Intelligent acquisition and analysis method and system for screen multivariate data

Abstract

The invention relates to the technical field of data analysis, in particular to an intelligent acquisition and analysis method and system for screen multivariate data. The method comprises a screen data acquisition step, a first processing step, a second processing step, a standard data determination step, a similarity calculation step, a first judgment step, an association step, a checking step, an abnormity alarm step and an output step. The system comprises a screen data acquisition module, a first processing module, a second processing module, a standard data determination module, a similarity calculation module, a first judgment module, an association module, a verification module, an abnormity alarm module and an output module. The method has the effect of improving the accuracy of the data.

Description

Technical Field

[0001] The present application relates to the technical field of data analysis, and in particular to a method and system for intelligently collecting and analyzing multivariate screen data. Background Art

[0002] At present, with the rapid development of information technology, data plays a vital role in all walks of life. However, the falsification of computer data has become an issue that cannot be ignored, especially in key areas involving economic interests, such as the recording and display of refueling data at gas stations. Traditional data recording methods often rely on data stored in computers, which are easy to be tampered with or forged, thus affecting the authenticity and credibility of the data.

[0003] Gas stations are important nodes for energy supply, and the accuracy of their refueling data is directly related to the rights and interests of both parties to the transaction. Therefore, how to ensure the authenticity of gas station refueling data and prevent data falsification has become an urgent problem to be solved.

[0004] In the prior art, although there are some data verification methods, most of them are limited to the verification of a single data source, or rely on the auxiliary verification of external hardware devices. These methods are powerless when dealing with complex data fraud methods. Therefore, a method is developed that can intelligently collect multi-dimensional data displayed on the screen and perform comprehensive verification and analysis to improve the accuracy of gas station refueling data. Summary of the invention

[0005] In order to improve the accuracy of data, the present application provides a method and system for intelligent collection and analysis of multivariate screen data.

[0006] In the first aspect, the present application provides a method for intelligent collection and analysis of multivariate screen data, which adopts the following technical solution: A method for intelligent collection and analysis of multivariate screen data, comprising the following steps: Screen data collection: real-time monitoring and collection of refueling data displayed on the gas station screen, and acquisition of refueling process operation data; First processing: pre-processing the collected refueling data, extracting key data fields, and recording them as first data; Second processing: pre-processing the collected single refueling process operation data, extracting the data changes within the interval T time, and recording them as the second data; Standard data determination: Determine standard refueling data based on normal data; Similarity calculation: calculating the similarity between the second data and the standard refueling data; First judgment: judge whether the similarity is greater than or equal to the set threshold. If so, execute the association step; otherwise, execute the verification step; Association: Store the second data as a label of the first data, establish an association relationship, and then perform the output step; Verification: Obtain environmental data, determine whether there is refueling and intervention behavior, verify authenticity, and perform manual verification. If the manual verification is correct, store the environmental data as additional data, associate it with the first data for storage, and then execute the output step; otherwise, execute the abnormal alarm step; Abnormal alarm: establish an abnormal detection model, issue real-time alarms for abnormal data found during the verification process, and generate abnormal reports; Output: Display the verified data in a visual form.

[0007] By adopting the above technical solution, through real-time monitoring and collection of refueling data displayed on the gas station screen, instant data acquisition and processing are realized, and the timeliness and accuracy of the data are improved; the introduction of intelligent collection and analysis methods can automatically pre-process the collected data and extract key fields, reducing manual intervention and improving analysis efficiency; by pre-processing the operation data of the refueling process and extracting data changes within the interval T time, the solution can capture the data dynamics of the refueling process in more detail and provide richer information for subsequent analysis; distinguishing the processing methods of the first data and the second data helps to more accurately understand the different levels of refueling behavior and improve the accuracy of the analysis.

[0008] By determining standard refueling data and calculating the similarity with the actual collected data, the solution can identify refueling behaviors that meet the standards, provide a reliable basis for data association, and reduce the occurrence of data falsification, thereby maintaining the accuracy of the data; setting a similarity threshold and making a first judgment based on it can help quickly screen out potential normal or abnormal refueling behaviors and provide direction for subsequent processing; when the similarity does not meet the threshold, the solution can obtain environmental data and determine whether there is refueling behavior and intervention behavior, thereby verifying the authenticity of the data; introducing a manual verification link ensures the accuracy and reliability of data verification and avoids false positives or negative positives; establishing an anomaly detection model, real-time alarming of abnormal data discovered during the verification process, and generating anomaly reports, which helps to promptly discover and handle potential refueling behavior anomalies or system errors; the abnormal alarm function enhances the security and stability of the system and provides strong support for the management and operation of gas stations.

[0009] Displaying the verified data in a visual form makes the analysis results more intuitive and easy to understand, which helps gas station managers quickly understand the refueling behavior and make reasonable decisions; and the data is more authentic, which is convenient for truly reflecting economic benefits.

[0010] Optionally, a label adding step is further provided between the associating step and the outputting step; Label adding: including the first label acquisition step and the first labeling step; First label acquisition: obtaining the reason for the difference between the second data and the standard refueling data, forming a first label according to the reason for the difference and the staff information, and executing the first labeling step; First labeling: Associating a first label with the first data, and then performing an output step.

[0011] By adopting the above technical solution, through the first label acquisition step, the reasons for the differences between the second data and the standard refueling data can be deeply analyzed; this helps to understand abnormal situations or special events in refueling behavior, and provides richer background information for subsequent data analysis and decision-making; and this transparency helps prevent data from being tampered with or falsified, because any inconsistent data will be marked and traced back to its source.

[0012] By forming the first label based on the reason for the difference and the staff information, the staff involved in the refueling process and their responsibilities can be clearly identified; this helps to strengthen the management and supervision of gas stations, ensure the accuracy and compliance of refueling behavior, and help to quickly locate the responsible person when data problems are discovered, thereby effectively curbing data falsification.

[0013] Through the first labeling step, the first label is associated with the first data, so that the differences and causes between the data can be more intuitively displayed during visual display; this helps gas station managers to identify problems more quickly and take corresponding measures to improve them; after the introduction of the first label, the refueling data can be classified and analyzed more finely, and the difficulty of data falsification is increased; and this helps managers to understand data characteristics more quickly, discover potential problems and trends, and make more accurate and efficient decisions.

[0014] The introduction of the first tag makes the refueling data more traceable. In the subsequent data analysis and audit process, the first tag can be used to quickly locate relevant refueling behaviors and events to ensure the accuracy and completeness of the data. Auditors can quickly locate relevant refueling behaviors and events through the first tag to check the accuracy and completeness of the data, thereby effectively preventing data falsification. The reasons for data differences analyzed in the first label acquisition step provide rich background information for data authenticity verification; this information helps managers understand whether the differences between the data are caused by normal factors (such as equipment failure, environmental factors, etc.) or abnormal behaviors such as data falsification.

[0015] Through the first labeling step, the first label is associated with the first data, so that relevant data and events can be located more quickly during the data authenticity verification process; this helps to speed up the verification speed and improve the verification efficiency.

[0016] The introduction of the first label makes the data authenticity verification process more rigorous and reliable. By comprehensively considering the reasons for data differences, staff information, and the associated information of the first label, managers can more accurately judge the authenticity of the data, thereby avoiding misjudgment or omission.

[0017] Optionally, a second label acquisition step, a second judgment step, a second labeling step, a third labeling step and a manual verification step are further provided between the first labeling step and the output step; Second label acquisition: obtaining whether the refueling user is an old user, if yes, executing the second judgment step, otherwise, executing the second labeling step; Second judgment: judging whether the refueling habits of old users are close to historical habits, if so, executing the third labeling step, otherwise, executing the manual verification step; Second labeling: associating the new user label with the first data and performing an output step; Third labeling: associating the old user label with the first data and the historical refueling data, and then performing the output step; Manual verification: Obtain the refueling video and verify whether there is any refueling behavior. If so, execute the output step; otherwise, execute the abnormal alarm step.

