A method, device and medium for identifying a printing ticket problem of an environmental monitoring device

Abstract

The application relates to an environment monitoring device printing slip problem identification method, device and medium, and belongs to the technical field of environment monitoring. The application obtains a digital image of a printing slip of an environment monitoring device; feature extraction is performed on the digital image to obtain to-be-checked data; a pre-established knowledge base and an algorithm base are called; the knowledge base stores standard data related to environment monitoring, and the algorithm base comprises a plurality of calculation methods and logical checking methods for checking and identifying problems of the printing slip data; a convolutional neural network is used to perform fitting calculation on the to-be-checked data, the obtained calculation result is compared with standard data in the knowledge base, a checking result of whether the printing slip of the environment monitoring device is abnormal is generated based on the comparison result, and the checking result is fed back to a user. The application realizes automatic checking of problems in a manually monitored printing slip, and improves the problem identification capability and efficiency of monitoring data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of environmental monitoring, and particularly relates to a method for identifying problems in printed slips of an environmental monitoring device, a device and a medium. BACKGROUND

[0002] In previous inventions in the professional field, it is common to implant mobile terminals through programs to identify general text abnormalities. This method can be roughly applied to conventional parameters in some key industries such as steel and coking. However, it lacks effective problem identification capability for data text in complex processes and other different industries. On the other hand, the judgment of different formats and professional terms is usually slow in recognition efficiency and poor in accuracy.

[0003] Therefore, how to finely, effectively and accurately identify the logical distinction brought by different industries, and adapt to complex scenarios such as multiple processes, multiple parameters and multiple samples is a research topic worth studying. SUMMARY

[0004] In view of the above analysis, the embodiments of the present application aim to provide a method for identifying problems in printed slips of an environmental monitoring device, a device and a medium, to automatically check problems in manually monitored printed slips, and to improve the problem identification capability and efficiency of monitoring data.

[0005] In a first aspect, the application provides a method for identifying problems in printed slips of an environmental monitoring device, comprising:

[0006] obtaining a digital image of a printed slip of an environmental monitoring device;

[0007] extracting features from the digital image to obtain data to be checked;

[0008] calling a pre-established knowledge base and an algorithm library; the knowledge base stores standard data related to environmental monitoring, including printed slip format requirements, basic information judgment rules, a calculation model group composed of industry specifications, and theoretical thresholds based on industry characteristics; the algorithm library includes a plurality of calculation methods and logical checking methods for checking and identifying problems in printed slip data;

[0009] Based on the calculation methods and logical checking methods in the algorithm library, the convolutional neural network is used to fit and calculate the data to be checked. The calculation results are compared with the standard data in the knowledge base, and a verification result of whether the printed slip of the environmental monitoring device has an abnormality is generated based on the comparison result;

[0010] The verification result is fed back to the user.

[0011] Optionally, the printed slip standard format requirements include text font, standard line spacing, and numbering rules.

[0012] The calculation method and logic checking method based on the algorithm library are used to fit and calculate the to-be-checked data by using a convolutional neural network, the obtained calculation result is compared with standard data in the knowledge base, and a verification result of whether the print slip of the environmental monitoring device exists an abnormality is generated based on the comparison result.

[0013] The print slip is checked for standard format and numbering problems by using a convolutional neural network. When there are problems such as a format that does not meet the standard format requirement, different number of digits, disordered numbering sequence, or the same numbering, it is determined that the print slip exists a counterfeiting problem, and a verification result of whether the print slip exists an abnormality is generated.

[0014] Optionally, the basic information judgment rule includes sampling point information, time rule, personnel arrangement rule, and signature authentication standard.

[0015] The calculation method and logic checking method based on the algorithm library are used to fit and calculate the to-be-checked data by using a convolutional neural network, the obtained calculation result is compared with standard data in the knowledge base, and a verification result of whether the print slip of the environmental monitoring device exists an abnormality is generated based on the comparison result.

[0016] The time and point data on each print slip are analyzed to check whether there are repeated time or sampling records in adjacent time. According to the operation personnel and time recorded on the print slip, a personnel arrangement table in the knowledge base is checked to detect whether there is a logic conflict. The signature on the print slip is checked to see whether it meets the preset effective standard, including the style, position of the signature, and whether it contains authentication elements. A verification result of whether the print slip of the environmental monitoring device exists an abnormality is generated based on the comparison result.

[0017] Optionally, the calculation method and logic checking method based on the algorithm library are used to fit and calculate the to-be-checked data by using a convolutional neural network, the obtained calculation result is compared with standard data in the knowledge base, and a verification result of whether the print slip of the environmental monitoring device exists an abnormality is generated based on the comparison result.

