Coal data generation method, device and equipment based on Internet of Things platform

Abstract

The embodiment of the invention provides a coal data generation method, device and equipment based on an Internet of Things platform. The scheme comprises the steps of receiving an acquisition request sent by a user and used for acquiring coal data of a preset department; determining a plurality of departments capable of providing the coal data based on the acquisition dimension of the coal data; sending the acquisition request to each department; receiving each piece of initial coal data fed back by each department in response to the acquisition request; inputting the initial coal data and cue word information used for generating final coal data into a big language model, so that the big language model generates the final coal data based on the initial coal data according to thinking chain information in the cue word information; and feeding back the final coal data generated by the large language model to the user. According to the scheme, errors and potential malicious counterfeiting behaviors of the coal data can be avoided, so that the accuracy of the coal data can be improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent processing technology in the coal industry, and in particular to a method for generating coal data based on an Internet of Things platform. The present application also relates to a coal data generating device based on an Internet of Things platform, and a coal data generating equipment based on an Internet of Things platform. Background Art

[0002] During the coal mining process, multiple departments often need to work together, such as coal production, transportation, sales, and logistics. In order to formulate subsequent coal mining plans, decision makers and planners usually need each department to report its coal data.

[0003] Currently, when decision-makers receive coal data from a department, they often use these reports to formulate future production plans. However, due to personal errors or deliberate tampering, department personnel may report incorrect or false data. This directly impacts decision-makers' planning, leading to errors in production plans and ultimately affecting the efficiency and feasibility of the entire mining process.

[0004] Based on this, how to improve the accuracy of coal data has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] In view of this, embodiments of the present application provide a method, apparatus, and device for generating coal data based on an Internet of Things platform to solve the problem of poor accuracy of coal data.

[0006] According to a first aspect of an embodiment of the present application, a method for generating coal data based on an Internet of Things platform is provided, comprising: receiving an acquisition request sent by a user for obtaining coal data of a preset department; determining several departments that can provide the coal data based on acquisition dimensions of the coal data, the acquisition dimensions comprising at least one of a production dimension, a transportation dimension, a sales dimension, and a logistics dimension; sending the acquisition request to each of the several departments; receiving each initial coal data fed back by the each department in response to the acquisition request; inputting each initial coal data and prompt word information for generating final coal data into a large language model, so that the large language model generates final coal data based on each initial coal data according to the thinking chain information in the prompt word information; the thinking chain information is used to reflect the logical steps of the large language model in generating final coal data based on each initial coal data; and feeding back the final coal data generated by the large language model to the user.

[0007] According to the second aspect of the embodiment of the present application, a coal data generation device based on an Internet of Things platform is provided, including: an acquisition request receiving module for receiving an acquisition request sent by a user for obtaining coal data of a preset department; a department determination module for determining, based on the acquisition dimension of the coal data, several departments that can provide the coal data, the acquisition dimension including at least one of a production dimension, a transportation dimension, a sales dimension and a logistics dimension; an acquisition request sending module for sending the acquisition request to each of the several departments; a coal data receiving module for receiving each initial coal data fed back by the each department in response to the acquisition request; an input module for inputting the each initial coal data and prompt word information for generating final coal data into a large language model, so that the large language model generates final coal data based on the each initial coal data according to the thinking chain information in the prompt word information; the thinking chain information is used to reflect the logical steps of the large language model in generating final coal data based on the each initial coal data; a coal data feedback module for feeding back the final coal data generated by the large language model to the user.

[0008] According to a third aspect of an embodiment of the present application, a coal data generating device based on an Internet of Things platform is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: receive an acquisition request sent by a user for obtaining coal data of a preset department; determine several departments that can provide the coal data based on the acquisition dimensions of the coal data, the acquisition dimensions including at least one of a production dimension, a transportation dimension, a sales dimension and a logistics dimension; send the acquisition request to each of the several departments; receive each initial coal data fed back by the each department in response to the acquisition request; input the each initial coal data and prompt word information for generating final coal data into a large language model, so that the large language model generates final coal data based on the each initial coal data according to the thinking chain information in the prompt word information; the thinking chain information is used to reflect the logical steps of the large language model in generating final coal data based on the each initial coal data; and feed back the final coal data generated by the large language model to the user.

