Method and device for judging authenticity of data reported by coal enterprise, equipment and storage medium

Abstract

The invention relates to a coal enterprise reported data authenticity judgment method and device, equipment and a storage medium. The method comprises the following steps: synchronously acquiring each piece of original data of a service system according to a preset period; performing cross validation on the original data based on a preset multi-dimensional verification rule; and judging the authenticity of the reported data corresponding to the original data based on a verification result obtained by the cross verification. Therefore, according to the embodiment of the invention, the original data of the business system are synchronously acquired, and cross validation is carried out, so that the problem that the authenticity of the reported data is difficult to verify due to data barriers of single data is solved.

Description

Technical Field

[0001] This disclosure relates to the field of enterprise management system technology, and in particular to a method, device and storage medium for judging the authenticity of data reported by coal enterprises. Background Technology

[0002] In related technologies, the acquisition of key data such as inventory and output in the daily operation and management of coal enterprises mainly relies on manual reporting. After manual reporting, enterprises have limited means to judge the authenticity of the data, making it difficult to make an effective judgment on the authenticity of the data. Summary of the Invention

[0003] In view of this, this disclosure proposes a method, device and storage medium for judging the authenticity of data reported by coal enterprises, which judges the authenticity of data reported by coal enterprises based on multiple data sources.

[0004] According to one aspect of this disclosure, a method for judging the authenticity of data reported by coal enterprises is provided, including: Collect raw data from the business system synchronously at a preset cycle; Based on preset multi-dimensional verification rules, cross-validation is performed on each of the original data. Based on the verification results obtained from the cross-validation, the authenticity of the reported data corresponding to the original data is determined.

[0005] In one possible implementation, the step of synchronously collecting various raw data from the business system at a preset period includes: The original data corresponding to different business systems are collected synchronously at the preset period.

[0006] In one possible implementation, the business system includes at least one of a production management system, an energy management system, or a bulk commodity management system; wherein, The raw data corresponding to the production management system includes at least one of the following: inventory data, production data, or ledger data for different coal types in each coal mine. The raw data corresponding to the energy management system includes at least electricity consumption data; The raw data corresponding to the bulk commodity management system includes at least sales data.

[0007] In one possible implementation, the cross-validation of the original data based on preset multi-dimensional verification rules includes: Obtain the production data and the ledger data; Optical character recognition is performed on the image of the ledger data to obtain structured ledger records; The production data is compared with the structured ledger records. If the information is inconsistent, the corresponding production data is marked as abnormal.

[0008] In one possible implementation, the cross-validation of the original data based on preset multi-dimensional verification rules includes: Obtain the power consumption data and the production data; The actual energy consumption per ton of coal is calculated based on the electricity consumption data and the production data. The actual energy consumption per ton of coal is compared with a preset standard energy consumption threshold. If the actual energy consumption exceeds the preset fluctuation range, the corresponding production data is marked as abnormal.

[0009] In one possible implementation, the cross-validation of the original data based on preset multi-dimensional verification rules includes: Obtain the inventory data and the sales data; The theoretical ending inventory data is calculated based on the inventory data and the sales data. The theoretical ending inventory data is compared with the actual ending inventory data. If the difference exceeds a preset threshold, the corresponding inventory data and / or sales data are marked as abnormal.

[0010] In one possible implementation, artificial intelligence models are used to collect the raw data and / or to cross-validate the raw data.

[0011] According to another aspect of this disclosure, a device for judging the authenticity of data reported by coal enterprises is provided, comprising: The data acquisition module is configured to synchronously acquire raw data from the business system at a preset period. The data verification module is configured to perform cross-verification on each of the original data based on preset multi-dimensional verification rules. The report generation module determines the authenticity of the reported data corresponding to the original data based on the verification results obtained from the cross-validation.

[0012] According to another aspect of this disclosure, a device for judging the authenticity of data reported by coal enterprises is provided, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method described in any of the above embodiments when executing the executable instructions.

[0013] According to another aspect of this disclosure, a non-volatile computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the method described in any of the above embodiments.

[0014] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: Based on enterprise business needs and data update frequency, a preset period, i.e., a data batch processing time interval, is determined. Multiple raw data sets from the business system are collected synchronously to enhance the dimensionality of the raw data, wherein the raw data comes from the management system of the corresponding business. By cross-validating each raw data set, the problems of data isolation and single-data verification are solved. The verification results obtained from cross-validation are used to characterize the logical consistency with the raw data, thereby enabling the verification of the corresponding reported data. Therefore, the embodiments of this disclosure, by synchronously collecting various raw data sets from the business system and performing cross-validation, eliminate the problem that single data sets are difficult to verify the authenticity of reported data due to data silos.