[0018] The flow rate is calculated according to a sampling flow rate calculation formula in the algorithm library. The flow rate calculation formula is as follows:

[0019]

[0020] wherein, V s represents the gas flow rate of wet exhaust, in m / s; V a represents the air flow rate of a ventilation duct at normal temperature and pressure, in m / s; B a represents atmospheric pressure, in Pa; K p represents a pitot tube correction coefficient; and P dP represents exhaust dynamic pressure, in Pa. s ρ represents the exhaust static pressure, in Pa. s This indicates the density of the wet exhaust gas, expressed in kg / m³. 3 M s The molecular weight of the wet exhaust gas is expressed in kg / kmol; t s The exhaust temperature is expressed in °C. The calculated flow rate is compared with the theoretical flow rate threshold in the knowledge base. If the flow rate exceeds the theoretical flow rate threshold, an anomaly is determined, and a verification result indicating an anomaly is generated on the small strip printed by the environmental monitoring equipment.

[0021] Based on the sampling nozzle diameter, sampling flow rate, and sampling duration, the sampling volume is calculated according to the sampling volume calculation formula in the algorithm library. The calculated sampling volume is then compared with the theoretical threshold of sampling volume in the knowledge base. If the volume exceeds the theoretical threshold, an anomaly is determined, and a verification result indicating an anomaly is generated for the small strip printed by the environmental monitoring equipment.

[0022] Obtain the pre-measurement pressure and determine whether the pre-measurement pressure exceeds the theoretical threshold. If it does, an anomaly is identified, and a verification result indicating an anomaly is generated and printed on the environmental monitoring equipment is generated.

[0023] Optionally, the step of extracting features from the digital image to obtain the data to be verified includes:

[0024] Optical character recognition technology is used to extract text data from the digital image, and the text data is parsed to identify key parameters to obtain the data to be verified; the data to be verified includes oxygen content, flow rate, smoke temperature and humidity;

[0025] The calculation method and logical checking method based on the algorithm library, using a convolutional neural network to fit and calculate the data to be checked, comparing the obtained calculation results with standard data in the knowledge base, and generating a verification result based on the comparison result to check whether there are any anomalies in the small strip printed by the environmental monitoring equipment, including:

[0026] Numerical analysis is performed on the data to be verified to calculate the mean, standard deviation and other statistical indicators of multiple samples at the same location; anomaly detection is performed on each parameter, and the parameter value of each sample is compared with the set threshold or mean; if the parameter value of a sample exceeds the set threshold or mean, it is determined that there is an anomaly, and a verification result indicating that there is an anomaly is generated and printed on the environmental monitoring equipment.

[0027] Optionally, the calculation method and logical checking method based on the algorithm library, using a convolutional neural network to fit and calculate the data to be checked, comparing the obtained calculation result with standard data in the knowledge base, and generating a verification result based on the comparison result to determine whether there is an anomaly in the printed strip of the environmental monitoring equipment, includes:

[0028] collecting sampling flow rate data of each sampling point from the printed slip of the environmental monitoring equipment;

[0029] statistically analyzing the collected sampling flow rate data to calculate the mean value, standard deviation and deviation rate;

[0030] comparing the calculated deviation rate with the deviation rate threshold set in the knowledge base to determine whether it meets the requirements of constant speed tracking sampling, and if not, determining that there is an anomaly and generating a verification result that the printed slip of the environmental monitoring equipment has an anomaly.

[0031] Optionally, before the digital image of the printed slip of the environmental monitoring equipment is obtained, the method further comprises:

[0032] obtaining information inputting the industry type, process and working condition to which the printed slip of the environmental monitoring equipment belongs;

[0033] loading corresponding data from the knowledge base and algorithm base based on the industry type, process and working condition to which the printed slip of the environmental monitoring equipment belongs.

[0034] Optionally, after the digital image of the printed slip of the environmental monitoring equipment is obtained, the method further comprises:

[0035] performing image preprocessing on the digital image, the image preprocessing comprising grayscale processing, binarization processing and noise removal processing on the digital image.

[0036] In a second aspect, the present application provides an environmental monitoring equipment printed slip problem identification device, comprising a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to realize the environmental monitoring equipment printed slip problem identification method according to any one of the above.

[0037] In a third aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the environmental monitoring equipment printed slip problem identification method according to any one of the above.

