Quality control methods and equipment for data reported by pollution-emitting enterprises

Abstract

The disclosed embodiments provide a method and device for quality control of data reported by pollution-emitting enterprises. The method for quality control of data reported by pollution-emitting enterprises includes: mining and analyzing the production technology and production process data of various industries, and constructing a raw material-output product association database; when receiving the data reported by the pollution-emitting enterprise, querying the raw material-output product association database based on the reported data to determine the possible unreported data; generating corresponding reporting data boxes and / or reporting prompt information based on the possible unreported data. By pre-constructing the raw material-output product association database, when obtaining part of the reported data reported by the pollution-emitting enterprise, querying the reported data through the partial reported data to see if there is any possible missing data. When it is determined that there is possible missing data, prompts can be given by generating corresponding reporting data boxes or reporting prompt information to guide the pollution-emitting enterprise to report the possible missing data.

Description

Technical Field

[0001] The present disclosure relates to the technical field of pollutant emission accounting, and in particular to a method and device for quality control of data reported by pollution emission enterprises. Background Art

[0002] Pollution source data reporting is the foundation for calculating pollution emissions. The accuracy and rationality of these data directly impacts the statistical accounting of regional pollution emissions. Currently, data reporting for stationary pollution sources is performed by manufacturing companies, which are directly responsible for these sources. This reporting is plagued by issues such as arbitrary reporting, omissions, and errors, and the accuracy of these reported data cannot be guaranteed. Reliable statistical accounting data cannot be generated based on these unreliable data. Summary of the Invention

[0003] In a first aspect, an embodiment of the present disclosure provides a method for quality control of data reported by a pollution-emitting enterprise, comprising:

[0004] Mining and analyzing the production technology and production process data of various industries to build a raw material-output correlation database, which includes the input and output correlation relationships of production technology, raw materials, terminal output products, and intermediate output products in various industries;

[0005] Upon receiving reported data from a pollution-emitting enterprise, querying the raw material-output product association database based on the reported data to determine possible unreported data; the reported data includes at least one of the industry, production process, some raw materials, some terminal output products, and some intermediate products; the possible unreported data includes at least one of unreported initial raw materials, unreported intermediate products, and unreported terminal output products;

[0006] A corresponding reporting data box and / or reporting prompt information is generated based on the possible unreported data; the reporting data box is used to receive the possible unreported data of the corresponding attribute, and the reporting prompt information is used to prompt the existence of the possible unreported data.

[0007] Optionally, conduct mining and analysis of production technology and process data in various industries to build a raw material-output product association database, including:

[0008] Collect historical reporting data and standardize them to obtain standardized names for various raw materials and outputs;

[0009] Based on the input-output relationship in the production processes and production process data of various industries, a raw material-output product association database with standardized naming under various production processes is established.

[0010] Optionally, the method further includes: if the reported data includes process steps of a production process, determining whether the production process of the pollution-emitting enterprise is a combined process;

[0011] If the production process is determined to be a combination process, determining the unreported process steps based on the reported process steps and the corresponding combination process, and determining the output products of the unreported process steps based on the raw material-product association database;

[0012] Generate reporting prompt information indicating that there are unreported process steps and corresponding output products, and / or generate a reporting data frame for reporting the output products corresponding to the unreported process steps.

[0013] Optionally, when the reported data includes first numerical data related to raw material consumption or output product yield, the method further includes:

[0014] Determining whether the first numerical data is outside a normal production activity level interval, wherein the normal production activity level interval is determined by statistically analyzing historical numerical data, the historical numerical data being corresponding raw material consumption or output production of similar enterprises obtained based on the industry type and enterprise scale of the pollution-emitting enterprise;

[0015] In the case where the first numerical data is outside the normal production activity level, prompt information is generated to prompt whether the first numerical data is abnormal reporting data.

[0016] Optionally, when the first numerical data is outside the normal production activity level, generating prompt information for determining whether the first numerical data is abnormal reporting data further includes:

[0017] Determining whether there is second numerical data associated with the first numerical data in the reported data;

[0018] When the reported data includes second numerical data associated with the first numerical data, calculating an actual correlation coefficient between the first numerical data and the second numerical data;

[0019] Determine whether the actual correlation coefficient is outside the predetermined reasonable correlation coefficient range;

[0020] When the actual correlation coefficient is outside the predetermined reasonable correlation coefficient range, prompt information is generated to determine whether the first numerical data is abnormal reported data.