[0015] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0016] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0017] Figure 1 This is a flowchart of a method for judging the authenticity of data reported by coal enterprises according to an embodiment of this disclosure.

[0018] Figure 2 This is a flowchart demonstrating the authenticity of an embodiment of the present disclosure.

[0019] Figure 3 This is a flowchart of IoT verification according to an embodiment of the present disclosure.

[0020] Figure 4 This is a block diagram of a coal enterprise data reporting authenticity judgment system according to an embodiment of the present disclosure.

[0021] Figure 5 This is a block diagram of a coal enterprise data reporting authenticity judgment system according to an embodiment of the present disclosure.

[0022] Figure 6 This is a block diagram of a device for judging the authenticity of data reported by a coal enterprise according to an embodiment of the present disclosure.

[0023] Figure 7 This is a block diagram of a device for judging the authenticity of data reported by a coal enterprise according to an embodiment of the present disclosure. Detailed Implementation

[0024] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0025] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0026] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0027] In related technologies, coal enterprises primarily rely on manual reporting for key data such as inventory and production output in their daily operations and management. After manual reporting, enterprises have limited means to verify the authenticity of the data. Furthermore, the internal production, sales, and energy monitoring systems operate independently, preventing real-time data sharing and interaction, making it difficult to effectively assess data authenticity. For example, some technologies attempt to verify production data through manual checking of ledgers, but this method is inefficient and cannot comprehensively assess the correlation between energy consumption, inventory, and sales. Other technologies only perform simple verification on a single data source (such as production system output data), lacking cross-validation of multi-dimensional data, making it difficult to comprehensively and accurately determine the authenticity of the reported data.

[0028] In view of this, this disclosure provides a method, apparatus, equipment and storage medium for judging the authenticity of data reported by coal enterprises. Figure 1 This is a flowchart of a method for judging the authenticity of data reported by coal enterprises according to an embodiment of this disclosure, such as... Figure 1 As shown, the method for judging the authenticity of the data reported by this coal company includes the following steps: Step S1: Synchronously collect all raw data from the business system at a preset cycle.

[0029] Based on the enterprise's business needs and data update frequency, a preset period, i.e., the data batch processing time interval, is determined. Multiple raw data from the business system are collected synchronously to enhance the dimensionality of the raw data, which comes from the management system of the corresponding business.

[0030] Step S2: Based on the preset multi-dimensional verification rules, perform cross-validation on each original data.

[0031] By cross-validating the original data, the problems of data isolation and single validation were solved.

[0032] Step S3: Based on the verification results obtained from cross-validation, determine the authenticity of the reported data corresponding to the original data.

[0033] The verification results obtained by cross-validation are used to characterize the logical consistency with the original data, and can then be used to verify the corresponding reported data.

[0034] Therefore, this embodiment of the disclosure eliminates the problem that it is difficult to verify the authenticity of reported data due to data barriers by synchronously collecting various raw data from the business system and performing cross-validation.

[0035] It should be noted here that the preset period is determined based on the enterprise's business needs and data update frequency, or a corresponding preset period can be set separately for different business systems. For example, this disclosure does not specify a specific preset period for collecting daily, weekly, and monthly coal production and inventory data of each mine from the production system, performing fixed batch runs every morning, acquiring production data every six hours, or acquiring inventory data daily.

[0036] In some embodiments, raw data from various business systems are collected synchronously at a preset period, including: synchronously collecting raw data from different business systems at a preset period. That is, the raw data can come not only from the same business system, but also from different business systems, which not only further enhances the dimensionality of the raw data, but also improves the accuracy of judging the reported data by combining it with the actual operation of the enterprise.

[0037] The business system must include at least one of the following: a production management system, an energy management system, or a bulk commodity management system. The production management system is responsible for monitoring and managing various data generated from the daily production activities of the coal enterprise; that is, the raw data corresponding to the production management system must include at least one of the following: inventory data, production data, or ledger data for different coal types at each coal mine. The energy management system is responsible for monitoring and managing various data related to the energy consumed in the daily production activities of the coal enterprise; that is, the raw data corresponding to the energy management system must include at least electricity consumption data. The bulk commodity management system is responsible for monitoring and managing data generated from the daily bulk coal transactions of the coal enterprise; that is, the raw data corresponding to the bulk commodity management system must include at least sales data.