[0038] The environment monitoring equipment print slip problem identification method provided in the application comprises the following steps: acquiring a digital image of a print slip of an environment monitoring equipment; performing feature extraction on the digital image to obtain to-be-checked data; calling a pre-established knowledge base and an algorithm base; based on a calculation method and a logic checking method in the algorithm base, fitting calculation is performed on the to-be-checked data by using a convolutional neural network, the calculation result obtained is compared with standard data in the knowledge base, and a checking result of whether the print slip of the environment monitoring equipment is abnormal is generated based on the comparison result; and the checking result is fed back to a user. Through automatic image processing and data identification, the application significantly reduces the time for manual input and analysis of data, realizes rapid extraction and processing of data from the print slip, accurately identifies text and symbols on the print slip by using the convolutional neural network, reduces possible misunderstanding or error generated by traditional manual reading, compares with the standard data in the knowledge base to ensure that all data are processed and interpreted according to a unified standard, and improves the consistency and reliability of the data. The application realizes automatic checking of problems in the manually monitored print slip, and improves the problem identification capability and efficiency of the monitoring data.

[0039] In addition, the application further provides an environment monitoring equipment print slip problem identification device and medium with the above technical effects. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art according to these drawings.

[0041] Figure 1 The flow chart of one specific embodiment of the environment monitoring equipment print slip problem identification method provided in the application;

[0042] Figure 2 The schematic diagram of another specific embodiment of the environment monitoring equipment print slip problem identification method provided in the application;

[0043] Figure 3 The structural block of the environment monitoring equipment print slip problem identification device provided in the application. DETAILED DESCRIPTION

[0044] To make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. It should be noted that the embodiments in the present disclosure and the features in the embodiments can be combined, separated, interchanged and / or rearranged without conflict, if possible. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0045] The terms used herein are for the purpose of describing specific embodiments and are not intended to be limiting. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, to the extent that the terms "including," "includes," "having," "has," "a," "an," "one," and / or "said" are used in this specification and / or claims, they are intended to be inclusive (i.e., to say that one, more than one, or all of the stated elements are present), unless explicitly indicated to the contrary.

[0046] Environmental monitoring equipment print-out is a form of output generated by relevant equipment during environmental monitoring, which records monitoring data and related information in the form of a record strip, usually used to display and record the quality parameters of environmental factors such as air, water quality, and soil.

[0047] The environmental monitoring equipment print-out usually has the following data and features:

[0048] (1) Data content: The environmental monitoring equipment print-out usually contains key monitoring indicators such as temperature, humidity, chemical concentration, noise level, radiation level, etc. In addition, it can also include time stamp, equipment identification, location information, etc. to ensure the traceability and accuracy of the data.

[0049] (2) Format: The format of the print-out is usually a paper strip or roll, with data printed directly by a printer. The format is designed to be easy to read and archive, commonly in table form or continuous time sequence record.

[0050] The printed slip provides an instant physical record of the environmental monitoring data, facilitating subsequent review and comparison. If the monitored data exceeds the normal range, the abnormal readings on the printed slip can quickly remind the operator to check or take measures. The printed slip can also be used to compare with the electronic record to verify the consistency and accuracy of the data.

[0051] The printed slip of the environmental monitoring device is an important part of environmental monitoring and management, providing a quick and intuitive way of data recording, which plays an important role in ensuring environmental safety and responding to environmental changes. Based on the characteristics and content of the environmental monitoring printed slip, this application uses OCR technology, grayscale, and binarization technology as data extraction means, increases the knowledge base and algorithm base as the basic base, combines the convolutional neural network (CNN) technology, identifies basic parameters such as oxygen content, working condition flow, and flow rate, checks and fits the calculation results, forms the problem identification logic and algorithm for professional printed slip format review, sampling period review, flow and gas volume calculation, etc., and realizes the rapid diagnosis of printed slips of environmental monitoring in different industries and processes.

[0052] The flow chart of one specific embodiment of the environmental monitoring device printed slip problem identification method provided by the present application is shown in Figure 1 The method comprises the following steps:

[0053] S101: Obtain a digital image of the printed slip of the environmental monitoring device.

[0054] A scanner or high-resolution camera can be used to capture the image of the printed slip, so that the captured image is clear and all the text and graphics are recognizable. The image format can be saved in a high-quality digital format such as JPEG, PNG, or TIFF for subsequent processing.

[0055] S102: Feature extraction is performed on the digital image to obtain the data to be checked.

[0056] This step can specifically use optical character recognition (OCR) technology to extract the data in the printed slip and convert the extracted data into editable text content as the data to be checked. This step can convert the text in the image into machine-readable text data, providing a basis for the next feature comparison.

[0057] Before this step, the digital image can also be pre-processed, including but not limited to grayscale, binarization processing, and image denoising and enhancement processing, the purpose of which is to improve the image quality and highlight the key information.

[0058] Optionally, after feature extraction, the data can also be parsed to obtain key information as the data to be checked.

