent judgment module automatically determines the thickness qualification of the solar screen based on the output of the thickness intelligent analysis module and in combination with the preset thickness tolerance standard, outputs the judgment result and confidence score, and triggers a multi-level alarm mechanism through the early warning notification module when the judgment result is unqualified or critical, and sends real-time early warning information through sound and light alarms, mobile application push and email notification, so as to achieve rapid response and improve the timeliness and automation level of quality control.

[0018] 3. In this invention, the data storage module uses a distributed database to store the original thickness data, analysis results, judgment records, and early warning logs. The data is compressed, encrypted, and an index structure is established to ensure data security and integrity. At the same time, the cloud platform interface module synchronizes the data to the cloud server, provides a remote access interface, supports multi-user concurrent operation and data visualization, realizes cross-platform data sharing, and improves the flexibility and scalability of data management. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the intelligent processing and storage system for solar screen thickness measurement data according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] For specific implementation examples, please refer to: Figure 1 A smart processing and storage system for solar screen thickness measurement data, characterized in that the system includes the following modules: a data acquisition module, a thickness intelligent analysis module, an intelligent judgment module, an early warning notification module, a data storage module, and a cloud platform interface module; The data acquisition module is used to collect the thickness measurement data of the solar screen in real time through the sensor network. The thickness measurement data includes multi-point thickness values, thickness uniformity index and measurement environment data. The measurement environment data includes temperature, humidity and vibration interference. The sensor network covers the screen surface in a grid pattern. The thickness intelligent analysis module is used to clean, normalize, and extract features from the collected thickness measurement data. Based on a time series analysis model, it calculates thickness variation trends, identifies abnormal thickness fluctuations and degradation patterns, and generates a thickness analysis report. The specific steps of the time series analysis model include: The thickness measurement data is subjected to a stationarity test. If the data is non-stationary, it is differentially processed. The stationarity test uses the ADF test, and its calculation formula is as follows: ; in, This represents the augmented Dickey-Fuller test statistic. This represents the least squares estimate of the autoregressive coefficients. This represents the standard error of the estimated value; Calculate the autocorrelation function and, based on the graph of the autocorrelation function, conduct a preliminary analysis of the stationarity of the time series and the characteristics of the model. The formula for calculating the autocorrelation function is: ; in, Indicates lag The autocorrelation function value of the period, express Thickness measurement at time [time] This represents the mean of the thickness sequence. Indicates the total number of samples; The model parameters are estimated using the least squares method and the maximum likelihood estimation method. The least squares formula is: ; in, Indicates minimization. Represents the total number of samples. Indicates the first The actual observed thickness value of each sample Indicates the first Predicted values ​​for each sample; The formula for maximum likelihood estimation is: : in, To maximize, Represents the total number of samples. Indicates the first The actual observed thickness value of each sample Represents the logarithmic function. Represents the probability density function. Indicates unknown model parameters; Perform residual testing on the model. If the test results show that the white noise hypothesis is true, then the model is accepted; otherwise, further optimization and model reconfiguration are required. The thickness variation trend is predicted based on the fitted model, and the prediction results are output. The intelligent judgment module is used to automatically determine the thickness qualification of the solar grid based on the output of the thickness intelligent analysis module and the preset thickness tolerance standard, and output the judgment result and confidence score. The early warning notification module is used to trigger a multi-level alarm mechanism when the intelligent judgment module outputs unqualified or critical results, and send real-time early warning information to operators through audible and visual alarms, mobile application push and email notifications; The data storage module is used to store the original thickness data, analysis results, judgment records and early warning logs using a distributed database, and to compress, encrypt and establish an index structure for the data; The cloud platform interface module is used to synchronize data to the cloud server through standardized protocols, provide remote access interfaces, support multi-user concurrent operation and data visualization, and enable cross-platform data sharing.