[0021] Optionally, when the reported data includes first numerical data related to raw material consumption or output product yield, the method further includes:

[0022] Obtaining historical numerical data related to corresponding raw material consumption or output of the pollution-emitting enterprise, and sorting the historical numerical data by time to obtain a sorted sequence;

[0023] identifying whether the first numerical data is abnormal data based on the sorting sequence;

[0024] In the case that the first numerical data is abnormal data, prompt information is generated to prompt whether the first numerical data is abnormal reported data.

[0025] Optionally, when the reported data includes two numerical data related to raw material consumption and / or output product output, and the two numerical data are determined to be relevant data based on the production process adopted by the pollution-emitting enterprise, the method further includes:

[0026] Calculating an actual correlation coefficient based on the two numerical data, and determining whether the actual correlation coefficient is outside a predetermined reasonable correlation coefficient range;

[0027] When the actual correlation coefficient is outside the predetermined reasonable correlation coefficient range, prompt information is generated indicating that at least one of the two numerical data is abnormal data.

[0028] Optionally, the reported data includes basic information, including specific information of the production line, a detailed description of the production process, and a classification description of the types of raw materials;

[0029] The method further includes: when the reported data includes numerical data related to raw material consumption or output product yield, performing semantic information mining based on the basic information, the numerical data, and attributes of the numerical data to determine whether there is abnormal data in the numerical data;

[0030] In the case where it is determined that there is abnormal data in the numerical data, target numerical data identified as abnormal data is determined, and prompt information indicating that the target numerical data is abnormal data is generated.

[0031] Optionally, the method further includes: obtaining market industry data; the market industry data includes historical data and / or forecast data related to the market status of the industry to which the pollution-emitting enterprise belongs;

[0032] Performing semantic information mining based on the basic information, the numerical data, and attributes of the numerical data to determine whether there is abnormal data in the numerical data includes:

[0033] Semantic information mining is performed based on the basic information, the numerical data, the attribute information of the numerical data, and the market industry data to determine whether the numerical data has abnormal data.

[0034] In a second aspect, an embodiment of the present disclosure provides a computing device comprising a storage device and a processor, wherein the storage device is used to store a computer program; when the computer program is loaded by the processor, the processor executes the aforementioned method for quality control of data reported by pollution emission enterprises.

[0035] By pre-building a raw material-output product association database, the disclosed embodiments can be used to query the partially reported data from polluting enterprises to determine if any data may be missing. If any missing data is identified, a corresponding reporting box or reporting prompt can be generated to provide guidance, guiding the polluting enterprises to report any missing data. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0037] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without inventive work, including:

[0038] Figure 1 This is a flow chart of a method for quality control of data reported by pollution-emitting enterprises provided in some embodiments of the present disclosure;

[0039] Figure 2 This is a flow chart of a method for prompting that reported data has missing data, provided by some embodiments of the present disclosure;

[0040] Figure 3 is a flow chart of a method for determining abnormal numerical data provided in some embodiments of the present disclosure;

[0041] Figure 4 is a flow chart of a method for determining abnormal numerical data provided by some further embodiments of the present disclosure;

[0042] Figure 5 It is a structural diagram of a computing device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0043] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0044] As used herein, the term "including" and its variations are open-ended inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description. In this document, relational terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0045] To address the issue of inaccurate later-stage pollution emission accounting data due to errors in some reported data, the present disclosure provides a new method for quality control of data reported by pollution emission enterprises. This method guides reporting personnel during the reporting process, identifies issues in the reported data that do not conform to data logic, and rapidly analyzes the reported data.

[0046] Figure 1 This is a flow chart of the quality control method for reporting data of pollution emission enterprises provided by some embodiments of the present disclosure. Figure 1 As shown, the method for quality control of data reported by pollution emission enterprises provided in the embodiment of the present disclosure includes S110-S130.

[0047] S110: Mining and analyzing the production technology and production process data of various industries to build a raw material-output product association database.

[0048] When reporting data on fixed pollution sources, polluting companies often report arbitrary data items based on their own experience and existing data, submitting whatever data comes to mind without a comprehensive understanding of their company's production processes, inputs, and outputs. This issue results in many companies reporting data that clearly doesn't reflect their actual circumstances.