[0038] There are objective patterns among the raw data from different management systems. For example, higher electricity consumption corresponds to longer operating times of production equipment within a coal enterprise, resulting in higher output; conversely, lower coal inventory corresponds to more coal sold, leading to higher sales volume. Therefore, by synchronously collecting raw data from different business systems and cross-validating it, data barriers can be broken down, and the reported data can be verified.

[0039] The original data used for cross-validation includes at least two original data sets to verify the objective laws between the at least two original data sets. That is, cross-validation is performed on each original data set based on preset multi-dimensional verification rules.

[0040] In some embodiments, the preset multi-dimensional verification rules include empirical verification, IoT verification, and logical verification. Empirical verification verifies the most original evidence, IoT verification verifies the inherent physical laws of the production process, and logical verification verifies the basic business logic of enterprise operations.

[0041] Figure 2 This is a flowchart illustrating the verification of an embodiment of the present disclosure, as follows: Figure 2 As shown, the verification experiment involves cross-validating each original data point based on preset multi-dimensional verification rules, including the following steps: Step S211: Obtain production data and ledger data.

[0042] Production data can be obtained through the production management system and is manually recorded for reporting. Ledger data consists of manual records, meaning data handwritten by staff during the production process. Both production data and ledger data are from the same time period, i.e., within the same pre-set cycle.

[0043] Step S212: Perform optical character recognition on the image of the ledger data to obtain structured ledger records.

[0044] Optical character recognition (OCR) technology converts handwritten or printed information in ledger images into structured text data and parses out key fields such as "mine, working face, date, shift, and output".

[0045] Step S213: Compare the production data with the structured ledger records. If the information is inconsistent, mark the corresponding production data as abnormal.

[0046] The extracted structured ledger data is meticulously compared with the production data obtained from or reported by the production management system, line by line and field by field. If inconsistencies are found in key information such as quantity, time, or location, the production data is immediately marked as abnormal.

[0047] It should be noted that in some other embodiments of this disclosure, during step S213, the production data is compared with the structured ledger records. If the information is inconsistent, the relevant data of the corresponding production data is marked as abnormal. For example, the production data is an accurate value directly recorded by the production management system. The relevant data of the production data, that is, data directly related to the production data, such as the corresponding working hours of the staff and the corresponding power consumption, are also included.

[0048] Figure 3 This is a flowchart of IoT verification according to an embodiment of the present disclosure, such as... Figure 3 As shown, IoT verification involves cross-validating various original data based on preset multi-dimensional verification rules, including the following steps: Step S221: Obtain power consumption data and production data.

[0049] This involves acquiring total electricity consumption data recorded by the energy management system during the production process, as well as output data manually recorded by the production management system. The output data and electricity consumption data are from the same time period, i.e., within the same preset cycle.

[0050] Step S222: Calculate the actual energy consumption per ton of coal based on electricity consumption data and production data.

[0051] The actual energy consumption per ton of coal is calculated using the formula: "Actual energy consumption per ton of coal = Electricity consumption / Production".

[0052] Step S223: Compare the actual energy consumption per ton of coal with the preset standard energy consumption threshold. If it exceeds the preset fluctuation range, mark the corresponding production data as abnormal.

[0053] Those skilled in the art can set the standard energy consumption threshold based on experience or historical data, and this disclosure does not impose any specific limitations on it.

[0054] It should be noted that in some other embodiments of this disclosure, when performing step S223, the actual energy consumption per ton of coal is compared with a preset standard energy consumption threshold. If it exceeds a preset fluctuation range, the relevant data of the corresponding production data is marked as abnormal. For example, the production data is an accurate value directly recorded by the production management system. The relevant data of the production data, that is, the data directly related to the production data, such as the corresponding working hours of the staff and the corresponding power consumption, are also included.

[0055] Figure 3 This is a flowchart of logical verification according to an embodiment of the present disclosure, such as... Figure 3 As shown, logical verification involves cross-validating each original data point based on preset multi-dimensional verification rules, including the following steps: Step S231: Obtain inventory data and sales data.

[0056] Production data can be obtained through the production management system. The obtained production data is either manually recorded for reporting or directly recorded by the production management system.

[0057] Step S232: Calculate the theoretical ending inventory data based on inventory data and sales data.

[0058] The theoretical ending inventory is calculated using the formula: "Theoretical ending inventory = Initial inventory + Current period output - Current period sales - Reasonable loss".