[0022] The data acquisition module includes a sensor deployment unit, a data verification unit, and a real-time transmission unit; The sensor deployment unit is used to deploy various sensors in key areas of the solar screen, including laser thickness sensors, infrared sensors and image sensors. The sensors are distributed in a grid pattern to cover the entire surface of the screen. The data verification unit is used to perform redundancy verification and outlier filtering on the raw thickness data collected by the sensor. The verification methods include threshold comparison, time series consistency analysis and noise filtering. The real-time transmission unit is used to encrypt and transmit the verified data to the local processor via a wireless communication protocol, and adaptively switch between Wi-Fi and Bluetooth communication modes according to the signal strength.

[0023] The thickness intelligent analysis module includes a data preprocessing unit, a trend analysis unit, and a report generation unit; The data preprocessing unit is used to normalize the input thickness measurement data, imputate missing values, and extract features, including short-term thickness fluctuations, long-term thickness degradation rates, and environmentally relevant indicators. The trend analysis unit is used to train a thickness prediction model using machine learning algorithms. Model types include linear regression models and recurrent neural networks. The model parameters are optimized using historical data to predict future thickness changes. The specific training steps for the linear regression model include: Historical thickness data is divided into feature variables and target variables, and this is achieved by constructing a feature matrix formula: ; in, Represents the characteristic matrix, Indicates the first The first sample The specific numerical values ​​of each feature variable Indicates the first The first sample The specific numerical values ​​of each feature variable Indicates the first The first sample The specific numerical values ​​of each feature variable Indicates the first The first sample The specific numerical values ​​of each feature variable Indicates the first The first sample The specific numerical values ​​of each feature variable Indicates the first The first sample The specific numerical values ​​of each feature variable Represents the total number of samples. Indicates the number of features; The regression coefficients are calculated using the least squares method to establish a linear equation. The formula for calculating the regression coefficients is as follows: ; in, Represents the regression coefficient vector. Represents the characteristic matrix, Represents the target thickness value vector. Indicates transpose; The mean squared error is used as the loss function, and the model parameters are optimized by gradient descent. The loss function is defined as: ; in, This represents the mean squared error loss function. The total number of samples, For the first The true value of each sample The predicted value corresponding to the model; The optimization formula for gradient descent is: ; in, For learning rate, For the loss function with respect to parameters The gradient; The specific training steps for a recurrent neural network include: Construct the network structure, including an input layer, a recurrent hidden layer, and an output layer. The recurrent hidden layer performs the following computation to update its state: ; in, express The hidden state at all times express Input at any time This represents the weight matrix input to the hidden layer. This represents the weight matrix from hidden layer to hidden layer. Indicates the hidden layer bias; Time series data is input into the network, and the gradient is calculated using the backpropagation algorithm. The backpropagation calculation formula is: ; in, This indicates the partial derivative. For the first Layer activation output, For the error term propagated to this layer, Indicates transpose; Use the Adam optimizer to update the weight parameters and minimize the prediction error; The report generation unit is used to generate a thickness analysis report based on the trend analysis results. The report includes a thickness distribution map, outlier markers, and trend prediction curves, and is converted into PDF and image formats for output.

[0024] The intelligent judgment module includes a rule engine unit, a judgment logic unit, and a result output unit; The rule engine unit is used to load preset thickness tolerance rules, which are based on the solar screen industry standard and include upper and lower limits for thickness and uniformity requirements. The decision logic unit is used to match thickness analysis data with the rule engine, calculate thickness deviation scores, and automatically classify and determine the results using a decision tree algorithm. Specific steps include: Calculate the information gain for each feature, select the optimal splitting feature, and the formula for calculating information gain is: ; in, Representation of features Information gain Represents the dataset Information entropy Representation of features The number of possible values, Representation of features Values A subset of; Recursively construct decision tree nodes until the number of node samples is lower than a preset threshold and the purity requirement is met; The generated decision tree is pruned to reduce overfitting. The pruning function is as follows: ; in, This indicates the loss after pruning. Representation of decision tree The prediction error Represents the complexity parameter. Indicates the number of leaf nodes. Indicates cost; The pruned decision tree is used to classify the thickness analysis data and output the judgment results; The results output unit is used to generate a judgment report, which includes the judgment level, detailed reasons, and improvement suggestions, and is stored in a structured format.