[0018] By adopting the above technical solution, the solution can distinguish whether the refueling user is an old user, and further analyze whether the refueling habits of the old user are close to the historical habits; this detailed user behavior analysis helps gas stations to understand user needs more deeply and provide more personalized services; through the second labeling and third labeling steps, the new user label and the old user label are associated with the first data, realizing the label management of data. This helps gas stations to manage and utilize data resources more efficiently and improve the efficiency of data analysis and decision-making.

[0019] In the manual verification step, abnormal refueling behavior can be quickly identified by obtaining refueling videos and verifying whether there is refueling behavior. This real-time video monitoring and verification method enhances the safety and prevention capabilities of gas stations; for old users, by associating historical refueling data, more accurate refueling recommendations and services can be provided, improving user satisfaction and loyalty; by analyzing the closeness of old users' refueling habits to historical habits, as well as real-time video monitoring and verification of refueling behavior, data falsification can be effectively prevented. This multi-level verification method enhances the credibility and accuracy of the data; after introducing the second and third tags, refueling data can be more accurately identified and classified, improving the overall quality and availability of the data; by analyzing whether the user is an old user, whether the refueling habits are close to historical habits, and real-time video monitoring and other information, it provides rich information and clues for data authenticity verification.

[0020] After the introduction of label management, relevant refueling data and events can be located more quickly, accelerating the process of data authenticity verification; through multi-level verification methods and information comparison, the authenticity of the data can be judged more accurately, reducing the possibility of misjudgment and omission.

[0021] Optionally, before the output step, a secure transmission step is also provided; Secure transmission: including data storage step, segmentation step, data integration step, encryption step and verification and restoration step; Data storage: storing the first data and the second data within the time period t as a first data packet, calculating a hash value of the first data, and dividing the hash value into n parts, each of which is recorded as third data; Segmentation: segment the first data into n parts, and record each part as fourth data, where the value of n is greater than 1 and less than the number of characters in the first data; Data integration: shuffle the third data and the fourth data at the same position, record them as fifth data, form an index array according to the second data, establish an association between the index array and the fifth data, and store them as a second data packet; Encryption: using an encryption algorithm to encrypt the data in the second data packet, using the encrypted second data packet as output data for data transmission, and performing a verification and restoration step before performing the output step; Verify and restore: restore the third data and the fourth data according to the index array, and verify whether the fourth data has changed. If not, restore the data to the first data and the second data, and then execute the output step.

[0022] By adopting the above technical solution, through multiple links such as data storage, segmentation, integration and encryption, the solution provides all-round security protection for data; especially the encryption step, which uses an encryption algorithm to encrypt the data in the second data packet, effectively preventing data leakage and tampering during transmission.

[0023] When storing data, the hash value of the first data is calculated and divided into n parts, which are scrambled and integrated together with the divided first data. This processing method not only increases the complexity of the data, but also makes it easier to check the integrity of the data during verification and restoration.

[0024] Although the secure transmission step increases the complexity of data processing, through reasonable segmentation, integration and encryption processing, the solution can optimize data transmission efficiency and reduce data transmission delays and bandwidth occupancy while ensuring data security; the multiple sub-steps in the secure transmission step, especially the encryption and verification and restoration steps, increase the difficulty of data falsification; even if someone tries to tamper with the data, they need to bypass multiple security links, which greatly raises the threshold for falsification.

[0025] By verifying the restoration step, it is possible to check whether the data has changed during transmission; this mechanism helps ensure the authenticity and credibility of the data and reduce decision-making errors caused by data falsification.

[0026] The hash value calculation and verification and restoration steps in the secure transmission step provide an effective means for verifying the authenticity of the data; by comparing the hash values ​​before and after transmission, it is possible to determine whether the data has changed, thereby verifying the authenticity of the data.

[0027] Compared with traditional data verification methods, this solution can more accurately verify the authenticity of data through the synergy of multiple sub-steps. In particular, the data integrity check in the verification and restoration step can ensure that the data has not been changed during the transmission process.

[0028] Although the secure transmission step increases the complexity of data processing, it also provides richer information and means for data authenticity verification; by rationally utilizing this information and means, the efficiency and accuracy of verification can be improved.

[0029] Optionally, before the verification and restoration step, an array calculation and data verification step is also provided; Data calculation: Calculate the hash value of the index array, recorded as the sixth data; Data verification: Verify whether the hash value in the sixth data has changed. If not, execute the verification and restoration step; otherwise, execute the abnormal alarm step.

[0030] By adopting the above technical solution, by calculating the hash value (sixth data) of the index array and checking whether it has changed in the data verification step, the solution provides an additional verification means for data integrity; this mechanism can ensure that the index array is not tampered with during the transmission process, thereby indirectly ensuring the integrity of the fifth data associated therewith.

[0031] The introduction of array calculation and data verification steps increases the complexity and security of data processing; even if someone tries to tamper with the data during transmission, it is necessary to modify the index array and the fifth data at the same time and ensure that the association between them is not destroyed; this high-difficulty tampering requirement further improves the security of the data.

[0032] In the data verification step, if the hash value of the sixth data is found to have changed, the abnormal alarm step is executed; this mechanism can promptly detect and respond to abnormal situations such as data tampering or transmission errors, thereby avoiding the use of erroneous data for subsequent processing or decision-making.

[0033] By adding array calculation and data verification steps, the solution improves the robustness of the entire data processing and transmission system. Even if errors or anomalies occur in certain links, the system can detect them in time and take corresponding processing measures to ensure the accuracy and reliability of the data.

[0034] The introduction of array calculation and data verification steps makes data falsification more difficult and expensive; falsifiers need to modify data in multiple links at the same time and ensure that the correlation between them is not destroyed, which greatly increases the difficulty and cost of falsification.

[0035] Through multi-level verification methods, the scheme can ensure the authenticity and credibility of the data. Even if someone tries to tamper with the data, it is difficult to bypass all the verification links, thereby improving the credibility of the data; the array calculation and data verification steps provide an additional verification layer for data authenticity verification; by comparing the hash values ​​before and after transmission, it can be determined whether the index array has changed, thereby indirectly verifying the authenticity of the fifth data.

[0036] Compared with traditional data verification methods, this scheme can more accurately verify the authenticity of data through multi-level verification means; in particular, by calculating the hash value of the index array and verifying it, the accuracy of verification can be further improved.

[0037] Optionally, after the abnormal alarm step, a data recovery step is set; Data recovery: Create a data backup for the first data packet and associate it with the data of the computer terminal. After executing the abnormal alarm step, trigger the data recovery mechanism, restore the first data packet from the data backup according to the data of the computer terminal, and then execute the output step.

[0038] By adopting the above technical solution and introducing the data recovery step, the original data can be quickly restored from the data backup when the data is abnormal or tampered with; by establishing a data backup and associating it with the computer terminal data, the solution provides additional security for the data; even if the original data has problems during transmission or storage, it can be quickly restored through the data recovery mechanism, thereby ensuring the integrity and accuracy of the data.

[0039] After the abnormal alarm step, the data recovery mechanism is triggered and the data is automatically restored from the data backup. This automated exception handling process reduces the need for manual intervention and improves processing efficiency and accuracy. The addition of the data recovery step enables the entire data processing and transmission system to recover quickly and continue working when faced with abnormal situations. The existence of the data recovery mechanism requires counterfeiters to consider how to bypass the data backup and recovery mechanism when tampering with data. This additional layer of protection increases the difficulty of counterfeiting and reduces the risk of data tampering.