[0059] S103: Call the pre-established knowledge base and algorithm library.

[0060] The knowledge base stores standard data related to environmental monitoring, including print strip format requirements, basic information judgment rules, a calculation model group composed of industry specifications, and theoretical threshold values based on industry characteristics; the algorithm library includes various calculation methods and logical checking methods for checking and identifying problems in print strip data.

[0061] In this application, industry experience and specification summaries are stored in the knowledge base. The knowledge base can include but is not limited to: field query, environmental monitoring specification, industry experience. Algorithms are used to analyze extracted data as a method to identify possible biases and anomalies. The algorithm library can include but is not limited to: format review, sampling period verification, flow accounting, and gas volume accounting.

[0062] S104: Based on the calculation methods and logical checking methods in the algorithm library, the convolutional neural network is used to fit the calculation of the data to be checked, and the calculation results are compared with the standard data in the knowledge base, and the verification result of whether the print strip of the environmental monitoring equipment is abnormal is generated based on the comparison result.

[0063] This step identifies the features in the text of the print strip to be audited through feature comparison, combines the knowledge base and algorithm library, and performs deep learning through convolutional neural network technology to automatically detect logical problems in the print strip of the manual monitoring equipment in the environmental monitoring field.

[0064] It can be understood that this step not only compares the text extracted by OCR, but also compares these data with the pre-set environmental monitoring specifications and industry experience to identify possible abnormalities. Feature comparison can include but is not limited to comparing the logical consistency, format correctness of the data, and consistency with historical data or industry standards.

[0065] In this process, the convolutional neural network (CNN) is used as an anomaly recognition algorithm. The model corresponding to the convolutional neural network is suitable for processing structured text and image data. The model can learn and identify complex patterns and associations from data.

[0066] The network architecture of the convolutional neural network: the CNN architecture includes convolutional layers, pooling layers, and fully connected layers. The convolutional layer is used to extract local features in the image, the pooling layer is used to reduce the spatial size of the features (reduce the amount of calculation), and the fully connected layer is used to output classification or regression results. During model training, historical data and labeled samples are used to train the CNN model. This includes samples of normal and abnormal conditions to improve the accuracy and robustness of the model.

[0067] The input data to be checked is input into the trained CNN model to obtain a prediction result. The output of the CNN is compared with the standard data in the knowledge base. It is checked whether the format requirements are met and whether the numerical value is within the allowed threshold range. If the data deviates from the preset standard, it is marked as abnormal. Further, the specific abnormal type and possible reasons can also be recorded.

[0068] S105: feedback the verification result to the user.

[0069] Specifically, the verification result can be divided into two working conditions of "no abnormality" and "abnormality". Displaying "no abnormality" is considered to pass the check; displaying "abnormality" appears an alarm prompt, indicating that the check fails. After obtaining the verification result, it can be further fed back to the user through a visual display interface. The display items can include all the detailed information of the data to be checked and any identified abnormalities.

[0070] The present application significantly reduces the time of manual input and analysis of data through automatic image processing and data recognition, realizes rapid extraction and processing of data from printed slips. The convolutional neural network is used to accurately identify the text and symbols on the printed slip, reducing the misunderstanding or error that may be caused by traditional manual reading. By comparing with the standard data in the knowledge base, it is ensured that all data is processed and interpreted according to the unified standard, improving the consistency and reliability of the data. It can be seen that the present application realizes automatic checking of problems in manual monitoring of printed slips, improves the problem identification ability and efficiency of monitoring data.

[0071] In order to realize the rapid diagnosis of the environment monitoring printed slip, the embodiments provided by the present application can construct a comprehensive system which uses OCR technology, image processing technology (such as grayscale, binarization), knowledge base, algorithm library and convolutional neural network (CNN) to realize automatic data verification and problem identification. Figure 2 The schematic diagram showing another specific embodiment of the environment monitoring device printed slip problem identification method provided by the present application is shown in FIG. 6. Figure 2 The process is specifically implemented as follows:

[0072] Original data acquisition, which usually includes scanning or taking pictures of the manually monitored printed slip to obtain a digital image.

[0073] After acquiring the digital image, the image can be pre-processed, including grayscale processing to convert color images to grayscale images, reducing the complexity of processing; through binary processing, the image is converted to black and white, highlighting the contrast between text and background, facilitating subsequent character recognition; noise removal is used to clean up interference elements in the image. Pre-processing operations help to clearly display the text in the image, facilitating subsequent OCR technology for character recognition. The main purpose of this step is to optimize image quality and improve the accuracy of character recognition. OCR technology is used to extract text data from the optimized image. These text data will be used for subsequent data comparison and analysis, which is a key link in data processing, converting image data into operable text format.