[0025] The early warning notification module includes an alarm classification unit, a notification sending unit, and a log recording unit; The alarm grading unit is used to classify alarm levels according to the severity of the judgment results, including green (normal), yellow (caution), and red (emergency), with different response protocols corresponding to different levels; The notification sending unit is used to send alert information through channels such as SMS gateways, mail servers, and mobile application APIs; The log recording unit is used to store all warning events and related data, generate audit trails, and create indexes according to timestamps and event types.

[0026] The alarm grading unit calculates and compares the real-time thickness deviation value with a preset threshold, and redetermines the alarm level based on the comparison result. The specific steps include: Real-time thickness measurements are acquired, and the relative deviation from the standard thickness is calculated. The formula for calculating the relative deviation is: ; in, express The relative thickness deviation at any given time, express The thickness value measured at any time. Indicates the standard thickness value; The relative thickness deviation value is compared with the preset upper and lower threshold values, and the alarm level is determined based on the comparison result using conditional judgment logic. When a notification sending unit fails to send, it switches the communication channel and retryes sending according to a preset retry strategy. The log recording unit uses a time-series database to encrypt and store early warning logs, and establishes a joint index based on time range, alarm level, and device.

[0027] The data storage module includes a database management unit, a backup and recovery unit, and a data retrieval unit; The database management unit is used to manage distributed database clusters and perform data sharding and load balancing operations. The backup and recovery unit is used to periodically and automatically back up data to off-site storage and perform data recovery operations in case of system failure; The data retrieval unit provides SQL and NoSQL query interfaces, parses and executes complex input query conditions, and establishes a streaming data processing channel.

[0028] The database management unit uses LZ77 and Huffman coding compression algorithms to compress stored data; The specific steps for using LZ77 include: Using a sliding window to scan the data, find the longest match between the current sequence and historical sequences. The formula for calculating the longest match is: ; in, Indicates the matching distance. Indicates the current position. Indicates the matching position; Replace the matched sequence with pointers and length information; The specific steps of Huffman coding include: The frequency of character occurrences in statistical data; Construct a Huffman tree to generate a variable-length encoding table. The encoding length optimization objective is: ; in, Indicates minimization. Character The probability of its occurrence, Character The encoding length, Indicates the total number of characters; Convert the original data into a Huffman coded sequence; The backup and recovery unit executes an incremental backup strategy, comparing the current data with the previous backup data and backing up the differing portions. Specific steps include: Perform a full backup on the first run, storing all data; During subsequent backups, the changed data is identified by comparing the hash value of the current data with that of the previous backup. The hash-based block-level change detection algorithm is as follows: ; in, This represents the set of data blocks that have changed. Indicates the first One data block, This represents the hash value of the current data block. This represents the hash value of the last backed-up data block; Only the changed data blocks are backed up, and the original data information is recorded. The formula for calculating the backup data blocks is: ; in, This indicates the amount of data backed up this time. This represents the set of data blocks that have changed. Represents data block Size; The data retrieval unit integrates a full-text search engine, builds an inverted index, and executes a keyword matching algorithm. Specific steps include: Perform word segmentation on the stored text data to generate terms; Construct an inverted index that maps terms to a list of documents containing those terms. The inverted index structure is as follows: ; in, Indicates terms Inverted list, Indicates document identifier, Indicates the frequency of terms in the document. This represents a list of the positions where terms appear; When processing query requests, the query terms are segmented, the inverted index is retrieved, and the document relevance score is calculated using the term frequency-inverse document frequency algorithm. The calculation formula is as follows: ; in, Representing words In the document The weight values ​​in the middle, Representing words In the document The word frequency quantization value in the text Indicates the total number of documents. Indicates words The number of documents, Represents a logarithmic function.