[0040] Through the data recovery mechanism, even if the data is abnormal during transmission or storage, the original data can be quickly restored; this mechanism enhances the credibility of the data, allowing enterprises to rely more on data for decision-making and business development; during the data authenticity verification process, if problems or abnormalities are found in the data, the original data can be restored from the data backup through the data recovery step for re-verification; this method ensures the accuracy and reliability of data authenticity verification; the introduction of the data recovery step makes the data authenticity verification process more flexible. Even if errors or abnormalities occur in certain links, the data can be quickly restored through the data recovery mechanism and the verification process can continue, thereby ensuring the integrity and accuracy of the verification.

[0041] Optionally, in the data integration step, the scrambling process is implemented using a random algorithm or a pseudo-random algorithm.

[0042] By adopting the above technical solution, a random algorithm or a pseudo-random algorithm can generate an unpredictable data arrangement order, which makes it difficult for attackers to crack or tamper with the data by analyzing the arrangement rules of the data; scrambling is performed through random or pseudo-random data arrangement, making the originally ordered or related data difficult to identify.

[0043] In some cases, data may contain certain patterns or regularities that may be exploited by attackers to crack the data. Random or pseudo-random scrambling can effectively prevent the leakage of these patterns or regularities, thereby protecting the confidentiality of the data.

[0044] Although scrambling increases the complexity of data processing, random algorithms or pseudo-random algorithms usually have higher execution efficiency; therefore, using these algorithms in the data integration step can optimize the efficiency of data integration while ensuring data security.

[0045] Random algorithms or pseudo-random algorithms usually have good versatility and adaptability, and can be applied to data processing tasks of different types and scales. Since the scrambling process uses random or pseudo-random algorithms, the order of data arrangement is difficult to predict. This increases the difficulty for counterfeiters to tamper with the data because they need to modify multiple data points at the same time and ensure that the correlation between them is not destroyed.

[0046] Scrambling makes it more difficult for data to be tampered with or forged during transmission or storage; therefore, scrambled data has a higher degree of credibility during verification, helping to ensure the authenticity and accuracy of the data.

[0047] When verifying the scrambled data, you can check whether the data has changed by comparing the order of the data before and after transmission. Since the scrambling process increases the confusion and complexity of the data, it is difficult for attackers to crack or tamper with the data through simple analysis methods. Therefore, using scrambled data in the process of data authenticity verification can further improve the accuracy of verification.

[0048] Optionally, in the data integration step, a time series is extracted according to the first data and a timestamp is formed, and then an index array is formed according to the second data and the timestamp.

[0049] By adopting the above technical solution, by generating a timestamp for time series data and constructing an index array based on the timestamp and the second data, the efficiency of data retrieval can be greatly improved; the timestamp, as the unique identifier of the data, can quickly locate the specific data point, and the index array further accelerates the data retrieval process; the technical solution optimizes the storage structure of the data by introducing the timestamp and the index array; the timestamp allows the data to be arranged and stored in chronological order, which is convenient for subsequent analysis and processing; and the index array provides a fast access path to the data, reducing the data reading time; in a distributed system or a multi-threaded environment, data consistency is an important issue, and by generating a unique timestamp for the time series data and constructing an index array based on the timestamp, the consistency of the data during transmission and storage can be ensured; the uniqueness of the timestamp ensures that each data point has a unique identifier, avoiding data duplication and conflict.

[0050] This technical solution supports complex queries based on timestamps. For example, it can filter and sort based on time range, data value and other conditions to meet diverse data analysis needs. In addition, index arrays also provide the ability to quickly group and aggregate data, further enhancing the flexibility and efficiency of queries.

[0051] By generating timestamps for time series data and building index arrays based on timestamps, duplicate storage and redundancy of data can be avoided. Each data point has a unique timestamp identifier, so it only needs to be saved once during storage, which reduces the storage space requirements for data and improves data storage efficiency.

[0052] The introduction of timestamps and index arrays also improves data security. The uniqueness of timestamps makes it difficult for data to be tampered with or forged during transmission; while index arrays provide a fast access path to data, allowing timely detection and processing when data is illegally accessed or tampered with.

[0053] Optionally, in the manual verification step, if the refueling video shows that there is no refueling behavior, the abnormal alarm step is triggered and an abnormal report including a video screenshot is generated to facilitate verification and processing by the management personnel.

[0054] By adopting the above technical solution, by introducing manual verification steps and combining the judgment of refueling video, the solution can more accurately identify abnormal situations where there is no refueling behavior; compared with traditional manual inspections or automated monitoring based on simple rules, this greatly improves the accuracy and reliability of monitoring; when the refueling video shows that there is no refueling behavior, the system can immediately trigger the abnormal alarm step and generate an abnormal report containing video screenshots. This instant feedback mechanism enables managers to quickly obtain abnormal information, so that they can take timely measures to verify and handle it, avoiding potential safety risks and losses; the video screenshots included in the abnormal report provide managers with intuitive evidence, which facilitates them to quickly understand the abnormal situation and make judgments; this reduces the time and energy that managers need to spend on investigation and verification, and improves the overall management efficiency; although manual verification is involved, the entire process is intelligent in triggering abnormal alarms and generating abnormal reports. This combination of manual and automated methods not only ensures the accuracy of monitoring, but also improves work efficiency, and is an important step towards comprehensive intelligent management.

[0055] Safety is of vital importance for places such as gas stations that involve flammable and explosive items. This technical solution effectively avoids safety risks caused by misoperation or malicious behavior by promptly discovering and handling abnormal situations where refueling does not occur, providing strong protection for the safe operation of gas stations.

[0056] Optionally, before the encryption step, a data compression step and a verification step are also included; Data compression: compressing the data in the second data packet to reduce the bandwidth and storage space required for data transmission, and then using the compressed data as the data to be encrypted; Verification: Perform integrity check on the compressed data as the data to be encrypted, generate a verification code, and perform the encryption step together with the verification code and the data to be encrypted.

[0057] By adopting the above technical solution, data compression can significantly reduce the bandwidth required for data transmission. By compressing data, the load of network transmission can be reduced and the efficiency of data transmission can be improved. The compressed data occupies less storage space, thereby reducing storage costs.

[0058] Compressing data not only reduces storage space usage, but also improves query performance. Because compressed data takes up less I / O bandwidth, the database system can read and write data faster, thereby improving overall query speed.

[0059] The verification step performs integrity verification on the compressed data, generates a check code, and encrypts it together with the encryption; in this way, after the data is decrypted, the integrity of the data can be verified by the check code to ensure that the data has not been tampered with or damaged during transmission and storage.

[0060] The verification step increases the security of the data. Even if the data is intercepted during transmission, attackers cannot easily tamper with the data because any tampering will cause the verification code to mismatch and be discovered by the receiver.

[0061] Through the verification step, the system can detect and handle data errors in a timely manner, thereby enhancing the reliability of the system; this helps to avoid system crashes or performance degradation caused by data errors.

[0062] The addition of data compression and verification steps optimizes the encryption process; by compressing first and then encrypting, it not only reduces the volume of encrypted data, but also improves the security of the encryption process. At the same time, the addition of the verification step provides a guarantee for the integrity of the decrypted data.

[0063] This technical solution improves overall performance by reducing the bandwidth and storage space required for data transmission and improving query performance; this is especially important in scenarios that require processing large amounts of data, such as big data analysis and cloud computing.