[0059] Step S233: Compare the theoretical ending inventory data with the actual ending inventory data. If the difference exceeds the preset threshold, mark the corresponding inventory data and / or sales data as abnormal.

[0060] Those skilled in the art can set reasonable loss and difference thresholds based on experience or historical data, and this disclosure does not impose specific limitations on this.

[0061] It should be noted that in some other embodiments of this disclosure, when performing step S233, the theoretical ending inventory data is compared with the actual ending inventory data. If the difference exceeds a preset threshold, the related data of the corresponding inventory data and / or the related data of the sales data are marked as abnormal. For example, the inventory data is an accurate value directly recorded by the production management system, and the related data of the inventory data are data directly related to the inventory data, such as inbound and outbound records. The sales data is an accurate value directly recorded by the bulk sales management system, and the related data of the sales data are data directly related to the sales data, such as the company's net profit or total sales.

[0062] Therefore, using the method described in any of the above embodiments, after verifying the original data, a report on the marked abnormal data is generated. This involves collecting abnormal data, original data, and the calculation process during verification, compiling a report according to a preset template, including the verification time range, data source, details of the abnormal data, possible causes, and improvement suggestions, and pushing it to managers in a visual format via email through the enterprise management platform. In addition to the enterprise management platform and email, reports can also be pushed through internal enterprise instant messaging tools and mobile apps; this disclosure does not specifically limit the push channels.

[0063] In some embodiments of this disclosure, artificial intelligence models are used to collect raw data and / or perform cross-validation on the raw data to improve the efficiency of the processing. Specifically, artificial intelligence models are set up for different business systems and different multi-dimensional verification rules.

[0064] For example, Figure 4This is a block diagram of a coal enterprise data authenticity judgment system according to an embodiment of this disclosure, such as... Figure 4 As shown, the production verification agent acquires the reported production data and production ledger photos obtained by the production agent. The production ledger photos are converted using OCR to obtain structured data. Production data from different working faces in the same coal mine are compared item by item to verify information such as production time, shift, and output. If discrepancies in quantity or time are found, the production data is immediately flagged as problematic. The energy agent transmits the acquired electricity consumption data and the production data provided by the production agent to the production verification agent. The production verification agent calculates the energy consumption per unit output using the formula "energy consumption per ton of coal = electricity / output," and compares the calculation result with the standard limit. If the result exceeds a reasonable fluctuation range (e.g., exceeding the standard limit by 10%), the production data is flagged as abnormal. Based on the initial inventory data provided by the inventory intelligence agent, the current period's production data from the production intelligence agent, the current period's sales data from the bulk commodity intelligence agent, and the preset reasonable loss range, the theoretical ending inventory is calculated using the formula "Ending Inventory = Initial Inventory + Current Period Production - Current Period Sales - Losses (Reasonable Range)". This is compared with the actual reported ending inventory obtained by the production intelligence agent. If the difference exceeds a threshold (e.g., 5%), the relevant data is marked for further verification. The report generation intelligence agent collects abnormal data, raw data, and calculation processes during the verification process, compiles a report according to a preset template (including verification time range, data source, details of abnormal data, cause speculation, and improvement suggestions), and pushes it to managers in a visual format via email through the enterprise management platform.

[0065] By using multiple AI agents, i.e. AI models, to acquire independent data from multiple sources, and combining IoT, logic, and verification experiments, we can accurately identify problems such as human input errors and human tampering, thus avoiding data distortion.

[0066] Furthermore, the embodiments of this disclosure can also realize the flow and integration of system data such as production, sales, energy, and inventory, supporting multi-dimensional comprehensive analysis and reducing the difficulty of judging the authenticity of data. 3. It provides managers with reliable data support, avoiding economic losses caused by making plans based on erroneous data, and promoting the transformation of management from "experience-driven" to "data-driven". 4. It automatically runs batches to acquire data, performs multi-dimensional intelligent verification and report generation, reduces manual operation, improves data processing and verification efficiency, and enhances data transparency and traceability.

[0067] According to another aspect of this disclosure, a device for judging the authenticity of data reported by coal enterprises is provided. Figure 5 This is a block diagram of a device for judging the authenticity of data reported by coal enterprises according to an embodiment of this disclosure, such as... Figure 5As shown, the device includes: a data acquisition module 110, a data verification module 120, and a report generation module 130. The data acquisition module 110 is configured to synchronously acquire raw data from the business system at preset intervals. The data verification module 120 is configured to cross-validate the raw data based on preset multi-dimensional verification rules. The report generation module 130 is configured to determine the authenticity of the reported data corresponding to the raw data based on the verification results obtained from the cross-validation.