[0009] At least one embodiment of this specification can achieve the following beneficial effects: when coal data from a specific department is needed, multiple relevant departments can each provide their own determined initial coal data. Each determined initial coal data is then analyzed and processed by a large language model, which then generates the final coal data based on this initial coal data. In this way, the coal data required by the user is not provided by a single department, but rather by multiple departments working together to provide it. This avoids errors in the coal data and potential malicious falsification, thereby improving the accuracy of the coal data. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0011] Figure 1 This is a flow chart of a method for generating coal data based on an Internet of Things platform provided in an embodiment of this specification; Figure 2 is a schematic diagram of the shape of a first coal pile in a carriage of a transport vehicle provided in an embodiment of this specification; Figure 3 is a schematic diagram of the shape of a second coal pile in a carriage of a transport vehicle at a first moment provided in an embodiment of this specification; Figure 4 is a schematic diagram of the shape of a third coal pile in a carriage of a transport vehicle at a second moment provided in an embodiment of this specification; Figure 5 is a schematic diagram of the shape of a fourth coal pile in a carriage of a transport vehicle at a third moment provided in an embodiment of this specification; Figure 6 This is a schematic diagram of the structure of a coal data generation device based on the Internet of Things platform provided in an embodiment of this specification; Figure 7 This is a structural diagram of a coal data generation device based on the Internet of Things platform provided in an embodiment of this specification. DETAILED DESCRIPTION

[0012] The following description sets forth many specific details to facilitate a thorough understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0013] The terms used in one or more embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of the present application. The singular forms "a", "the" and "the" used in one or more embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of the present application refers to and includes any or all possible combinations of one or more associated listed items.

[0014] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of the present application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of one or more embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0015] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0016] Currently, coal production decision-makers may report erroneous or false data to a department due to personal errors or deliberate manipulation. This can directly impact their planning, leading to errors in production plans and ultimately impacting the efficiency and feasibility of the entire coal mining process.

[0017] The technical solutions provided by the embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0018] Figure 1 This is a flow chart of a method for generating coal data based on an Internet of Things platform provided in an embodiment of this specification.

[0019] From a program perspective, the execution body of the process can be a program installed on the coal data generation device. It is understood that the method can be executed by any device, equipment, platform, or device cluster with computing and processing capabilities.

[0020] like Figure 1 As shown, the process may include the following steps.

[0021] Step 102: Receive an acquisition request sent by a user for acquiring coal data of a preset department.

[0022] In the embodiments of this specification, the IoT platform may include a main agent and multiple sub-agents, each of which corresponds to a department and communicates with the sensor devices and controller devices in the production process responsible for that department, each of which has independent networking capabilities. The sub-agents can generate control instructions based on the operational data collected by the sensor devices and send these instructions to the corresponding controllers to perform the corresponding operations. For example, a sub-agent associated with the coal production department can control the mine's fresh air system to perform corresponding operations based on the sensor data monitoring air quality in the mine. In addition, the sub-agents can also generate corresponding coal data based on the operational data collected by the sensor devices, such as calculating the weight of coal based on the conveying rate of the coal conveyor belt. The main agent then performs overall production planning for the coal production process by aggregating the operational data reported by each sub-agent. In actual applications, the main agent and each sub-agent can be configured with a large language model system, or the main agent and each sub-agent can call on the corresponding large model to perform tasks. This is not limited to this.

[0023] In the examples of this specification, users may refer to decision-makers in the coal production process. Predefined departments may include coal generation, coal transportation, coal logistics, and coal sales. Coal data may refer to various types of coal-related data involved in the coal production process, such as coal weight data, coal combustion performance data, coal mining data, coal transportation data, and coal inventory data.