[0049] To solve this problem, the embodiments of the present disclosure consider reducing the dependence on the quality of pollution-emitting enterprises and their reporting personnel as much as possible. It does not require them to have a deep understanding of the enterprises at the technical and production levels, and realizes the reporting of relevant data of pollution-emitting enterprises through active guidance.

[0050] In order to achieve the goals in the previous section, we first need to have relevant basic data, that is, the premise for realizing guided enterprise reporting. In the embodiment of the present disclosure, this premise is the raw material-output product association database. The raw material-output product association database is a database that stores what types of output products will be produced based on the production process when the production enterprise inputs the corresponding raw materials under specific process conditions. The aforementioned output products include the intermediate output products and terminal output products of the enterprise. In other words, as long as there is input of the corresponding type of raw materials and output of the corresponding products in the corresponding production process, this data will be written into the raw material-output product association database. The output products mentioned here should be understood in a broad sense, which includes both sales products and intermediate products produced by the enterprise with production stress as the goal, and associated pollutants in the production of the aforementioned products.

[0051] In the specific implementation, by mining and analyzing the production technology and production process data of various industries, the input-output correlation relationship between raw material inputs, intermediate products and terminal products when a specific industry adopts the corresponding production technology is sorted out, and then a raw material output correlation database is obtained.

[0052] In a specific implementation, the process of mining and analyzing the production technology and production process data of various industries and constructing a raw material-output product association database includes S111-S112.

[0053] S111: Collect historical reported data and standardize them to obtain standardized names for various raw materials and output products.

[0054] In practice, polluting companies often arbitrarily report data, often using non-standardized names for raw materials, outputs, and even production processes. This lack of standardization makes it difficult to establish close correlations between data, impacting the quality of the subsequent linked database.

[0055] To solve this problem, some embodiments of the present disclosure collect historical reported data from various industries and standardize the data names to form standard names for various production processes, raw materials, and output products. In specific implementations, the industry's production and emission coefficient manual can be referenced, and the relationship between raw materials and output products in the industry's production process can be analyzed, and the various raw materials and output products in the historical reported data can be mapped to the corresponding standard product names. By sorting out the historical reported data, it was determined that there were approximately 5,200 types of output products reported by pollution-emitting enterprises. Standardizing the aforementioned 5,200 products can be mapped to a total of 421 standardized product names in 230 industries.

[0056] S112: Based on the production processes of various industries and the relationship between production and emission coefficients in the production process, a raw material-output product association database with standardized naming under various production processes is established.

[0057] Through the analysis of historical reporting data, it is found that pollution-emitting enterprises usually only report information on initial raw materials and end products. However, the actual production of pollution emissions is not limited to the terminal. The intermediate products in the intermediate links of various production processes are also an important source of pollution and should be included in the accounting of pollution emissions. Based on this, the embodiment of the present disclosure is based on the production process of various industries and the relationship between the production and emission coefficients in the production process. With the help of big data analysis technology, in-depth mining and analysis work is carried out to reveal the intrinsic connection and interaction between key raw materials and pollutants, and to construct a standardized raw material-output product association database under various production processes.

[0058] As previously analyzed, because outputs include both end products and intermediate products, and different outputs may generate pollutants, the aforementioned raw material-output product association data can also be understood as an output product-required pollutant association database. In other words, through this raw material-output product association database, the association between intermediate products and required pollutants can be determined based on a single raw material or single product (including, in some cases, the production process). Furthermore, based on specific raw materials and output products, upstream, midstream, and downstream products and their required pollutants can be determined.

[0059] In specific implementation, a dynamic update mechanism can be introduced to ensure that the data in the constructed raw material-output product association database will be synchronized with the development of industry technology, reflecting the changes in industry processes and industry output products with technological progress, thereby ensuring the real-time and accuracy of the data reported by enterprises.

[0060] S120: When the reported data of the pollution-emitting enterprise is received, the raw material-output product association database is searched based on the reported data to determine whether there may be unreported data.

[0061] When polluting enterprises report relevant data, reporting personnel complete partial reporting based on known circumstances and data, forming the reported data. In practice, the reported data may include at least one of the polluting enterprise's sub-industry, the production process used, some raw materials, some end products, and some intermediate outputs.

[0062] After obtaining the reported data, searching the raw material-output product association data using the reported data can reveal possible unreported data. In specific implementations, possible unreported data includes at least one of unreported initial raw materials, unreported intermediate products, and unreported final products. It should be noted that possible unreported data may overlap with some reported data (perhaps due to process reasons where a certain raw material is an intermediate product or even a final product).