[0029] The cloud platform interface module includes a data conversion unit, a transmission security unit, and an interface management unit; The data conversion unit is used to encapsulate multi-source heterogeneous data into JSON and XML formats. The Transmission Security Unit (TSU) is used to encrypt transmitted data via the SSL / TLS protocol; The interface management unit is used to monitor interface traffic and execute load balancing and rate limiting strategies.

[0030] The data transformation unit loads the user-preset data pattern mapping configuration and executes the user-defined transformation rules to process the data; The transmission security unit integrates a digital signature mechanism to verify the authenticity of the data source. Specific steps include: Use an asymmetric encryption algorithm to generate public and private key pairs; The sender uses their private key to encrypt the data digest and generate a digital signature. The signature generation formula is as follows: ; in, Indicates digital signature, This indicates that encryption is performed using a private key. The hash value representing the data; The recipient uses the public key to decrypt the signature and verify the data integrity and authenticity of its source. The verification formula is as follows: ; in, This indicates the verification result. This indicates that the public key is used for decryption. Indicates digital signature, The hash value representing the data; The interface management unit is used to maintain API interface definitions for different versions, and to perform format conversion and adaptation for received older version API requests. It executes API interface update and deployment according to a predetermined process, including the following steps: Define a version number for each API interface, using semantic versioning rules. The version number format is: ; in, Represents an identifier. Indicates the major version number. Indicates the minor version number. Indicates the revision number; When a request is received, the version information in the request header is parsed and the request is routed to the corresponding version of the interface processing logic. For older version requests, an adapter pattern is used to convert them to the new version format, ensuring backward compatibility. The version compatibility formula is: ; in, Indicates whether it is compatible. Indicates the major version number requested. Indicates the current major version number. Indicates the minor version number of the request. This indicates the current minor version number.

[0031] The operation steps of this intelligent processing and storage system for solar screen thickness measurement data are as follows: Step 1: Comprehensive Data Collection and Real-time Transmission The system first operates through a data acquisition module. This module's sensor deployment unit deploys various sensors in a grid pattern across key areas of the solar panel, including laser thickness sensors, infrared sensors, and image sensors, ensuring coverage of the entire panel surface. This allows for real-time acquisition of comprehensive thickness measurement data, including multi-point thickness values, thickness uniformity indicators, and environmental data such as temperature, humidity, and vibration interference. Subsequently, a data verification unit performs redundancy checks and outlier filtering on the raw data. Verification methods include threshold comparison, time-series consistency analysis, and noise filtering to ensure data quality. Finally, a real-time transmission unit encrypts the verified data via a wireless communication protocol and adaptively switches between Wi-Fi and Bluetooth communication modes before transmitting it to the local processor.

[0032] Step 2: Intelligent Thickness Analysis and Trend Prediction The collected data enters the thickness intelligent analysis module for processing. First, the data preprocessing unit cleans, normalizes, imputes missing values, and extracts features from the input thickness measurement data. The extracted features include short-term thickness fluctuations, long-term thickness degradation rates, and environmentally relevant indicators. Next, the trend analysis unit applies a time series analysis model to analyze historical data, calculate thickness change trends, and identify abnormal thickness fluctuations and degradation patterns. Finally, the report generation unit generates a thickness analysis report based on the analysis results. The report includes a thickness distribution map, outlier markers, and trend prediction curves, and is output in formats such as PDF.

[0033] Step 3: Automatic Judgment and Confidence Scoring The analyzed data is then automatically evaluated by the intelligent judgment module. The rule engine unit loads the thickness tolerance standard based on the industry standard. The judgment logic unit matches the analyzed data with the tolerance standard, calculates the thickness deviation score, and applies the decision tree algorithm to automatically classify the thickness qualification of the solar screen. It outputs the judgment results including qualified, critical and unqualified levels, and attaches a confidence score. The result output unit generates a structured judgment report containing the judgment level, detailed reason explanation and improvement suggestions.