[0024] Step 104: Based on the acquisition dimension of the coal data, determine several departments that can provide the coal data, where the acquisition dimension includes at least one of a production dimension, a transportation dimension, a sales dimension, and a logistics dimension.

[0025] In the embodiments of this specification, an acquisition dimension may refer to a dimension of a department that can obtain the coal data. Based on the acquisition dimension, several departments that can provide the coal data are determined. The acquisition dimension includes at least one of a production dimension, a transportation dimension, a sales dimension, and a logistics dimension. The production dimension may refer to a dimension that determines the coal data from a production perspective, the transportation dimension may refer to a dimension that determines the coal data from a transportation perspective, the sales dimension may refer to a dimension that determines the coal data from a sales perspective, and the logistics dimension may refer to a dimension that determines the coal data from a logistics perspective.

[0026] Step 106: Send the acquisition request to each of the several departments.

[0027] In the embodiments of this specification, each department can be provided with a corresponding intelligent entity, for example, the coal production department is provided with a coal production intelligent entity, the coal transportation department is provided with a coal transportation intelligent entity, the coal sales department is provided with a coal sales intelligent entity, and the coal logistics department is provided with a coal logistics intelligent entity.

[0028] In actual applications, the Internet of Things platform can be equipped with a main intelligent agent, which is used to receive acquisition requests sent by users. The main intelligent agent can send the acquisition requests sent by users to the corresponding intelligent agents of each department, so that each department can determine the corresponding initial coal data based on its own intelligent agent.

[0029] In actual applications, a communication connection can be set up between the main intelligent agent and the sub-intelligent agents of each department. Based on the communication connection, the main intelligent agent can send instructions to each sub-intelligent agent, and each sub-intelligent agent can respond to the instruction and feedback the response result to the main intelligent agent.

[0030] Step 108: Receive the various initial coal data fed back by the various departments in response to the acquisition request.

[0031] In the embodiments of this specification, each department can determine initial coal data in response to a request using various methods. One method involves leveraging an agent associated with the department, in conjunction with sensor devices, to generate initial coal data based on collected operational data. In practical applications, operational data collected by various sensor devices includes, but is not limited to, the operating status of coal mining equipment, coal flow during transportation, temperature and humidity in coal storage, and physical properties of the coal.

[0032] In practical applications, sensor devices can exchange data with intelligent agents in real time or periodically through a communication network. A stable communication connection can be established between the intelligent agent and the sensor devices, for example, through wireless communication, optical fiber, or other network technologies, to ensure real-time data transmission and reception.

[0033] In practical applications, sensor devices can proactively transmit collected operational data to the intelligent agent, or, based on the agent's instructions, feed back collected data at preset intervals or event triggers. The intelligent agent can further analyze and process this data, combining it with specific algorithmic models to generate initial coal data that aligns with actual conditions. This allows departments to accurately and promptly determine initial coal data, while ensuring data accuracy and reliability.

[0034] Step 110: Input the various initial coal data and the prompt word information used to generate the final coal data into the large language model, so that the large language model generates the final coal data based on the various initial coal data according to the thought chain information in the prompt word information; the thought chain information is used to reflect the logical steps of the large language model to generate the final coal data based on the various initial coal data.

[0035] In the embodiments of this specification, the initial coal data generated by each agent is input into a large language model, where the large language model may be a pre-trained model. The prompt information may be instruction information input into the large language model to instruct the large language model to generate final coal data based on the input initial coal data, such as "Please refer to the input initial coal data to generate final coal data." In the embodiments of this specification, the prompt word information can include a chain of thought information. Carrying this chain of thought information in the prompt word information allows the large language model to process related tasks based on the chain of thought information in the prompt word information and according to the logical steps reflected in the chain of thought information. Chain of Thought (COT) is a method that generates more accurate and coherent output by guiding the model through step-by-step reasoning. Traditional prompt word design typically directly presents a question or task, and the model generates output information based on the input. However, this approach often ignores the intermediate steps in the model's reasoning process, resulting in inaccurate or illogical output information. The core concept of COT is to guide the model to think along a logical chain by introducing a series of step-by-step reasoning steps in the prompt. This allows the model to not only generate the final answer but also demonstrate the reasoning process, thereby improving the accuracy and interpretability of the output. In the embodiments of this specification, the prompt word used can include information reflecting the chain of thought, i.e., chain of thought prompt information.