[0063] In practice, polluting enterprises often submit data using enterprise-side electronic devices, while data queries to identify potential unreported data are often performed by data servers deployed by industry regulatory authorities. Therefore, in practice, the data servers deployed by industry regulatory authorities often receive the reported data from polluting enterprises and then perform data queries based on the reported data to identify potential unreported data (this enhances data confidentiality). Of course, in some embodiments, the aforementioned raw material-output correlation database may also be distributed to enterprise-side electronic devices, which can then perform data searches based on the reported data to identify potential unreported data.

[0064] For example, in one specific application, a pollution-reporting enterprise with the industry code 2622 reported only phosphate rock and sulfuric acid. Based on the industry code and the enterprise's production process, it was determined that this enterprise used phosphate rock, sulfuric acid, phosphoric acid, and synthetic ammonia as raw materials to produce diammonium phosphate. Using the raw material-output association database, it was determined that synthetic ammonia, a key product (and a major pollutant), was omitted from the raw material list.

[0065] For example, in another specific application, a pollution-reporting enterprise in the cogeneration industry (industry code 4412) reported using natural gas as its raw material (fuel). Based on its production process and the aforementioned raw material-output product association database, it was determined that the outputs (pollutants) emitted from the polluting enterprise's industrial boilers included sulfur dioxide, nitrogen oxides, and volatile organic compounds. However, the enterprise's reported data only reported sulfur dioxide and volatile organic compounds. Using the raw material-output product association database, it was determined that nitrogen oxides, a key output (pollutant), were omitted from the raw material list.

[0066] S130: Generate corresponding reporting data box and / or reporting prompt information based on the data that may not be reported.

[0067] After determining that data may not be reported, a data reporting frame or reporting prompt information can be generated according to the specific situation. The data reporting frame is used to receive data that may not be reported for the corresponding attribute. The reporting prompt information is used to indicate that data may not be reported.

[0068] In a specific implementation, if the device performing data processing is a backend server controlled by the competent authority, the backend server can send an instruction to the enterprise-side electronic device to generate a reporting data box or reporting prompt information, and the enterprise-side electronic device can then directly generate the reporting data box or reporting prompt information. If the device performing data processing is an enterprise-side electronic device, the enterprise-side electronic device can directly generate the bet winning reporting data box or reporting prompt information.

[0069] By adopting the embodiment of the present disclosure, by pre-building a raw material-output product association database, when obtaining partial reported data of the pollution emission enterprise, the partially reported data can be used to query whether there is any possible missing data in the reported data.

[0070] When it is determined that there may be missing data, prompts can be provided by generating corresponding data reporting boxes or reporting prompt information to guide pollution-emitting enterprises to report the possible missing data.

[0071] An analysis of the current state of processes across a wide range of industries and enterprises reveals that many industries utilize combined production processes, meaning they require at least two steps to convert raw materials into end products. For example, through in-depth research and analysis of production processes across various industries, 16 specific subcategories have been identified as possessing combined processes: nitrogen fertilizer manufacturing, steelmaking, coking, ironmaking, coal-based synthesis gas production, coal product manufacturing, coal-based liquid fuel production, other non-metallic mineral product manufacturing, other basic chemical raw material manufacturing, other coal processing, lightweight building material manufacturing, biomass compacted fuel processing, cement product manufacturing, inorganic alkali manufacturing, inorganic acid manufacturing, and inorganic salt manufacturing.

[0072] For the aforementioned industries that adopt combination processes, the embodiments of the present disclosure further carefully sort out and analyze the combination process production processes of these industries, similarly label and classify the emission process nodes that may appear in each combination process, and determine the process steps included in the combination process and the output products of each process step.

[0073] Figure 2 This is a flow chart of a method for prompting missing data in reported data provided by some embodiments of the present disclosure. Figure 2 As shown, during the data reporting process of the pollution reporting enterprise, if the reported data includes process steps of the production process, the following S140-S160 can also be executed.

[0074] S140: Determine whether the production process of the pollution-emitting enterprise is a combined process; if so, execute S150; if not, end the execution.

[0075] Whether a polluting enterprise's production process is a combination process is determined based on the polluting enterprise's industry and the process type reported. Once the reported process type is determined, the corresponding process database can be used to determine whether the enterprise's process is a combination process.

[0076] S150: Determine unreported process steps based on reported process steps and corresponding combined processes, and determine output products of unreported process steps based on a raw material-product association database.