[0064] By combining verification steps and compression technology, the system can detect and handle data errors in a timely manner, avoiding system crashes or performance degradation caused by data errors, thereby enhancing system stability.

[0065] In the second aspect, the present application provides a collection and analysis system, which adopts the following technical solution: A collection and analysis system includes the following modules: Screen data acquisition module: used to monitor and collect refueling data displayed on the gas station screen in real time, and obtain refueling process operation data; The first processing module: the input end is connected to the output end of the screen data acquisition module, and is used to pre-process the collected refueling data and extract key data fields, which are recorded as first data; The second processing module: the input end is connected to the output end of the screen data acquisition module, and is used to pre-process the collected single refueling process operation data, extract the data changes within the interval T time, and record them as the second data; Standard data determination module: determines standard refueling data based on normal data; Similarity calculation module: the input end is connected to the output end of the second processing module and the output end of the standard data determination module, and is used to calculate the similarity between the second data and the standard refueling data; The first judgment module: the input end is connected to the output end of the similarity calculation module, and is used to judge whether the similarity is greater than or equal to a set threshold; Association module: an input end is connected to the output end of the first judgment module and the output end of the first processing module, and is used to store the second data as a label of the first data and establish an association relationship after the similarity is greater than or equal to a set threshold; Verification module: The input end is connected to the output end of the first judgment module, and is used to obtain environmental data after the similarity is less than the set threshold, and determine whether there is refueling behavior and intervention behavior, verify the authenticity, and perform manual verification. If the manual verification is correct, the environmental data is stored as additional data and associated with the first data for storage; Abnormal alarm module: The output end connection of the input verification module is used to establish an abnormal detection model, issue a real-time alarm for abnormal data found during the verification process, and generate an abnormal report; Output module: The input end is connected to the output end of the associated module and is used to display the verified data in a visual form.

[0066] By adopting the above technical solution, first, after the acquisition system is started, the screen data acquisition module will monitor and collect the refueling data displayed on the gas station screen in real time. These data cover the operation data of the refueling process, such as the amount of refueling, refueling time, and the number of the refueling gun.

[0067] The collected data will be transmitted to two processing modules: the first processing module and the second processing module; the first processing module pre-processes the refueling data and extracts key data fields, such as refueling quantity, refueling time, etc., which are recorded as the first data; the second processing module pre-processes the operation data of a single refueling process and extracts data changes within the interval T time, such as changes in refueling speed, changes in the state of the refueling gun, etc., which are recorded as the second data.

[0068] The standard data determination module determines the normal refueling data range or standard refueling data based on historical data or preset rules; the similarity calculation module calculates the similarity between the second data and the standard refueling data; and the first judgment module determines whether the similarity is greater than or equal to a set threshold based on the similarity calculation result.

[0069] If the similarity is greater than or equal to the set threshold, the association module will store the second data as a label of the first data and establish an association relationship between them; if the similarity is less than the set threshold, the verification module will intervene to obtain environmental data (such as camera monitoring data, fuel dispenser status data, etc.), and determine whether there is refueling behavior and intervention behavior to verify the authenticity of the data; at this time, the system will also perform manual verification to ensure the accuracy of the data. If the manual verification is correct, the verification module will store the environmental data as additional data and associate it with the first data for storage; the abnormal alarm module will establish an abnormal detection model, issue a real-time alarm for abnormal data found during the verification process, and generate an abnormal report; the output module is responsible for displaying the verified data in a visual form for management personnel to view and analyze.

[0070] Through steps such as data preprocessing, similarity calculation and manual verification, the system can screen out abnormal data to ensure that the stored data is more accurate and reliable; the process of automated data collection and processing greatly reduces the need for manual intervention and improves data processing efficiency; the output module displays the verified data in a visual form, allowing managers to intuitively understand the operation of the gas station and promptly discover and solve problems; the abnormal alarm module can monitor and alarm abnormal data in real time, helping managers to quickly respond to and deal with potential problems and ensure the safe operation of the gas station; by establishing correlations between data, the system can provide more comprehensive data analysis support to help managers better understand the operation of the gas station.

[0071] In summary, the present application includes at least one of the following beneficial technical effects: 1. The reasons for data differences analyzed in the first label acquisition step provide rich background information for data authenticity verification; this information helps managers understand whether the differences between the data are caused by normal factors (such as equipment failure, environmental factors, etc.) or abnormal behaviors such as data falsification; through the first labeling step, the first label is associated with the first data, so that relevant data and events can be located more quickly during the data authenticity verification process; this helps to speed up the verification speed and improve the verification efficiency; the introduction of the first label makes the data authenticity verification process more rigorous and reliable; by comprehensively considering the reasons for data differences, staff information and the associated information of the first label, managers can more accurately judge the authenticity of the data, thereby avoiding misjudgment or omission; 2. Improve overall performance by reducing the bandwidth and storage space required for data transmission and improving query performance. This is especially important in scenarios that require processing large amounts of data, such as big data analysis and cloud computing. By combining verification steps with compression technology, the system can detect and handle data errors in a timely manner, avoiding system crashes or performance degradation caused by data errors, thereby enhancing system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 is a flow chart of the collection and analysis method in Example 1 of the present application; Figure 2 It is a flowchart of the sub-steps of the label adding step in Example 2 of the present application; Figure 3 It is a flowchart of the sub-steps of the secure transmission step in Example 2 of the present application. DETAILED DESCRIPTION

[0073] The following combination Figures 1 to 3 This application is described in further detail.

[0074] This embodiment discloses a method for intelligent collection and analysis of multivariate screen data.

[0075] Example 1: Reference Figure 1 ,Intelligent collection and analysis method of multi-dimensional screen data ,Screen data collection: real-time monitoring and collection of ,the refueling data displayed on the screen of the gas station to obtain the ,operation data of the refueling process; Specifically, through specific screen data collection tools or software, real-time monitoring and collection of refueling data displayed on the screen of the gas station, as well as operation data during the refueling process. These data may include refueling amount, refueling time, refueling gun number, refueling speed, refueling gun status, etc.; First processing: pre-processing the collected refueling data, extracting key data fields, and recording them as first data; Specifically, the collected refueling data is preprocessed using image processing technology and natural language processing technology, including data cleaning and format conversion, to extract key data fields, such as refueling amount, refueling time, etc., which are recorded as the first data; for example, from the collected data, key fields such as the refueling amount (such as 50 liters) and refueling time (such as 08:30:00 to 08:35:00 on December 27, 2024) of each refueling event are extracted as the first data.

[0076] Second processing: pre-processing the collected single refueling process operation data, extracting the data changes within the interval T time, and recording them as the second data; Specifically, the collected single refueling process operation data is preprocessed to extract data changes within an interval T, such as changes in refueling speed, changes in the state of the fuel gun, etc., and record them as the second data; for example, in a refueling process, the changes in refueling speed every 5 minutes (T=300 seconds) and the changes in the state of the fuel gun (such as from idle to in use, and then to idle) are extracted as the second data.

[0077] Standard data determination: Determine standard refueling data based on normal data; Specifically, based on historical data or preset rules, determine the normal refueling data range or standard refueling data; these standard data may include the normal range of refueling volume, the normal range of refueling time, etc. In addition to directly determining the normal range of refueling volume and refueling time based on historical data, we can also calculate the normal refueling time for a specified refueling volume based on the refueling speed of the fuel gun. This usually involves understanding the performance of the fuel gun and considering the variables in the actual refueling process; for example, assume that the average refueling speed of a gas station's fuel gun is 4 liters per minute (this is an assumed value, and the actual situation may vary depending on factors such as the fuel gun model, oil type, oil temperature, etc.). Now, we need to calculate the normal refueling time required to add 50 liters of oil.