[0036] In practical applications, thought chain information can be used to reflect the logical steps of a large language model to generate final coal data based on various initial coal data.

[0037] In order to facilitate those skilled in the art to understand the present solution, this specification provides a specific example for prompt word information.

[0038] The prompt word information may include ##task description and ##COT, wherein ##COT may include step 1, step 2 and step 3.

[0039] ##Task description: Please combine the initial coal data to generate the final coal data.

[0040] ##COT: Step 1: Determine the department information corresponding to each initial coal data and the method description for producing the data.

[0041] Step 2: Based on the department information and method description, determine the weight value corresponding to the initial coal data; Step 3: Based on the initial coal data and the weight values, the final coal data is generated by weighted summation.

[0042] In this embodiment, after receiving the initial coal data from various departments, the large language model generates the final coal data based on the user's prompts and the logical steps reflected in the thought chain. This process of the large language model goes beyond simply merging data. Instead, it comprehensively analyzes and infers the initial data to ensure the generated data is highly accurate, comprehensive, and consistent.

[0043] Specifically, the large language model first preprocesses the initial coal data to ensure uniformity, clarity, and operability. This process may involve data cleaning, standardization, and missing value handling to ensure comparability and validity. The large language model then gradually derives the final coal data based on user needs and the logical steps reflected in the thought chain.

[0044] Optionally, the method of generating the final coal data based on the initial coal data may include: determining the weight value corresponding to the initial coal data, the larger the weight value, the greater the impact of the initial coal data on the final result of the final coal data; and determining the final coal data by weighted summation based on the initial coal data and the weight value.

[0045] In the embodiment of this specification, the initial coal data may carry relevant department information and a description of the method for generating the data. The large language model can determine the authenticity of the initial coal data based on the department information carried in the initial coal data. For example, if the initial coal data is coal weight data, the initial coal data a is determined by the coal production department, and the initial coal data b is determined by the coal sales department. If the initial coal data a is greater than the initial coal data b, and the difference between the two is less than the set threshold a, the large model can analyze that the authenticity of both sets of data is high. The reason is that the initial coal data a is confirmed by the coal production department, and the initial coal data b is confirmed by the coal sales department. During the transportation of coal from the production department to the sales department, some coal slag may fall, resulting in the initial coal data b being lower than the initial coal data a. Moreover, the difference between the two is still within the preset reasonable range, so it can be considered that the authenticity of the two data is relatively high.

[0046] If the initial coal data a is smaller than the initial coal data b, the large model can analyze that the authenticity of these two sets of data is relatively poor. This is because the initial coal data a is confirmed by the coal production department, and the initial coal data b is confirmed by the coal sales department. During the transportation of coal from the production department to the sales department, some coal slag may fall, causing the initial coal data b to be lower than the initial coal data a. However, the initial coal data a is smaller than the initial coal data b, so the authenticity of these two data can be considered relatively poor.

[0047] In the embodiments of this specification, the accuracy of the initial coal data can also be determined based on the method description of generating coal data carried in the initial coal data. For example, if the initial coal data is coal weight data, where the initial coal data a is determined by the conveying rate of the conveyor belt, and the initial coal data b is determined by the weighing equipment, then the accuracy of the initial coal data b is relatively high, and the accuracy of the initial coal data a is relatively low.

[0048] In the embodiments of this specification, the large language model can determine the weight value corresponding to each initial coal data based on the relevant department information contained in the initial coal data and the method description for generating the data. The larger the weight value, the greater the influence of the corresponding initial coal data on the final coal data, and the sum of the weight values is unity. The final coal data is determined by weighted summation of the initial coal data and weight values.