[0077] S160: Generate reporting prompt information indicating that there are unreported process steps and corresponding output products, and / or generate a reporting data frame for reporting the output products corresponding to the unreported process steps.

[0078] If it is determined that the production process used by the polluting enterprise is a combination process, the combination process can then be used to determine whether there are any unreported process steps and further determine the output products of the unreported process steps. After determining the above data, a reporting prompt can be generated to indicate the unreported process steps and corresponding output products, or a reporting data box can be generated for reporting the output products corresponding to the unreported process steps, using methods similar to those mentioned above.

[0079] By adopting the aforementioned S140-S160, by automatically identifying the process flow of the combined process, the data to be reported for the combined process can be automatically reviewed in an automated manner without manual review, thereby improving work efficiency and reducing errors caused by the reporting personnel's lack of understanding of the process.

[0080] For example, a coking company produces coke from coking coal. Because coking falls under one of the aforementioned combined process industries, combined process quality control is required. The complete combined process for the coking industry consists of a coal loading station, a coke pushing station, a coke oven chimney, a general exhaust outlet, and unorganized processes. However, a company's coking production line does not account for a coke pushing station. Using the aforementioned method, this missing coke pushing station can be identified and a reporting data frame can be generated to report the coke pushing station data.

[0081] In addition to the aforementioned missing data, errors in enterprise-reported data may also include errors in reported numerical data. The following describes how to identify possible errors in reported numerical data.

[0082] Figure 3is a flow chart of a method for determining abnormal numerical data provided in some embodiments of the present disclosure. In some embodiments, when the reported data includes first numerical data related to raw material consumption or output product yield, the quality control method for the reported data may further include the following S170-S180.

[0083] S170: Determine whether the first numerical data is outside the normal production activity level range; if so, execute S180.

[0084] S180: Generate prompt information to determine whether the first numerical data is abnormal reported data.

[0085] Through extensive empirical data, it has been found that the raw material consumption or output of polluting enterprises of roughly the same size within the same industry falls within a specific numerical range. Based on this, when determining the industry type and enterprise size of a polluting enterprise, the disclosed embodiments obtain historical data on the corresponding raw material consumption or output of similar enterprises, perform statistical analysis on this historical data, and determine the range of normal production activity levels for enterprises of the corresponding size.

[0086] In specific implementations, the quartile method can be used to conduct statistical analysis on historical numerical data of enterprises of corresponding sizes to determine quartile values. Based on the quartile values, a box plot method can be used to determine normal thresholds, and the intervals determined by the normal thresholds can be used as normal production activity level intervals. For example, in some embodiments, by comprehensively collecting and integrating historical data recorded in emission source statistical tables for different time periods, a detailed list of normal production activity level intervals for 3,481 different types and sizes of enterprises is provided. These production activity level intervals cover various scales, from small and micro-handicraft workshops to medium and large-scale enterprises, and corresponding production activity level intervals are provided for various output products.

[0087] If the first numerical data is outside the normal production activity level range, it is determined that the first numerical data is abnormal reporting data, and thus corresponding prompt information is generated.

[0088] In specific implementation, it may be difficult to detect whether there is a problem with the data reported by the pollution-emitting enterprises simply from the first numerical data reported. By comparing the first numerical data reported by the pollution-emitting enterprises with the corresponding normal production activity level range, abnormal numbers can be identified, and the enterprise data reporting personnel can be reminded to complete the data reporting according to the specific situation, even if erroneous numerical data is found in the reported data. In addition, this method can also enable enterprises to more accurately determine the development trend of industry process technology, and then reasonably plan the direction of future technological improvements based on the technological evolution, so that enterprises can adapt to market competition. In specific implementation, in order to ensure the correctness of the normal activity level range of enterprises of all sizes, a dynamic update mechanism can also be used to readjust the normal production activity level range according to the specific situation, thereby ensuring that the normal production activity level range represents the actual situation of the industry.

[0089] In some embodiments, if the first numerical data is determined to be outside the normal production activity level, this may be due to factors such as industry cycles or enterprise production line transformation, and may not be consistent with actual conditions. To address this issue, if the first numerical data is determined to be outside the normal production activity level, the following steps S190-S210 may be performed first.

[0090] S190: Determine whether there is second numerical data associated with the first numerical data in the reported data; if yes, execute S200; if not, execute S180.