[0078] Calculate the refueling time: refueling volume = 50 liters; refueling speed = 4 liters / minute; therefore, normal refueling time = refueling volume / refueling speed = 50 liters / 4 liters / minute = 12.5 minutes; In this example, we can use 12.5 minutes as a reference range for the normal refueling time required to add 50 liters of oil. Of course, in actual applications, due to various factors (such as oil gun aging, oil viscosity changes, refueling pipe pressure fluctuations, etc.), the actual refueling time may deviate. Therefore, the determination of standard data also needs to take these actual factors into consideration, and may require regular adjustments and calibrations.

[0079] In addition, it is worth noting that different fuel dispensers or fuel guns may have different refueling speeds, so in actual applications, standard data needs to be determined based on the specific fuel dispensers and fuel guns. At the same time, in order to ensure the accuracy and reliability of the refueling data, the fuel dispensers and fuel guns need to be regularly maintained and calibrated.

[0080] Similarity calculation: calculating the similarity between the second data and the standard refueling data; Specifically, a specific similarity calculation algorithm (such as cosine similarity, Euclidean distance, etc.) is used to calculate the similarity between the second data and the standard refueling data; for example, the present embodiment preferably uses a cosine similarity algorithm to calculate the similarity between the extracted second data and the standard refueling data to obtain a similarity value (such as 0.85).

[0081] First judgment: judge whether the similarity is greater than or equal to the set threshold. If so, execute the association step; otherwise, execute the verification step; Specifically, the calculated similarity is compared with the set threshold to determine whether the similarity is greater than or equal to the set threshold; for example, the similarity threshold is set to 0.8, and if the calculated similarity value is 0.85, it is determined that the similarity is greater than or equal to the set threshold; the value of the set threshold can be determined based on the ROC curve (receiver operating characteristic curve) and the AUC value (area under the curve), or obtained based on historical experience; by analyzing the ROC curve, the accuracy and recall rates under different thresholds can be found, thereby selecting a suitable threshold.

[0082] Association: Store the second data as a label of the first data, establish an association relationship, and then perform the output step; Specifically, if the similarity is greater than or equal to the set threshold, the second data is stored as a label of the first data, and an association relationship is established between them; for example, the extracted second data (such as changes in refueling speed, changes in the state of the fuel gun) is stored as a label of the first data (such as refueling amount, refueling time), and an association relationship is established between them for subsequent data analysis and visualization; Verification: Obtain environmental data, determine whether there is refueling and intervention behavior, verify authenticity, and perform manual verification. If the manual verification is correct, store the environmental data as additional data, associate it with the first data for storage, and then execute the output step; otherwise, execute the abnormal alarm step; Specifically, if the similarity is less than the set threshold, the environmental data (such as camera monitoring data, fuel dispenser status data, etc.) is obtained, and it is determined whether there is any refueling behavior and intervention behavior to verify the authenticity of the data. Then a manual verification is performed. If the manual verification is correct, the environmental data is stored as additional data and associated with the first data for storage; for example, if the similarity is less than the set threshold (such as 0.7), the camera monitoring data of the gas station is obtained to check whether there is any abnormal behavior during the refueling process; at the same time, the status data of the fuel dispenser is checked to verify the authenticity of the data. If the data is confirmed to be correct after manual verification, the camera monitoring data and the fuel dispenser status data are used as additional data and associated with the first data for storage.

[0083] Abnormal alarm: establish an abnormal detection model, issue real-time alarms for abnormal data found during the verification process, and generate abnormal reports; Output: Display the verified data in a visual form.

[0084] Specifically, if the manual verification is incorrect or the data is abnormal, an anomaly detection model is established to issue a real-time alarm for the abnormal data found during the verification process, and generate an abnormal report; for example, if the data is abnormal after manual verification (such as abnormally high or low refueling volume), the abnormal alarm mechanism is triggered, an abnormal report is generated, and relevant personnel are notified in real time to handle it; the verified data is displayed in a visual form, such as generating bar graphs, line graphs, pie charts and other charts, so that managers can intuitively understand the operation of the gas station; for example, the verified refueling data, environmental data, etc. are displayed in the form of bar graphs, line graphs or data reports, and managers can intuitively understand the changes in key indicators such as the gas station's refueling volume, refueling time, and refueling speed through charts.

[0085] Example 1 of the present application monitors and collects the refueling data displayed on the gas station screen and the operation data during the refueling process in real time, and performs preprocessing, similarity calculation, association, verification and abnormal alarm steps, combines the gas station screen data and external data sources, and cross-validates the extracted key data to ensure the accuracy and consistency of the data; verifies the authenticity of the first data, and adds association after verification to facilitate the verification of the authenticity of the data, while increasing the difficulty of fraud, thereby realizing intelligent monitoring and analysis of the operation of the gas station.

[0086] Example 2: Reference Figure 2 , the difference between this embodiment and embodiment 1 is that a label adding step is provided between the associating step and the outputting step; Label adding: including a first label acquisition step, a first labeling step, a second label acquisition step, a second judgment step, a second labeling step, a third labeling step and a manual verification step; First label acquisition: obtaining the reason for the difference between the second data and the standard refueling data, forming a first label according to the reason for the difference and the staff information, and executing the first labeling step; First labeling: associating the first label with the first data, and then performing a second label acquisition step; Second label acquisition: obtaining whether the refueling user is an old user, if yes, executing the second judgment step, otherwise, executing the second labeling step; Second judgment: judging whether the refueling habits of old users are close to historical habits, if so, executing the third labeling step, otherwise, executing the manual verification step; Second labeling: associating the new user label with the first data and performing an output step; Third labeling: associating the old user label with the first data and the historical refueling data, and then performing the output step; Manual verification: Obtain the refueling video and verify whether there is any refueling behavior. If so, execute the output step; otherwise, execute the abnormal alarm step.

[0087] Specifically, after the association step, the system first analyzes the differences between the second data and the standard refueling data; these differences may be due to a variety of reasons, such as oil gun failure, fuel operator error, oil quality problems, etc.; the system automatically or manually generates a first label based on these differences and combined with the information of staff (such as fuel operators, maintenance personnel, etc.); for example, suppose that during a refueling process, the second data shows that the refueling speed is abnormally fast, while the standard refueling data shows that the normal refueling speed of the fuel gun should be slower. After analysis, the system determines that the cause of the difference is "fuel gun failure" and generates the first label "fuel gun failure-fuel operator A". Here, "fuel operator A" is the staff member responsible for the refueling.

[0088] The system associates the first tag with the first data (i.e., key refueling data) for subsequent analysis and tracking. This association is usually achieved by establishing a foreign key or index in the database; for example, the first tag "Fuel gun failure-refueler A" generated in the first tag acquisition step is associated with the first data "refueling volume 50 liters, refueling time 3 minutes". In this way, when the refueling event needs to be queried or analyzed later, the tags and data related to it can be easily found.

[0089] The system determines whether the refueling user is an old user by checking the user's identity information (such as membership number, mobile phone number, etc.). If the user information already exists in the database, it is considered an old user; otherwise, it is considered a new user; for example, if the refueling user provides the membership number "123456", the system queries the database and finds that the membership number already exists, so it determines that the user is an old user, or by identifying the user's license plate number, it determines whether the same license plate number has refueled at the gas station or a nearby gas station. If so, it is an old user.

[0090] For old users, the system analyzes whether their refueling habits are similar to their historical habits; this usually involves comparing multiple aspects of the current refueling data with the historical refueling data, such as the amount of refueling, refueling time, and refueling frequency. For example, suppose the historical refueling habit of the old user "123456" is to refuel about 30 liters each time, and the amount of refueling this time is 50 liters. After analysis, the system determines that the user's refueling habits are significantly different from the historical habits.