[0074] The text extracted by OCR is parsed to identify key parameters such as oxygen content, operating flow, flow rate, etc. This step can specifically use parsing algorithms to identify and extract numbers and their units. Convolutional Neural Network (CNN) technology is used to analyze the extracted text through deep learning, comparing data in the knowledge base and algorithm library. The core of this step is to use machine learning technology for automated data analysis to detect anomalies and errors in the data.

[0075] Among them, format review can check whether the data on the environmental monitoring print slip meets industry standards. Sampling period review can verify whether the data collection time meets the specified sampling period. Flow and gas volume accounting is used to calculate and verify the accuracy of flow and gas collection volume.

[0076] The analysis results will be classified as normal or abnormal, with normal data showing as passed and abnormal data triggering an alarm. The final results can be used for real-time monitoring or archived for future review or further analysis.

[0077] All components in the system can be integrated into a user-friendly interface to ensure that all operations can be performed through simple operations.

[0078] As a specific implementation, the print slip standard format requirements include text font, standard line spacing, numbering rules. Through the convolutional neural network, the print slip can be reviewed for standard format, identifying problems such as different number of digits, incorrect numbering order, or identical numbers, thereby identifying print slip fraud.

[0079] In the embodiment, based on the calculation method and logical checking method in the algorithm library, the convolutional neural network is used to perform fitting calculation on the to-be-checked data, the obtained calculation result is compared with the standard data in the knowledge base, and a checking result of whether the print slip of the environmental monitoring device is abnormal is generated based on the comparison result.

[0080] The basic information judgment rule includes sampling point information, time rule, personnel arrangement rule and signature authentication standard. The sampling point information is used to record the standard operation and historical data of each sampling point. The time rule is used to record the sampling time interval and time window specification, and is used to judge whether there is a time conflict or repetition. The personnel arrangement rule is used to record the person in charge of each sampling point and the work schedule thereof, and is used to detect personnel logical conflicts. The signature authentication standard is used to record what kind of signature is considered valid, including the format of the signature and the necessary authentication information.

[0081] The calculation method and logical checking method in the algorithm library are used to perform fitting calculation on the to-be-checked data by using the convolutional neural network, the obtained calculation result is compared with the standard data in the knowledge base, and a checking result of whether the print slip of the environmental monitoring device is abnormal is generated based on the comparison result. The time and point data on each print slip are compared with the rules in the knowledge base to check whether there are repeated time or sampling records in adjacent time; the operation personnel and time recorded on the print slip are checked against the personnel arrangement table in the knowledge base to detect whether there is a logical conflict (for example, the same person sampling at different locations at the same time); it is verified whether the signature on the print slip meets the preset effective standard: including the style, position of the signature and whether it contains necessary authentication elements (such as date, time stamp); and a checking result of whether the print slip of the environmental monitoring device is abnormal is generated based on the comparison result.

[0082] As a specific implementation, the knowledge base can include a calculation model group composed of industry standards such as HJ / T397-2007 and GB / T16157-1996, such as a sampling flow rate calculation formula, a sampling volume calculation formula, and theoretical thresholds based on industry characteristics, such as the benchmark oxygen content of 8% for coke oven stacks after implementing ultra-low in the coking industry, the benchmark oxygen content of 8% in the oxidized state of the furnace exhaust, the benchmark oxygen content of 5% in the reduced state of the furnace exhaust, the benchmark oxygen content of 10 for the cement kiln and kiln tail heat recovery system, the benchmark oxygen content of 8 for the drying equipment using independent heat source, the benchmark oxygen content of 8% for the non-pure oxygen combustion glass melting furnace flue gas, the benchmark oxygen content of 6% for the coal-fired boiler of thermal power, and the benchmark oxygen content of 18 for the ceramic industry.

[0083] The algorithm library can also include time, flow, volume calculation methods in addition to the specification model required in the knowledge base, and checks the key parameters such as sampling time, sampling number, sampling frequency, and sampling volume against the specification requirements of the knowledge base, for example, HJ / T 397-2007 stipulates that boiler particulate matter sampling must be multi-point sampling, and in principle, each point sampling time is not less than 3 min, and the sampling time of each point should be equal or the total gas volume collected by the samples collected for each boiler determination is not less than 1 m. Each sampling, at least 3 samples are collected, and the average value is taken.