[0091] S200: Calculating an actual correlation coefficient between the first numerical data and the second numerical data.

[0092] S210: Determine whether the actual correlation coefficient is outside a predetermined reasonable correlation coefficient range; if so, execute S180; if not, terminate the execution.

[0093] The aforementioned first numerical data and the second numerical data are associated, which in most cases means that the first numerical data and the second numerical data have a positive correlation. For example, the ratio between the amount of coking coal used in the production line of a coking enterprise and the output of crude benzene generated during the coking process is approximately 100:1 (here the reasonable coefficient range is set to 90:1-110:1). Accordingly, if a pollution-emitting enterprise reports an output of 100 tons of crude benzene for coking (as the first numerical data) and the amount of coking coal used as a raw material is 100,000 tons (as the second numerical data), the actual correlation coefficient between the first numerical data and the second numerical data is calculated to be 1000:1. The actual correlation coefficient is clearly outside the predetermined reasonable relationship coefficient range. At this point, it can be directly determined that the first numerical data is abnormally reported data, and therefore S180 can be executed as before.

[0094] Of course, in actual applications, when the first numerical data and the second numerical data are associated with each other, only the above steps S190 to S210 may be performed to identify whether the first numerical data is abnormal data.

[0095] The above method determines whether the first reported data submitted by the polluting enterprise is abnormal data by horizontal comparison. In other embodiments, vertical comparison can also be used to determine whether the first numerical data is abnormal data. Figure 4 This is a flow chart of a method for determining abnormal numerical data provided by some embodiments of the present disclosure. Figure 4 As shown, in some embodiments, the method for determining abnormal data includes the following S220-S240.

[0096] S220: Obtain historical numerical data of the pollution-emitting enterprise involving corresponding raw material consumption or output product output, and sort the historical numerical data according to time to obtain a sorted sequence.

[0097] For pollution-emitting enterprises such as refineries and chemical companies, the cost of shutting down their production lines after startup is extremely high. Even if the company suffers losses, it still needs to maintain normal production of the production lines. In addition, if the pollution-emitting enterprises have not adjusted their production capacity, their production activity levels should follow market laws. Accordingly, in this case, the raw material consumption and output of the enterprise are determined to have obvious data inertia, and always remain within a relatively reasonable numerical range. Based on this, the embodiment of the present disclosure obtains historical numerical data of the pollution-emitting enterprises involving corresponding raw material consumption or output product output, and sorts the historical numerical data according to time to obtain a sorted sequence.

[0098] S230: Identify whether the first numerical data is abnormal data based on the sorting sequence; if so, execute S240.

[0099] S240: Generate prompt information indicating that the first numerical data may be abnormally reported data.

[0100] After obtaining the sorted sequence, a numerical analysis can be performed on the sorted sequence to determine a reasonable statistical range and determine whether the first numerical data is within the reasonable statistical range. If the first numerical data is determined not to be within the reasonable statistical range based on the sorted sequence, the first numerical data is determined to be abnormal data.

[0101] In one specific application, historical data on specific raw materials or outputs from a pollution-emitting enterprise over the past three years can be sorted to construct a sorted sequence, and the first numerical data can be added to the data sequence. If significant fluctuations in the first numerical data within the data sequence are observed (e.g., a sharp increase, reaching ten times or more of the other data), the first numerical data is determined to be an outlier, and a corresponding warning message is generated.

[0102] In specific implementations, identifying the first numerical data as abnormal using a sorted sequence may be erroneous. For example, after a company enters the production ramp-up phase after a trial run, its raw material consumption and output may increase dramatically. In this case, during the execution of S240, the aforementioned S190-S210 may be executed again to further determine whether the data is abnormal.

[0103] In addition, in specific implementation, the raw materials and output of pollution-emitting enterprises will also be affected by industry development trends and market demand. In this case, the industry development situation and market demand can also be used as reference factors (for example, a reasonable scaling factor range can be determined based on industry development trends or market demand, and the scaling factor range can be compared with the historical data mean) to determine whether the first numerical data is abnormal data.

[0104] In some embodiments, the reported data also includes basic data. Basic data includes detailed information about the production line, a detailed description of the production process, and a classification description of the raw material types. In this case, if the reported data includes numerical data related to raw material consumption or output product volume, the following steps S250-S260 may also be performed.

[0105] S250: Perform semantic information mining based on the basic information, the numerical data, and the attributes of the numerical data to determine whether there is abnormal data in the numerical data; if so, execute S260.