[0091] If the refueling habits of the old user are close to the historical habits, the system generates an old user tag and associates it with the first data; at the same time, the system also associates the user's historical refueling data for more in-depth analysis and comparison; for example, if the refueling user is a new user, the system generates a new user tag and associates it with the first data; assuming that the refueling habits of the old user "123456" are very close to the historical habits (such as the refueling amount and refueling time are almost the same), the system generates an old user tag "old user-123456" and associates it with the first data. At the same time, the system also associates the user's historical refueling data; assuming that the refueling user is a new user, the system generates a new user tag "new user-ABC123" (where "ABC123" is the user's temporary membership number or mobile phone number and other identity information), and associates it with the first data.

[0092] When the system determines that the refueling habits of old users are significantly different from historical habits or there are other abnormal situations, the manual verification step will be triggered. The system obtains additional information such as refueling videos (such as camera surveillance videos) and notifies staff to verify. Staff members judge whether there is real refueling behavior by viewing videos and other information; for example, suppose the system determines that the refueling habits of old user "123456" are significantly different from historical habits, so the manual verification step is triggered; the staff member checks the camera surveillance video of the gas station and finds that the user did refuel, but the amount of refueling may be large due to special reasons (such as a large vehicle tank or the need to travel long distances, etc.). Therefore, the staff member confirms that the refueling behavior is real.

[0093] After all the above steps, the system will finally display the verified data in a visual form to the management personnel or relevant staff. If there are abnormalities in the data or situations that require special attention (such as untrue refueling behavior, oil gun failure, etc.), the system will trigger the abnormal alarm mechanism and generate a corresponding abnormal report; for example, assume that after all steps are completed, the system confirms that the refueling data is true and no abnormalities occur. Therefore, the system will display the verified data to the management personnel in the form of a bar chart or a line chart. The management personnel can intuitively see the operation of the gas station, such as the changing trends of key indicators such as refueling volume and refueling time. If there are abnormalities in the data (such as abnormally high or low refueling volume, abnormally long or short refueling time, etc.), the system will trigger the abnormal alarm mechanism and generate an abnormal report with detailed information for the reference of the management personnel.

[0094] Reference Figure 3 In other embodiments, before the output step, a secure transmission step is also provided; Secure transmission: including data storage step, segmentation step, data integration step, encryption step, data calculation step, data verification step and verification and restoration step; Data storage: storing the first data and the second data within the time period t as a first data packet, calculating a hash value of the first data, and dividing the hash value into n parts, each of which is recorded as third data; Specifically, first, the system needs to organize the first data (such as the gas station's refueling records, transaction information, etc.) and the second data (such as the gas station's operation log, equipment status, etc.) within the time period t, and package them into a first data packet; in order to ensure data integrity, the system calculates the hash value of the first data, and divides the hash value into n parts (the value of n is usually an integer greater than 1 and less than the number of characters in the first data), and each part is recorded as the third data; for example, assuming that the time period t is one day, the first data is the refueling record of a gas station, including information such as the amount of refueling and the time of refueling; the second data is the operation log of the gas station; the system organizes these data into a first data packet and calculates the hash value of the first data; assuming that the hash value is "abcd1234efgh5678", the system divides it into 4 parts, that is, the third data is "abcd", "1234", "efgh" and "5678".

[0095] Segmentation: segment the first data into n parts, and record each part as fourth data, where the value of n is greater than 1 and less than the number of characters in the first data; Specifically, the system then divides the first data into n parts, each of which is recorded as the fourth data; the n here should be the same as the n in the data storage step to ensure the smooth progress of subsequent data integration; continuing the above example, assuming that the first data is a long text, the system divides it into 4 parts, that is, the fourth data is paragraphs 1-1 / 4, 1 / 4-2 / 4, 2 / 4-3 / 4 and 3 / 4-1 of the text (this is only for illustration, the actual segmentation method may be different).

[0096] Data integration: shuffle the third data and the fourth data at the same position, record them as fifth data, form an index array according to the second data, establish an association between the index array and the fifth data, and store them as a second data packet; Specifically, the system scrambles the third data and the fourth data at the same position, and the scrambling is implemented by a random algorithm or a pseudo-random algorithm to form a new data block, which is recorded as the fifth data. At the same time, an index array is formed according to the second data, and an association is established between the index array and the fifth data. Finally, these data are packaged into a second data packet; continuing with the above example, the system may scramble the third data "abcd" and the first part of the fourth data (i.e., 1-1 / 4 paragraphs of the text) to form a part of the fifth data. Similarly, the other parts of the third data and the corresponding parts of the fourth data are processed. Then, the system forms an index array according to the second data (such as the order of entries in the operation log), and establishes an association between the index array and the fifth data.

[0097] Encryption: using an encryption algorithm to encrypt the data in the second data packet, using the encrypted second data packet as output data for data transmission, and performing a data calculation step before performing an output step; Specifically, the system uses an encryption algorithm (such as AES, RSA, etc.) to encrypt the data in the second data packet, and the encrypted second data packet is used as output data for data transmission.

[0098] Data calculation: Calculate the hash value of the index array, recorded as the sixth data; Specifically, before executing the output step, the system calculates the hash value of the index array, recorded as the sixth data, and this hash value is used in the subsequent data verification step.

[0099] Specifically, after the data is transmitted and received, the system verifies whether the hash value in the sixth data has changed. If the hash value has not changed, the verification and restoration step is performed; if the hash value has changed, the abnormal alarm step is performed.

[0100] Data verification: verify whether the hash value in the sixth data has changed. If not, execute the verification and restoration step; otherwise, execute the abnormal alarm step; Verify and restore: restore the third data and the fourth data according to the index array, and verify whether the fourth data has changed. If not, restore the data to the first data and the second data, and then execute the output step.

[0101] Reference Figure 3 , in other implementations, after the abnormality alarm step, a data recovery step is set; Data recovery: Create a data backup for the first data packet and associate it with the data of the computer terminal. After executing the abnormal alarm step, trigger the data recovery mechanism, restore the first data packet from the data backup according to the data of the computer terminal, and then execute the output step.

[0102] Specifically, after the data storage step, the system should immediately create a data backup for the first data packet; this backup can be stored in a local hard disk, a network storage device, or a cloud storage to ensure its security and accessibility; the data backup should be associated with the data of the computer terminal (such as a management console, server, etc.) so that the backup data can be quickly located and accessed when needed; after executing the abnormal alarm step, the system should automatically trigger the data recovery mechanism. This can be achieved by setting up a monitoring program or script, and when an abnormal alarm signal is detected, the data recovery process is immediately started; the system recovers the first data packet from the data backup based on the data of the computer terminal (such as the storage location of the backup data, the file name, and other information); during the recovery process, the system should ensure the integrity and accuracy of the data to avoid data damage or loss; after the data recovery is completed, the system should verify the recovered data to ensure that it is consistent with the original data; if the verification passes, the output step is executed to display the recovered data in a visual form to the management personnel or relevant staff.

[0103] In other embodiments, in the data integration step, a time series is extracted based on the first data and a timestamp is formed, and then an index array is formed based on the second data and the timestamp.

[0104] Specifically, the system first identifies time series information in the first data. Time series information generally refers to data points arranged in chronological order, such as date, time, etc.

[0105] Based on the extracted time series information, the system generates a unique timestamp for each data point. A timestamp is usually a string or number that uniquely identifies the time position of a data point.

[0106] The system then associates the second data with the first data (or more precisely, with the timestamp of the first data). This typically involves matching each entry in the second data with the corresponding timestamp in the first data.