[0084] The algorithm library includes sampling flow rate, sampling flow rate calculation, sampling flow rate is calculated by dynamic pressure, static pressure, pitot tube correction coefficient, the calculation result and the printed small number value are compared, and the deviation rate exceeds a certain value, which is regarded as abnormal. Based on the calculation method and logical checking method in the algorithm library, the convolutional neural network is used to fit and calculate the data to be checked, and the calculation result is compared with the standard data in the knowledge base, and based on the comparison result, the verification result of whether the printing small piece of the environmental monitoring equipment exists is generated. Abnormal includes:

[0085] The flow rate is calculated according to the sampling flow rate calculation formula in the algorithm library, and the flow rate calculation formula is:

[0086]

[0087] Wherein, V s represents the gas flow rate of wet exhaust, unit: m / s; V a represents the air flow rate of the ventilation duct at normal temperature and pressure, unit: m / s; B a represents atmospheric pressure, unit: Pa; K p represents the pitot tube correction coefficient; P d represents the exhaust dynamic pressure, unit: Pa; P s represents the exhaust static pressure, unit: Pa; p s represents the density of wet exhaust, unit: kg / m 3 ; M s represents the molecular weight of wet exhaust, unit: kg / kmol; t s represents the exhaust temperature, unit: ℃; The calculated flow rate is compared with the flow rate theoretical threshold value in the knowledge base, and if it exceeds the flow rate theoretical threshold value, it is determined that there is an abnormality, and the verification result of the existence of the printing small piece of the environmental monitoring equipment is generated. Abnormal.

[0088] Based on the sampling nozzle diameter, sampling flow rate, and sampling duration, the sampling volume is calculated according to the sampling volume calculation formula in the algorithm library. The calculated sampling volume is then compared with the theoretical sampling volume threshold in the knowledge base. If the calculated sampling volume exceeds the theoretical sampling volume threshold, an anomaly is determined, and a verification result indicating an anomaly is generated for the environmental monitoring equipment printing strip.

[0089] The algorithm library may also include pre-measurement pressure threshold identification. This process includes: acquiring the pre-measurement pressure, determining whether the pre-measurement pressure exceeds the theoretical pre-measurement pressure threshold, and if it does, determining that there is an anomaly and generating a verification result indicating that the environmental monitoring equipment prints an anomaly.

[0090] As one specific implementation, the algorithm library can include anomaly identification for multiple samples at the same location whose differences in oxygen content, flow rate, smoke temperature, and humidity exceed a certain specific limit. In this embodiment, feature extraction of the digital image to obtain the data to be verified includes: extracting text data from the digital image using optical character recognition technology, parsing the text data to identify key parameters, and obtaining the data to be verified; the data to be verified includes oxygen content, flow rate, smoke temperature, and humidity. Normal ranges or specific thresholds for various environmental parameters (such as oxygen content, flow rate, smoke temperature, and humidity) are predefined. These threshold values ​​are based on industry standards, historical data analysis, or expert opinions.

[0091] The calculation method and logical checking method based on the algorithm library, which uses a convolutional neural network to fit and calculate the data to be verified, compares the calculation results with standard data in the knowledge base, and generates a verification result for whether the environmental monitoring equipment printing strip is abnormal based on the comparison results, including: performing numerical analysis on the data to be verified, calculating the average, standard deviation and other statistical indicators of multiple samples at the same location; performing anomaly detection on each parameter, comparing the parameter value of each sample with a set threshold or average; if the parameter value of a sample exceeds the set threshold or average, it is determined that there is an anomaly, and a verification result for the abnormality of the environmental monitoring equipment printing strip is generated.

[0092] The algorithm library includes constant-rate tracking sampling review. When the deviation rate of the sampling flow rate of different printed strips at a unified location is less than a certain value, it is judged as constant-flow sampling, i.e., constant-rate tracking sampling that does not meet the specification requirements. In this embodiment, based on the calculation methods and logical checking methods in the algorithm library, a convolutional neural network is used to fit and calculate the data to be checked. The calculation results are compared with the standard data in the knowledge base. Based on the comparison results, a verification result is generated to determine whether there are any abnormalities in the printed strips of the environmental monitoring equipment.

[0093] Collect sampling flow rate data from the printed strips on the environmental monitoring equipment at each sampling point;

[0094] Statistical analysis is performed on the collected sampling flow rate data to calculate the average value, standard deviation and deviation rate;

[0095] According to the deviation rate threshold set in the knowledge base, the calculated deviation rate is compared with the deviation rate threshold to determine whether it meets the requirements of constant speed tracking sampling. If it does not meet the requirements, it is determined that there is an anomaly, and a check result of the environment monitoring device print strip is generated.