[0049] For example, assume there are four initial coal data sets: initial coal data 1, initial coal data 2, initial coal data 3, and initial coal data 4. The weight corresponding to initial coal data 1 is 30%, the weight corresponding to initial coal data 2 is 20%, the weight corresponding to initial coal data 3 is 10%, and the weight corresponding to initial coal data 4 is 40%. The final coal data is then determined as = initial coal data 1 × 30% + initial coal data 2 × 20% + initial coal data 3 × 10% + initial coal data 4 × 40%.

[0050] Step 112: Feedback the final coal data generated by the large language model to the user.

[0051] In the embodiments of this specification, the final coal data output by the large language model is sent to the user's terminal device; or a device loaded with the large language model is used to display the final coal data to the user.

[0052] It should be understood that the order of some steps in the methods described in one or more embodiments of this specification can be interchanged according to actual needs, or some steps can be omitted or deleted.

[0053] Figure 1 In the method, the coal data required by users is not provided by a single department, but by multiple departments in a collaborative manner, thereby avoiding errors in coal data and potential malicious falsification, and thus improving the accuracy of coal data.

[0054] based on Figure 1 The present specification also provides some specific implementation methods of the method, which are described below.

[0055] In an optional embodiment of the present specification, the coal data may include coal weight data, and the several departments may include a coal production department; the way in which the coal production department generates the initial coal data may include: obtaining the conveying rate of the conveyor belt used to convey coal, which is calculated by the coal production department, and the conveyor belt is a belt that transports the mined coal to the coal transport vehicle; based on the conveying rate, estimating the weight data.

[0056] In the embodiments of this specification, the weight data of coal may be a numerical value used to measure the weight or quantity of coal, typically in tons or kilograms, and represents the actual weight of coal during transportation, sales, production, etc. The coal production department may be an organization or institution specifically responsible for coal mining, processing, and production.

[0057] In the embodiments of this specification, the conveyor belt may be a belt used to transport coal from an underground location to an above-ground location. Specifically, the conveyor belt may be a belt used to transport coal mined by coal mining equipment from an underground mine to a coal transport vehicle.

[0058] In the embodiments of this specification, the conveying rate of the conveyor belt for conveying coal may represent the weight of coal conveyed by the conveyor belt in unit time.

[0059] Optionally, the method of determining the conveying rate of the conveyor belt may include: obtaining the target coal conveyed by the conveyor belt within a preset time period; measuring the weight value of the target coal using a weighing device; and determining the conveying rate of the conveyor belt based on the ratio between the weight value and the preset time period.

[0060] In actual applications, the conveying rate of the conveyor belt can be a fixed value within a preset time period, or it can be a variable value within the preset time period. For example, at time t1 in the preset time period, the conveying rate is v1, and at time t2, the conveying rate can be either v1 or v2.

[0061] In the embodiments of this specification, the conveyor belt's conveying speed is affected by the coal mining environment. In practical applications, if the mining environment is favorable, such as soft geology and high mining efficiency, the conveyor belt's conveying speed can be increased. Conversely, if the mining environment is poor, such as hard geology and low mining efficiency, the conveyor belt's conveying speed needs to be reduced. With this configuration, when coal mining efficiency is high, the conveyor belt's conveying efficiency can be increased to quickly transport the mined coal, thereby avoiding safety hazards caused by coal accumulation. When coal mining efficiency is low, the conveyor belt's conveying speed can be reduced to load more coal onto the belt, thereby improving the conveyor belt's utilization rate.

[0062] In the embodiments of this specification, the conveyor belt's conveying rate is affected by the number of idle transport vehicles. In practical applications, if there are many idle transport vehicles and the coal transport department has strong transport capacity, the conveyor belt's conveying rate can be increased. Conversely, if there are few idle transport vehicles and the coal transport department has weak transport capacity, the conveyor belt's conveying rate should be reduced. With this configuration, when there are many idle transport vehicles, the conveyor belt's conveying rate can be increased to quickly transport the mined coal, thereby improving coal transportation efficiency. When there are fewer idle transport vehicles, the conveyor belt's conveying rate should be reduced to prevent coal from accumulating due to inability to transport it in a timely manner, thereby reducing safety risks.