[0034] Step 4: Sending multi-level early warnings and notifications When the intelligent judgment module outputs unqualified or critical judgment results, the early warning notification module is immediately activated. The alarm grading unit classifies the alarm level according to the severity of the result. Subsequently, the notification sending unit triggers a multi-level alarm mechanism, sending real-time early warning information to operators through audible and visual alarms, mobile application push notifications, and email notifications. To ensure that the notification is delivered, the notification sending unit will switch communication channels according to a preset retry strategy when the sending fails. At the same time, the log recording unit encrypts and stores all early warning events and related data in a time-series database, generating an audit trail for future reference.

[0035] Step 5: Secure Storage and Cloud Synchronization The final step in the system is the persistence and sharing of data processing results. The data storage module uses a distributed database to store the original thickness data, analysis results, judgment records, and early warning logs. The database management unit performs data sharding and load balancing operations, and uses LZ77 and Huffman coding compression algorithms to compress and encrypt the data. The backup and recovery unit executes an incremental backup strategy, periodically backing up the data to off-site storage. The data retrieval unit provides SQL and NoSQL query interfaces. Meanwhile, the cloud platform interface module encapsulates the data into JSON and XML formats through the data conversion unit. After being encrypted by the transmission security unit using the SSL / TLS protocol, the data is synchronized to the cloud server through the interface management unit, providing remote access interfaces, supporting multi-user concurrent operations and data visualization, and enabling cross-platform data sharing.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart processing and storage system for solar screen thickness measurement data, characterized in that: The system includes the following modules: a data acquisition module, a thickness intelligent analysis module, an intelligent judgment module, an early warning notification module, a data storage module, and a cloud platform interface module; The data acquisition module is used to collect the thickness measurement data of the solar screen in real time through a sensor network. The thickness measurement data includes multi-point thickness values, thickness uniformity index and measurement environment data. The measurement environment data includes temperature, humidity and vibration interference. The sensor network covers the screen surface in a grid pattern. The thickness intelligent analysis module is used to clean, normalize and extract features from the collected thickness measurement data, calculate the thickness change trend based on the time series analysis model, identify abnormal thickness fluctuations and degradation patterns, and generate a thickness analysis report. The intelligent judgment module is used to automatically judge the thickness qualification of the solar screen based on the output results of the thickness intelligent analysis module and in combination with the preset thickness tolerance standard, and output the judgment result and confidence score. The early warning notification module is used to trigger a multi-level alarm mechanism when the intelligent judgment module outputs unqualified or critical results, and send real-time early warning information to the operator through sound and light alarms, mobile application push and email notification. The data storage module is used to store the original thickness data, analysis results, judgment records and early warning logs using a distributed database, and to perform data compression, encryption and indexing operations and establish an index structure. The cloud platform interface module is used to synchronize data to the cloud server through standardized protocols, provide a remote access interface, support multi-user concurrent operation and data visualization, and enable cross-platform data sharing.

2. The intelligent processing and storage system for solar screen thickness measurement data according to claim 1, characterized in that: The data acquisition module includes a sensor deployment unit, a data verification unit, and a real-time transmission unit; The sensor deployment unit is used to deploy various sensors in key areas of the solar screen, including laser thickness sensors, infrared sensors and image sensors. The sensors are distributed in a grid pattern to cover the entire surface of the screen. The data verification unit is used to perform redundancy verification and outlier filtering on the raw thickness data collected by the sensor. The verification methods include threshold comparison, time sequence consistency analysis and noise filtering. The real-time transmission unit is used to encrypt and transmit the verified data to the local processor via a wireless communication protocol, and adaptively switch between Wi-Fi and Bluetooth communication modes according to the signal strength.