[0106] S260: Determine the target numerical data identified as abnormal data, and generate prompt information indicating that the target numerical data is abnormal data.

[0107] In a specific implementation, semantic information mining based on basic information, numerical data, and attribute information of the numerical data can be performed by inputting the aforementioned information into a pre-trained language recognition model to determine whether the numerical data and corresponding attribute information conflict with the basic information. If there is a conflict, and the probability of the aforementioned conflict reaching a preset probability, the corresponding numerical data is determined to be abnormal data. Accordingly, the language recognition model can be used to determine which numerical data is the target numerical data identified as abnormal data, and then generate a prompt message indicating that the target numerical data is abnormal data.

[0108] In practice, this method can identify anomalous data at specific points in reported data, alerting companies to potential issues with time recording or transparency when reporting production activity levels. This can encourage companies to improve the integrity and transparency of reported data, ensuring the authenticity and reliability of reported data. More specifically, in cases where the reporting company includes a combination of processes, where a single process within the combination includes multiple production lines, this method can identify whether the numerical data reported for some production lines is anomalous.

[0109] In other more specific applications, market conditions may also influence the determination of data anomalies. In this case, market industry data may also be obtained. Market industry data includes historical or forecasted data related to the market status of the industry to which the polluting enterprise belongs. Accordingly, the aforementioned S250 can be further refined to perform semantic information mining based on basic information, numerical data, attribute information of the numerical data, and market industry data to determine whether the numerical data contains any anomalies.

[0110] As analyzed above, the pollution reporting data quality control method provided by the embodiment of the present disclosure utilizes data analysis technology and combines it with the specific industry characteristics of the enterprise to establish a pollution reporting data instruction method based on industry characteristics and data fusion, thereby achieving precise quality control of emission source data. Through process monitoring and automated correction prompts, data processing efficiency and accuracy can be greatly improved, and human errors and malicious intervention in the data reporting stage can be reduced. In addition, using this method, if a certain data is identified as abnormal data but the enterprise verifies that the relevant data is normal data, it can also prompt the enterprise to discover potential problems in production activities, provide reliable decision-making support, and increase the transparency and scientific nature of management.

[0111] In addition to providing the aforementioned method for statistically analyzing volatile organic compound emissions during storage and transportation, the embodiments of the present disclosure also provide a computing device for implementing the aforementioned method. Figure 5 This is a schematic diagram of the structure of the computing device provided by the embodiment of the present disclosure. Figure 5 , which shows a structural diagram of a computing device 500 suitable for implementing the embodiments of the present disclosure. Figure 5 The computing device shown is only an example and should not bring any limitation to the functionality and scope of use of the embodiments of the present disclosure.

[0112] like Figure 5As shown, computing device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. RAM 503 also stores various programs and data required for the operation of computing device 500. Processing device 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0113] Typically, the following devices may be connected to the I / O interface 505: an input device 505 including, for example, a touch screen, a touchpad, a camera, a microphone, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the computing device 500 to communicate with other devices wirelessly or by wire to exchange data. Figure 5 The computing device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0114] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0115] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable storage medium, a computer-readable signal medium, or any combination of the above two.

[0116] Computer-readable storage media may be, for example, but not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0117] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, embodying computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0118] In some embodiments, the client and server can communicate using any currently known or later developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0119] The computer-readable medium may be included in the computing device, or may exist independently without being incorporated into the computing device.