[0107] Based on this association, the system constructs an index array. Each element of the index array contains a reference to a specific entry in the second data (such as an index number or pointer to the entry) and a timestamp associated with it. In this way, the index array provides a method for quickly finding a specific entry in the second data and its corresponding timestamp.

[0108] For example, suppose there is a gas station management system, where the first data is the gas station's refueling record, and the second data is the gas station's employee shift handover record. The following is a specific implementation process of the data integration step refinement: Time series extraction and timestamp formation: The system extracts the date and time information of each refueling from the refueling record (for example, "2023-04-01 08:30:00"); for each refueling record, the system generates a unique timestamp (for example, using the UNIX timestamp format, or simply using the date and time string as the timestamp).

[0109] Index array formation: The system associates the employee shift handover record with the timestamp of the refueling record. For example, if a shift handover record shows that an employee started work at "2023-04-01 08:00:00" and finished work at "2023-04-01 16:00:00", the system will associate all refueling records in that time period with the employee's shift handover record. Based on this association, the system builds an index array. For example, the index array may contain the following elements: Element 1: {timestamp: "2023-04-01 08:30:00", employee index: "Zhang San"}; Element 2: {timestamp: "2023-04-01 09:15:00", employee index: "Zhang San"}; ... (other refueling records); Element N: {timestamp: "2023-04-01 15:45:00", employee index: "Li Si"} (assuming that Zhang San handed over the shift to Li Si before 16:00); In this way, the index array provides a fast search method for refueling records sorted by employee and time.

[0110] In other embodiments, in the manual verification step, if the refueling video shows that there is no refueling behavior, an abnormal alarm step is triggered, and an abnormal report including a video screenshot is generated to facilitate verification and processing by management personnel.

[0111] In other embodiments, before the encryption step, a data compression step and a verification step are also included; Data compression: compressing the data in the second data packet to reduce the bandwidth and storage space required for data transmission, and then using the compressed data as the data to be encrypted; Verification: Perform integrity check on the compressed data as the data to be encrypted, generate a verification code, and perform the encryption step together with the verification code and the data to be encrypted.

[0112] Specifically, choose a compression algorithm: Choose a suitable compression algorithm based on the type and characteristics of the data. Common compression algorithms include RLF (compression for continuous repeated information), Huffman algorithm (dictionary-based compression), LZ77 (improved Huffman algorithm, using a sliding dictionary), etc. For image data, lossy compression (such as JPEG) or lossless compression (such as PNG) algorithms can also be used.

[0113] Perform compression processing: Apply the selected compression algorithm to the data in the second data packet. This typically involves identifying and eliminating redundant information in the data, such as repeated data blocks, unnecessary metadata, etc.

[0114] Generate compressed data: After the compression algorithm is processed, compressed data is generated. This data is usually smaller than the original data, so it can reduce the bandwidth and storage space required for transmission.

[0115] For example, suppose there is a refueling record in the gas station management system as follows: Refueling time: 2023-12-25 10:00:00; Refueling volume: 50 liters; Refueling card number: 1234567890; Refueling gun number: 001; If there are multiple refueling record data, and some fields (such as refueling card number, refueling gun number) are repeated, the RLF compression algorithm can be used for compression; for example, if multiple records use the same refueling card number and refueling gun number, these repeated information can be compressed into the form of "number of repetitions, field value". The compressed data may be similar to: [1, "2023-12-25 10:00:00", 50, "1234567890", "001"] [2, "2023-12-25 10:15:00", 60, "", ""] / / The empty string here means repeating the previous gas card number and gas gun number; Of course, this is just a simple example; in actual applications, the compression algorithm may be more complicated and need to consider more data characteristics and compression efficiency.

[0116] Verification step (taking the compressed data of gas station refueling records as an example): In the gas station management system, after the refueling record data is compressed, a verification step is required to ensure the integrity of the data.

[0117] Implementation method: Select the verification algorithm: Select the appropriate verification algorithm according to the sensitivity and importance of the refueling record data. Common verification algorithms include CRC (Cyclic Redundancy Check), MD5, SHA, etc. These algorithms can generate a fixed-length verification code to uniquely identify the integrity of the data.

[0118] Generate checksum: Apply the selected checksum algorithm to the compressed fueling record data. This typically involves splitting the data into smaller chunks (if not previously split) and performing a checksum calculation on each chunk. The calculated checksum can be appended to the corresponding chunk or stored in a separate checksum file.

[0119] Verify data integrity: During data transmission or storage, the receiver can use the same verification algorithm to calculate the verification code of the received data and compare it with the verification code provided by the sender. If the two are consistent, it means that the data has not been damaged or tampered with during transmission or storage; if they are inconsistent, it means that there may be problems with the data and further processing or verification is required.

[0120] For example, continuing with the above example, suppose we use the CRC-32 cyclic redundancy check algorithm to generate a checksum. For compressed refueling record data (such as the compressed data block in the above example), the CRC-32 algorithm will generate a 32-bit checksum. Append this checksum to the compressed data to form a complete data packet to be encrypted (although the encryption step is not mentioned here, the checksum is usually attached to the data before encryption). For example: [1,"2023-12-25-10:00:00",50,"1234567890","001"].CRC32_Checksum("0xABCDEF12"); [2,"2023-12-25-10:15:00",60,"",""].CRC32_Checksum("0x34567890"); Here, "CRC32_Checksum" indicates the checksum generated by the CRC-32 algorithm, while "0xABCDEF12" and "0x34567890" are assumed checksum values ​​(the actual calculation results will be different). During data transmission or storage, the receiver can use the CRC-32 algorithm to calculate the checksum of the received data and compare it with these assumed checksums to verify the integrity of the data.

[0121] Performing integrity checks on compressed data before encryption can ensure that the data has not been damaged or tampered with during transmission or storage, which is critical to the accuracy and reliability of gas station refueling record data.

[0122] Performing the encryption step together with the checksum and the data to be encrypted can further enhance the security of the data. Even if the encrypted data is intercepted during transmission, hackers cannot infer the content of the original data through the checksum, thus protecting the confidentiality of the data.

[0123] During the decryption process, if the checksum does not match the decrypted data, it means that an error may have occurred during the data transmission or storage process. At this time, the location and type of the error can be detected based on the checksum, and corresponding measures can be taken to recover or retransmit to ensure the accuracy and reliability of the data.

[0124] The embodiment of the present application also discloses a collection and analysis system.

[0125] Reference Figure 2 , the acquisition and analysis system includes the following modules: Screen data acquisition module: used to monitor and collect refueling data displayed on the gas station screen in real time, and obtain refueling process operation data; Specifically, by using image recognition technology (such as OCR) or a camera or a camera, the refueling data displayed on the screen is captured; the screen changes are monitored in real time to ensure the real-time and accuracy of the data; the collected data is preliminarily cleaned to remove irrelevant information.

[0126] The first processing module: the input end is connected to the output end of the screen data acquisition module, and is used to pre-process the collected refueling data and extract key data fields, which are recorded as first data; The second processing module: the input end is connected to the output end of the screen data acquisition module, and is used to pre-process the collected single refueling process operation data, extract the data changes within the interval T time, and record them as the second data; Standard data determination module: determines standard refueling data based on normal data; Specifically, standard refueling data is determined based on historical refueling data or industry standards; standard data should include key parameters and thresholds in the normal refueling process; standard data is regularly updated and maintained to adapt to changes in gas station operations.