[0063] It's important to note that the conveyor belt's conveying speed is influenced by both the mining environment and the number of idle transport vehicles. In practice, if the mining environment is favorable, the degree to which the conveyor belt speed increases will be affected by the number of idle transport vehicles. Conversely, if the mining environment is poor, the degree to which the conveyor belt speed decreases will also be affected by the number of idle transport vehicles. Conversely, if there are many idle transport vehicles, the degree to which the conveyor belt speed increases will also be affected by the mining environment; if there are few idle transport vehicles, the degree to which the conveyor belt speed decreases will also be affected by the mining environment.

[0064] In the embodiment of this specification, the conveying speed of the conveyor belt is dynamically adjusted according to the actual situation. In order to reduce the storage of a large amount of conveying speed data and save data storage resources, when the difference between different conveying speeds is less than a second preset threshold, these speeds can be regarded as the same conveying speed.

[0065] Optionally, the method of determining the conveying rate of the conveyor belt may include: obtaining a first conveying rate of the conveyor belt at a first moment; obtaining a second conveying rate of the conveyor belt at a second moment, the first moment and the second moment being two adjacent moments; judging whether the difference between the first conveying rate and the second conveying rate is less than or equal to a second preset threshold, and obtaining a judgment result; if the judgment result indicates that the difference is less than or equal to the second preset threshold, then the conveying rate of the conveyor belt at the first moment and the conveying rate of the conveyor belt at the second moment are both determined as the first conveying rate, or the conveying rate of the conveyor belt at the first moment and the conveying rate of the conveyor belt at the second moment are both determined as the second conveying rate.

[0066] In the embodiments of this specification, since the conveyor belt's conveying rate can dynamically change based on actual scenarios, when calculating the weight of coal transported by the conveyor belt within a preset time period based on the conveying rate, it is first necessary to determine the corresponding conveying rates within the time period. Then, based on the time interval corresponding to each conveying rate, the weight of coal transported by the conveyor belt within the preset time period is calculated. For example, assuming that the weight of coal transported by the conveyor belt from time t1 to time t5 needs to be determined, if the conveying rate from time t1 to time t2 (time period T1) is v1 and the conveying rate from time t3 to time t5 (time period T2) is v2, then the calculated coal weight M1 = v1 × T1 + v2 × T2.

[0067] In the embodiment of this specification, since the delivery rate during time period T1 is set to v1 and the delivery rate during time period T2 is set to v2, the delivery rate data stored from time t1 to time t5 can be reduced, and the amount of calculation required to calculate the coal weight can also be reduced. Assuming that a delivery rate is stored for each time period from t1 to time t5, for example, the delivery rates stored from time t1 to time t5 are v1 to v5, the calculated coal weight M2 = v1 × t1 + v2 × t2 + v3 × t3 + v4 × t4 + v5 × t5. It can be seen that the amount of calculation required for M2 is much greater than that required for M1.

[0068] In an optional embodiment of the present specification, the coal data may include coal weight data, and the several departments may include a coal transportation department; the way in which the coal transportation department generates the initial coal data may include: obtaining the coal volume of coal transported by the transport vehicle used to transport coal, which is counted by the coal transportation department, and the coal volume is the volume counted in the carriage of the transport vehicle; based on the coal volume, estimating the weight data.

[0069] In the embodiment of this specification, the coal transportation department may be a department responsible for the transportation and management of coal from the mining site to the final consumption site, and the coal volume may be the volume measured for all the coal loaded on the transportation vehicle.

[0070] Optionally, the method of measuring the volume of coal may include: obtaining a distance value measured by a distance meter installed in a carriage of a transport vehicle, wherein the distance meter is installed on the inner top surface of the carriage, or the distance meter is installed on the inner side surface of the carriage; the distance value is the distance between the top of the carriage and the surface of the coal; and calculating the volume of the coal in the carriage based on the length value, width value, height value and the distance value of the carriage.