3. The intelligent processing and storage system for solar screen thickness measurement data according to claim 1, characterized in that: The thickness intelligent analysis module includes a data preprocessing unit, a trend analysis unit, and a report generation unit; The data preprocessing unit is used to normalize, imputate missing values, and extract features from the input thickness measurement data. The features include short-term thickness fluctuations, long-term thickness degradation rates, and environmentally relevant indicators. The trend analysis unit is used to train a thickness prediction model using machine learning algorithms. The model types include linear regression models and recurrent neural networks. The model parameters are optimized using historical data to predict future changes in thickness. The report generation unit is used to generate a thickness analysis report based on the trend analysis results. The report includes a thickness distribution map, outlier markers, and trend prediction curves, and the report is converted into PDF and image formats for output.

4. The intelligent processing and storage system for solar screen thickness measurement data according to claim 1, characterized in that: The intelligent judgment module includes a rule engine unit, a judgment logic unit, and a result output unit; The rule engine unit is used to load preset thickness tolerance rules, which are based on the solar screen industry standard and include upper and lower limits for thickness and uniformity requirements. The judgment logic unit is used to match the thickness analysis data with the rule engine, calculate the thickness deviation score, and apply the decision tree algorithm to automatically classify the judgment results. The result output unit is used to generate a judgment report, which includes the judgment level, detailed reasons and improvement suggestions, and is stored in a structured format.

5. The intelligent processing and storage system for solar screen thickness measurement data according to claim 1, characterized in that: The early warning notification module includes an alarm classification unit, a notification sending unit, and a log recording unit; The alarm classification unit is used to classify alarm levels according to the severity of the judgment result, including green normal, yellow alert and red emergency, with different levels corresponding to different response protocols; The notification sending unit is used to send warning information through channels such as SMS gateway, mail server and mobile application API; The log recording unit is used to store all early warning events and related data, generate audit trails, and establish indexes according to timestamps and event types.

6. The intelligent processing and storage system for solar screen thickness measurement data according to claim 5, characterized in that: The alarm grading unit calculates the comparison result between the real-time thickness deviation value and the preset threshold, and redetermines the alarm level based on the comparison result; When the notification sending unit fails to send, it switches the communication channel and retryes sending according to a preset retry strategy. The log recording unit uses a time-series database to encrypt and store early warning logs, and establishes a joint index based on time range, alarm level, and device ID.

7. The intelligent processing and storage system for solar screen thickness measurement data according to claim 1, characterized in that: The data storage module includes a database management unit, a backup and recovery unit, and a data retrieval unit; The database management unit is used to manage the distributed database cluster and perform data sharding and load balancing operations. The backup and recovery unit is used to periodically and automatically back up data to off-site storage and perform data recovery operations in case of system failure; The data retrieval unit is used to provide SQL and NoSQL query interfaces, parse and execute the input composite query conditions, and establish a streaming data processing channel.

8. The intelligent processing and storage system for solar screen thickness measurement data according to claim 7, characterized in that: The database management unit uses LZ77 and Huffman coding compression algorithms to compress the stored data; The backup and recovery unit executes an incremental backup strategy, which compares the current data with the previous backup data and backs up the differences. The data retrieval unit integrates a full-text search engine, establishes an inverted index, and executes a keyword matching algorithm.

9. The intelligent processing and storage system for solar screen thickness measurement data according to claim 1, characterized in that: The cloud platform interface module includes a data conversion unit, a transmission security unit, and an interface management unit. The data conversion unit is used to uniformly encapsulate multi-source heterogeneous data into JSON and XML formats; The transmission security unit is used to encrypt transmitted data using the SSL / TLS protocol; The interface management unit is used to monitor interface traffic and execute load balancing and rate limiting strategies.

10. The intelligent processing and storage system for solar screen thickness measurement data according to claim 9, characterized in that: The data conversion unit loads the user-preset data pattern mapping configuration and executes the user-defined conversion rules to process the data; The transmission security unit integrates a digital signature mechanism to verify the authenticity of the data source; The interface management unit is used to maintain API interface definitions of different versions and to process received older versions. The API requests are formatted and adapted, and the API interface is updated and deployed according to the predetermined process.

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