[0127] Similarity calculation module: the input end is connected to the output end of the second processing module and the output end of the standard data determination module, and is used to calculate the similarity between the second data and the standard refueling data; The first judgment module: the input end is connected to the output end of the similarity calculation module, and is used to judge whether the similarity is greater than or equal to a set threshold; Association module: an input end is connected to the output end of the first judgment module and the output end of the first processing module, and is used to store the second data as a label of the first data and establish an association relationship after the similarity is greater than or equal to a set threshold; Label adding module: the input end is connected to the output end of the association module, and is used to add a label to the first data; Verification module: The input end is connected to the output end of the first judgment module, and is used to obtain environmental data after the similarity is less than the set threshold, and determine whether there is refueling behavior and intervention behavior, verify the authenticity, and perform manual verification. If the manual verification is correct, the environmental data is stored as additional data and associated with the first data for storage; Abnormal alarm module: The output end connection of the input verification module is used to establish an abnormal detection model, issue a real-time alarm for abnormal data found during the verification process, and generate an abnormal report; Secure transmission module: The input end is connected to the output end of the tag adding module and is used to store, segment, integrate, encrypt, compress, check and verify data to reduce loss or tampering during data transmission.

[0128] Output module: The input end is connected to the output end of the secure transmission module and is used to display the verified data in a visual form.

[0129] Data recovery module: The input end is connected to the output end of the abnormal alarm module, which is used to trigger the recovery mechanism and restore the modified data based on the data backup.

[0130] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for intelligent collection and analysis of multivariate screen data, characterized in that: The following steps are involved: Screen data collection: real-time monitoring and collection of refueling data displayed on the gas station screen, and acquisition of refueling process operation data; First processing: pre-processing the collected refueling data, extracting key data fields, and recording them as first data; Second processing: pre-processing the collected single refueling process operation data, extracting the data changes within the interval T time, and recording them as the second data; Standard data determination: Determine standard refueling data based on normal data; Similarity calculation: calculating the similarity between the second data and the standard refueling data; First judgment: judge whether the similarity is greater than or equal to the set threshold, if so, execute the association step; Otherwise, the verification step is performed; Association: Store the second data as a label of the first data, establish an association relationship, and then perform the output step; Verification: Obtain environmental data, determine whether there is refueling and intervention behavior, verify authenticity, and perform manual verification. If the manual verification is correct, store the environmental data as additional data, associate it with the first data for storage, and then execute the output step; otherwise, execute the abnormal alarm step; Abnormal alarm: establish an abnormal detection model, issue real-time alarms for abnormal data found during the verification process, and generate abnormal reports; Output: Display the verified data in a visual form.

2. The method for intelligent collection and analysis of screen multivariate data according to claim 1, characterized in that: Between the association step and the output step, a label adding step is also provided; Label adding: including the first label acquisition step and the first labeling step; First label acquisition: obtaining the reason for the difference between the second data and the standard refueling data, forming a first label according to the reason for the difference and the staff information, and executing the first labeling step; First labeling: Associating a first label with the first data, and then performing an output step.

3. The method for intelligent collection and analysis of screen multivariate data according to claim 2, characterized in that: A second label acquisition step, a second judgment step, a second labeling step, a third labeling step and a manual verification step are also provided between the first labeling step and the output step; Second label acquisition: obtaining whether the refueling user is an old user, if yes, executing the second judgment step, otherwise, executing the second labeling step; Second judgment: judging whether the refueling habits of old users are close to historical habits, if so, executing the third labeling step, otherwise, executing the manual verification step; Second labeling: associating the new user label with the first data and performing an output step; Third labeling: associating the old user label with the first data and the historical refueling data, and then performing the output step; Manual verification: Obtain the refueling video and verify whether there is any refueling behavior. If so, execute the output step; otherwise, execute the abnormal alarm step.

4. The method for intelligent collection and analysis of screen multivariate data according to any one of claims 1 to 3, characterized in that: Before the output step, a secure transmission step is also provided; Secure transmission: including data storage step, segmentation step, data integration step, encryption step and verification and restoration step; Data storage: storing the first data and the second data within the time period t as a first data packet, calculating a hash value of the first data, and dividing the hash value into n parts, each of which is recorded as third data; Segmentation: segment the first data into n parts, and record each part as fourth data, where the value of n is greater than 1 and less than the number of characters in the first data; Data integration: shuffle the third data and the fourth data at the same position, record them as fifth data, form an index array according to the second data, establish an association between the index array and the fifth data, and store them as a second data packet; Encryption: using an encryption algorithm to encrypt the data in the second data packet, using the encrypted second data packet as output data for data transmission, and performing a verification and restoration step before performing the output step; Verify and restore: restore the third data and the fourth data according to the index array, and verify whether the fourth data has changed. If not, restore the data to the first data and the second data, and then execute the output step.

5. The method for intelligent collection and analysis of screen multivariate data according to claim 4, characterized in that: Before the verification and restoration step, array calculation and data verification steps are also set; Data calculation: Calculate the hash value of the index array, recorded as the sixth data; Data verification: verify whether the hash value in the sixth data has changed, if not, perform the verification and restoration step; Otherwise, execute the abnormal alarm step.

6. The method for intelligent collection and analysis of screen multivariate data according to claim 4, characterized in that: After the abnormal alarm step, set the data recovery step; Data recovery: Create a data backup for the first data packet and associate it with the data of the computer terminal. After executing the abnormal alarm step, trigger the data recovery mechanism, restore the first data packet from the data backup according to the data of the computer terminal, and then execute the output step.

7. The method for intelligent collection and analysis of screen multivariate data according to claim 4, characterized in that: In the data integration step, the scrambling process is implemented using a random algorithm or a pseudo-random algorithm.

8. The method for intelligent collection and analysis of screen multivariate data according to claim 4, characterized in that: In the data integration step, a time series is extracted according to the first data and a timestamp is formed, and then an index array is formed according to the second data and the timestamp.

9. The method for intelligent collection and analysis of screen multivariate data according to claim 4, characterized in that: In the manual verification step, if the refueling video shows that there is no refueling behavior, the abnormal alarm step is triggered and an abnormal report including video screenshots is generated to facilitate verification and processing by management personnel.

10. A collection and analysis system for implementing the screen multivariate data intelligent collection and analysis method according to any one of claims 1 to 9, characterized in that: Includes the following modules: Screen data acquisition module: used to monitor and collect refueling data displayed on the gas station screen in real time, and obtain refueling process operation data; The first processing module: the input end is connected to the output end of the screen data acquisition module, and is used to pre-process the collected refueling data and extract key data fields, which are recorded as first data; The second processing module: the input end is connected to the output end of the screen data acquisition module, and is used to pre-process the collected single refueling process operation data, extract the data changes within the interval T time, and record them as the second data; Standard data determination module: determines standard refueling data based on normal data; Similarity calculation module: the input end is connected to the output end of the second processing module and the output end of the standard data determination module, and is used to calculate the similarity between the second data and the standard refueling data; The first judgment module: the input end is connected to the output end of the similarity calculation module, and is used to judge whether the similarity is greater than or equal to a set threshold; Association module: an input end is connected to the output end of the first judgment module and the output end of the first processing module, and is used to store the second data as a label of the first data and establish an association relationship after the similarity is greater than or equal to a set threshold; Verification module: The input end is connected to the output end of the first judgment module, and is used to obtain environmental data after the similarity is less than the set threshold, and determine whether there is refueling behavior and intervention behavior, verify the authenticity, and perform manual verification. If the manual verification is correct, the environmental data is stored as additional data and associated with the first data for storage; Abnormal alarm module: The output end connection of the input verification module is used to establish an abnormal detection model, issue a real-time alarm for abnormal data found during the verification process, and generate an abnormal report; Output module: The input end is connected to the output end of the associated module and is used to display the verified data in a visual form.

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