[0120] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the tester computer, partially on the tester computer, as a stand-alone software package, partially on the tester computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the tester computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to the various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0122] The units involved in the embodiments described in the present disclosure may be implemented in software or in hardware. The name of the unit does not, in some cases, limit the unit itself. The functions described above in this document may be at least partially performed by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0123] The foregoing are merely specific embodiments of the present disclosure, intended to enable those skilled in the art to understand and implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not to be limited to the embodiments described herein, but is to be construed in the broadest manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for quality control of data reported by pollution-emitting enterprises, characterized in that: include: Mining and analyzing the production technology and production process data of various industries to build a raw material-output product association database. The raw material-output product association database includes the input-output association relationship of the production technology, raw materials, terminal output products, and intermediate output products of various industries. This includes: collecting historical reporting data and standardizing and organizing them to obtain standardized names for various raw materials and output products; based on the input-output relationship in the production technology and production process data of various industries, establishing a raw material-output product association database with standardized names under various production processes; Upon receiving reported data from a pollution-emitting enterprise, querying the raw material-output product association database based on the reported data to determine possible unreported data; the reported data includes at least one of the industry, production process, some raw materials, some terminal output products, and some intermediate products; the possible unreported data includes at least one of unreported initial raw materials, unreported intermediate products, and unreported terminal output products; Generate a corresponding reporting data frame and / or reporting prompt information based on the possibly unreported data; the reporting data frame is used to receive the possibly unreported data of the corresponding attribute, and the reporting prompt information is used to prompt the existence of the possibly unreported data; If the reported data includes process steps of a production process, determine whether the production process of the pollution-emitting enterprise is a combined process; If the production process is determined to be a combination process, determining the unreported process steps based on the reported process steps and the corresponding combination process, and determining the output products of the unreported process steps based on the raw material-product association database; Generate reporting prompt information indicating that there are unreported process steps and corresponding output products, and / or generate a reporting data frame for reporting the output products corresponding to the unreported process steps.

2. The method according to claim 1, characterized in that In the case where the reported data includes first numerical data related to raw material consumption or output product yield, the method further includes: Determining whether the first numerical data is outside a normal production activity level interval, wherein the normal production activity level interval is determined by statistically analyzing historical numerical data, the historical numerical data being corresponding raw material consumption or output production of similar enterprises obtained based on the industry type and enterprise scale of the pollution-emitting enterprise; In the case where the first numerical data is outside the normal production activity level, prompt information is generated to prompt whether the first numerical data is abnormal reporting data.

3. The method according to claim 2, characterized in that In the case where the first numerical data is outside the normal production activity level, generating prompt information for determining whether the first numerical data is abnormal reporting data further includes: Determining whether there is second numerical data associated with the first numerical data in the reported data; In the case where the reported data includes second numerical data associated with the first numerical data, calculating an actual correlation coefficient between the first numerical data and the second numerical data; Determine whether the actual correlation coefficient is outside the predetermined reasonable correlation coefficient range; When the actual correlation coefficient is outside the predetermined reasonable correlation coefficient range, prompt information is generated to determine whether the first numerical data is abnormal reported data.

4. The method according to claim 1, wherein In the case where the reported data includes first numerical data related to raw material consumption or output product yield, the method further includes: Obtaining historical numerical data related to corresponding raw material consumption or output of the pollution-emitting enterprise, and sorting the historical numerical data by time to obtain a sorted sequence; identifying whether the first numerical data is abnormal data based on the sorting sequence; In the case that the first numerical data is abnormal data, prompt information is generated to prompt whether the first numerical data is abnormal reporting data.

5. The method according to claim 1, wherein In the case where the reported data includes two numerical data related to raw material consumption and / or output product output, and the two numerical data are determined to be relevant data based on the production process adopted by the pollution-emitting enterprise, the method further includes: Calculating an actual correlation coefficient based on the two numerical data, and determining whether the actual correlation coefficient is outside a predetermined reasonable correlation coefficient range; When the actual correlation coefficient is outside the predetermined reasonable correlation coefficient range, prompt information is generated indicating that at least one of the two numerical data is abnormal data.

6. The method according to claim 1, characterized in that The reported data includes basic information, including specific information of the production line, a detailed description of the production process, and a classification description of the types of raw materials; The method further includes: when the reported data includes numerical data related to raw material consumption or output product yield, performing semantic information mining based on the basic information, the numerical data, and attributes of the numerical data to determine whether there is abnormal data in the numerical data; When it is determined that there is abnormal data in the numerical data, target numerical data identified as abnormal data is determined, and prompt information indicating that the target numerical data is abnormal data is generated.

7. The method according to claim 6, characterized in that Also includes: Obtaining market industry data; the market industry data includes historical data and / or forecast data related to the market status of the industry to which the pollution-emitting enterprise belongs; Performing semantic information mining based on the basic information, the numerical data, and attributes of the numerical data to determine whether there is abnormal data in the numerical data includes: Semantic information mining is performed based on the basic information, the numerical data, the attribute information of the numerical data, and the market industry data to determine whether the numerical data has abnormal data.

8. A computing device, characterized in that The device comprises a storage device and a processor, wherein the storage device is used to store a computer program; when the computer program is loaded by the processor, the processor executes the method for quality control of data reported by pollution emission enterprises as described in any one of claims 1 to 7.

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