[0068] Furthermore, according to another aspect of this disclosure, a device 200 for judging the authenticity of data reported by coal enterprises is also provided. See also... Figure 6 The coal enterprise data authenticity judgment device 200 of this embodiment includes a processor 210 and a memory 220 for storing executable instructions of the processor 210. The processor 210 is configured to implement any of the aforementioned coal enterprise data authenticity judgment methods when executing executable instructions.

[0069] It should be noted here that the number of processors 210 can be one or more. Furthermore, the coal enterprise data reporting authenticity judgment device 200 in this embodiment may also include an input device 230 and an output device 240. The processors 210, memory 220, input device 230, and output device 240 can be connected via a bus or other means, which are not specifically limited here.

[0070] The memory 220, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and various modules, such as the program or module corresponding to the coal enterprise data authenticity judgment method in this embodiment of the present disclosure. The processor 210 executes various functional applications and data processing of the coal enterprise data authenticity judgment device 200 by running the software program or module stored in the memory 220.

[0071] Input device 230 can be used to receive input digital numbers or signals. These signals may include key signals related to user settings and function control of the device / terminal / server. Output device 240 may include a display device such as a screen.

[0072] According to another aspect of this disclosure, a non-volatile computer-readable storage medium is also provided, on which computer program instructions are stored, which, when executed by processor 210, implement the method for judging the authenticity of data reported by coal enterprises as described above.

[0073] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for judging the authenticity of data reported by coal enterprises, characterized in that, include: Collect raw data from the business system synchronously at a preset cycle; Based on preset multi-dimensional verification rules, cross-validation is performed on each of the original data. Based on the verification results obtained from the cross-validation, the authenticity of the reported data corresponding to the original data is determined.

2. The method for judging the authenticity of data reported by coal enterprises according to claim 1, characterized in that, The method of synchronously collecting various raw data from the business system at a preset period includes: The original data corresponding to different business systems are collected synchronously at the preset period.

3. The method for judging the authenticity of data reported by coal enterprises according to claim 1, characterized in that, The business system includes at least one of a production management system, an energy management system, or a bulk commodity management system; wherein... The raw data corresponding to the production management system includes at least one of the following: inventory data, production data, or ledger data for different coal types in each coal mine. The raw data corresponding to the energy management system includes at least electricity consumption data; The raw data corresponding to the bulk commodity management system includes at least sales data.

4. The method for judging the authenticity of data reported by coal enterprises according to claim 3, characterized in that, The method of cross-validating the original data based on preset multi-dimensional verification rules includes: Obtain the production data and the ledger data; Optical character recognition is performed on the image of the ledger data to obtain structured ledger records; The production data is compared with the structured ledger records. If the information is inconsistent, the corresponding production data is marked as abnormal.

5. The method for judging the authenticity of data reported by coal enterprises according to claim 3, characterized in that, The method of cross-validating the original data based on preset multi-dimensional verification rules includes: Obtain the power consumption data and the production data; The actual energy consumption per ton of coal is calculated based on the electricity consumption data and the production data. The actual energy consumption per ton of coal is compared with a preset standard energy consumption threshold. If the actual energy consumption exceeds the preset fluctuation range, the corresponding production data is marked as abnormal.

6. The method for judging the authenticity of data reported by coal enterprises according to claim 3, characterized in that, The method of cross-validating the original data based on preset multi-dimensional verification rules includes: Obtain the inventory data and the sales data; The theoretical ending inventory data is calculated based on the inventory data and the sales data. The theoretical ending inventory data is compared with the actual ending inventory data. If the difference exceeds a preset threshold, the corresponding inventory data and / or sales data are marked as abnormal.

7. The method for judging the authenticity of data reported by coal enterprises according to any one of claims 1 to 6, characterized in that, The original data are collected using artificial intelligence models, and / or the original data are cross-validated.

8. A device for judging the authenticity of data reported by coal enterprises, characterized in that, include: The data acquisition module is configured to synchronously acquire raw data from the business system at a preset period. The data verification module is configured to perform cross-verification on each of the original data based on preset multi-dimensional verification rules. The report generation module is configured to determine the authenticity of the reported data corresponding to the original data based on the verification results obtained from the cross-validation.

9. A device for judging the authenticity of data reported by coal enterprises, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 7 when executing the executable instructions.

10. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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