[0096] In any of the above embodiments, before acquiring the digital image of the environment monitoring device print strip, the process of industry and parameter solidification of the environment monitoring device print strip can also be included. Industry and parameter solidification refers to specific print strip indicators brought by industry and process characteristics, including indicator types, types, threshold ranges, calculation formulas, unit conversions, etc., which are used to preset format review and judgment logic functions to prevent interference from other industries. Industry and parameter solidification can be manually input from the knowledge base and the algorithm library. By selecting the industry and process to solidify the parameters to be audited with industry characteristics, the knowledge base and algorithm library used for corresponding feature comparison can be corresponded.

[0097] The specific implementation of this process can be: acquiring information inputting the industry type, process, and working condition of the environment monitoring device print strip; loading corresponding data from the knowledge base and algorithm library based on the industry type, process, and working condition of the environment monitoring device print strip.

[0098] By presetting industry-specific parameters and standards, the system can more accurately interpret the data on the print strip and avoid misinterpretation due to differences in standards between industries. The specific parameters and standards allow the system to process data without having to judge the meaning and processing method of each parameter from scratch, thereby speeding up the data processing process. It can also ensure that the analysis is not disturbed by irrelevant industry standards, as the monitoring standards and environmental factors of interest may differ for each industry.

[0099] After acquiring the digital image of the environment monitoring device print strip, it also includes image preprocessing of the digital image, which includes grayscale processing, binary processing, and noise removal processing of the digital image. Through the preprocessing operation, the data dimension can be reduced, better contrast and brightness information can be provided, and the image features can be more easily analyzed and recognized, so that the print strip data can be quickly identified and standardized.

[0100] In addition, the present application also provides an environment monitoring device print strip problem identification device, such as Figure 3The structure block diagram of the environment monitoring device printing small strip problem identification device provided in the application is shown in the figure, and the device comprises a memory 31 and a processor 32. The memory 31 stores a computer program, and the computer program is executed by the processor 32 to realize the environment monitoring device printing small strip problem identification method according to any one of the above.

[0101] The environment monitoring device printing small strip problem identification device provided in the application corresponds to the environment monitoring device printing small strip problem identification method described above. For details, please refer to the above description, which will not be repeated here.

[0102] In addition, the application also provides a computer readable storage medium, which stores a computer program. When the computer program is executed by a processor, the environment monitoring device printing small strip problem identification method according to any one of the above is realized.

[0103] The computer readable storage medium includes permanent and non-permanent, removable and non-removable media, which can realize information storage by any method or technology. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. According to the definition in this paper, computer readable medium does not include transitory computer readable medium, such as modulated data signal and carrier wave.

[0104] The skilled person should also further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been described in the above description. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0105] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in random access memory (RAM), flash memory, read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and

[0106] The specific implementation described above is to explain the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific implementation of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An environmental monitoring device print tape issue identification method, characterized by, The method comprises the following steps: acquiring a digital image of a printing slip of an environmental monitoring device; performing feature extraction on the digital image to obtain to-be-checked data; calling a pre-established knowledge base and an algorithm base; the knowledge base stores standard data related to environmental monitoring, and the standard data comprises printing slip format requirements, basic information judgment rules, a calculation model group constituted by industry specifications, and theoretical thresholds based on industry characteristics; the algorithm base comprises a plurality of calculation methods and logical checking methods for checking and identifying problems of the printing slip data; based on the calculation methods and logical checking methods in the algorithm base, fitting calculation is performed on the to-be-checked data by using a convolutional neural network, the calculation result is compared with the standard data in the knowledge base, and a check result of whether the printing slip of the environmental monitoring device is abnormal is generated based on the comparison result, which comprises: calculating a flow rate according to a flow rate calculation formula in the algorithm base, and the flow rate calculation formula is: wherein, V s represents the gas flow rate of the wet exhaust, in m / s; V a represents the air flow rate of the ventilation duct at normal temperature and pressure, in m / s; B a represents the atmospheric pressure, in Pa; K p represents the Pitot tube correction coefficient; P d represents the exhaust dynamic pressure, in Pa; P s represents the exhaust static pressure, in Pa; p s represents the density of the wet exhaust, in kg / m 3 ; M s represents the molecular weight of the wet exhaust, in kg / kmol; t s represents the exhaust temperature, in °C; comparing the calculated flow rate with a flow rate theoretical threshold value in a knowledge base, and if the flow rate exceeds the flow rate theoretical threshold value, determining that an anomaly exists, and generating a check result of the environmental monitoring device indicating that an anomaly exists; based on the sampling nozzle diameter, the sampling flow rate, and the sampling time length, calculating a sampling volume according to a sampling volume calculation formula in the algorithm base, and comparing the calculated sampling volume with a theoretical threshold value of the sampling volume in the knowledge base; if the calculated sampling volume exceeds the theoretical threshold value of the sampling volume, it is determined that there is an abnormality, and a check result that the printing slip of the environmental monitoring device is abnormal is generated; acquiring a pre-metering pressure, and determining whether the pre-metering pressure exceeds a theoretical threshold value of the pre-metering pressure; if the pre-metering pressure exceeds the theoretical threshold value of the pre-metering pressure, it is determined that there is an abnormality, and a check result that the printing slip of the environmental monitoring device is abnormal is generated; feeding back the check result to a user.

2. The method of claim 1, wherein, The printing slip standard format requirements comprise a text font, a standard line spacing, and a numbering rule; based on the calculation methods and logical checking methods in the algorithm base, fitting calculation is performed on the to-be-checked data by using a convolutional neural network, the calculation result is compared with the standard data in the knowledge base, and a check result of whether the printing slip of the environmental monitoring device is abnormal is generated based on the comparison result, which comprises: performing standard format review and numbering problem checking on the printing slip by using the convolutional neural network; if there is a problem that the format does not conform to the standard format requirements, the number of digits is different, the numbering is disordered, or the numbering is the same, it is determined that the printing slip has a counterfeiting problem, and a check result that the printing slip is abnormal is generated.

3. The method of claim 1, wherein the print slip problem is identified by the environmental monitoring device based on the at least one of the following: The basic information judgment rules comprise sampling point information, time rules, personnel arrangement rules, and signature authentication standards; ​ based on the calculation methods and logical checking methods in the algorithm base, fitting calculation is performed on the to-be-checked data by using a convolutional neural network, the calculation result is compared with the standard data in the knowledge base, and a check result of whether the printing slip of the environmental monitoring device is abnormal is generated based on the comparison result, which comprises: The time and point data on each printed slip are analyzed to check whether there are repeated time or sampling records in adjacent time; according to the operator and time recorded on the printed slip, the personnel arrangement table in the knowledge base is checked to detect whether there is a logical conflict; it is checked whether the signature on the printed slip meets the preset effective standard: including the style, position of the signature and whether it contains authentication elements; based on the comparison result, a check result of whether the environmental monitoring equipment printed slip is abnormal is generated.

4. The method of claim 1, wherein, The feature extraction of the digital image includes: Text data is extracted from the digital image by using optical character recognition technology, and the text data is analyzed to identify key parameters to obtain the data to be checked; the data to be checked includes oxygen content, flow, smoke temperature and humidity; The calculation method and logic checking method in the algorithm library are used to fit and calculate the data to be checked by using a convolutional neural network, and the calculation result is compared with the standard data in the knowledge base, and a check result of whether the environmental monitoring equipment printed slip is abnormal is generated based on the comparison result, which includes: The data to be checked is subjected to numerical analysis, and the average value, standard deviation and other statistical indicators of multiple samples at the same point are calculated; the parameters of each sample are compared with the set threshold value or average value; if the parameter value of the sample exceeds the set threshold value or average value, it is determined that there is an abnormality, and a check result of the environmental monitoring equipment printed slip is generated.

5. The method of claim 1, wherein, The calculation method and logic checking method in the algorithm library are used to fit and calculate the data to be checked by using a convolutional neural network, and the calculation result is compared with the standard data in the knowledge base, and a check result of whether the environmental monitoring equipment printed slip is abnormal is generated based on the comparison result, which includes: The sampling flow rate data of each sampling point is collected from the printed slip of the environmental monitoring equipment; The collected sampling flow rate data is statistically analyzed to calculate the average value, standard deviation and deviation rate; According to the set deviation rate threshold value in the knowledge base, the calculated deviation rate is compared with the deviation rate threshold value to determine whether it meets the requirements of constant speed tracking sampling, and if it does not meet the requirements, it is determined that there is an abnormality, and a check result of the environmental monitoring equipment printed slip is generated.

6. The method of claim 1 to 3, wherein, Before the digital image of the environmental monitoring equipment printed slip is obtained, it further includes: Information inputting the industry type, process and working condition to which the environmental monitoring equipment printed slip belongs is obtained; Based on the industry type, process and working condition to which the environmental monitoring equipment printed slip belongs, corresponding data is loaded from the knowledge base and algorithm library.

7. The method of claim 6, wherein the method further comprises: After the digital image of the environmental monitoring equipment printed slip is obtained, it further includes: The digital image is preprocessed, which includes grayscale processing, binary processing and noise removal processing of the digital image.

8. An environmental monitoring device print tape issue identification device, characterized by, It includes a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to realize the environmental monitoring equipment printed slip problem identification method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, A computer program product, comprising a computer program stored thereon, which, when executed by a processor, implements the method according to any one of claims 1-7.

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