[0071] In the embodiments of this specification, to improve the accuracy of the calculated coal volume and, therefore, the estimated coal weight, it is generally necessary to ensure that the coal in the carriage forms a regular shape, such as a cuboid or cube. Therefore, when loading the coal, how to ensure that the coal forms a regular volume shape in the carriage is also a key issue that needs to be considered.

[0072] Currently, when conveyor belts load coal onto transport vehicle carriages, the discharge port is typically positioned toward the center of the carriage. The resulting coal pile often has an irregular shape, for example, with a higher center and a slope from the center to the perimeter. Consequently, when calculating the volume of the coal pile within the carriage, the resulting volumetric data may contain certain errors and be less accurate.

[0073] For example, Figure 2 Schematic diagram of the shape of the first coal pile in the carriage of the transport vehicle provided in the embodiment of this specification. Figure 2 As shown, the unloading port 203 of the conveyor belt is aligned with the middle position of the carriage 202 of the transport vehicle for coal loading. The loaded coal pile 201 presents an irregular shape with a bulge in the middle and a slope from the middle to the surrounding parts. Figure 2 When calculating the volume of a coal pile based on the shape of the coal pile in the image, the accuracy of the calculated volume of the coal pile is relatively poor because the coal pile does not have a regular shape.

[0074] In the embodiments of this specification, in order to overcome the problem of poor accuracy of the calculated volume of the coal pile, during the process of loading the coal, it is necessary to start loading from the front end of the carriage and control the vehicle to move at a predetermined rate to ensure that the coal can be evenly filled into the carriage, so that a coal pile of regular shape can be obtained, thereby improving the accuracy of the calculated volume of the coal pile.

[0075] Optionally, during the coal loading process, the volume of the carriage of the transport vehicle is obtained; a first conveying rate of the conveyor belt is obtained, the first conveying rate being used to reflect the weight of the coal conveyed by the conveyor belt per unit time; based on the conversion relationship between coal weight and coal volume, the first conveying rate is converted into a second conveying rate being used to reflect the volume of the coal conveyed by the conveyor belt per unit time; based on the ratio between the carriage volume and the second conveying rate, a loading time is determined to reflect the time required to fill the carriage; the carriage length of the carriage is obtained; based on the ratio of the carriage length to the loading time, a vehicle movement rate required to be set when loading the coal into the carriage is determined; and based on the vehicle movement rate, coal is loaded into the carriage. In this way, the loaded coal can form a regular volume shape in the carriage, thereby improving the accuracy of the calculated coal volume, and thereby improving the accuracy of the coal weight data estimated based on the coal volume.

[0076] In order to facilitate those skilled in the art to understand the present solution, a specific example of loading coal in a carriage is also proposed in the embodiments of this specification.

[0077] In practical applications, the second conveying rate of the conveyor belt for loading coal into the carriage is first determined. The second conveying rate can be used to reflect the volume of coal transported by the conveyor belt per unit time. Assume that the second conveying rate is Vn; then the carriage volume M of the carriage is determined; and the ratio between the carriage volume M and the second conveying rate Vn is used to determine the time T required to fill the carriage at the second conveying rate Vn, that is, the loading time for loading the carriage is determined; then the carriage length value L of the carriage is obtained; the moving speed v of the carriage moving a distance L within the time T is predicted; finally, the unloading port of the conveyor belt is aligned with the front end of the carriage, and coal is started to be loaded. During the loading process, the carriage moves forward at a speed v so that the unloading port of the conveyor belt can load different positions of the carriage within the time T. In this way, according to the loading method of the above example, a relatively standard rectangular coal pile can be obtained after the loading is completed.

[0078] For example, Figure 3 It is a schematic diagram of the shape of the second coal pile in the carriage of the transport vehicle at the first moment provided in the embodiment of this specification.

[0079] like Figure 3 As shown, assuming that the above time T includes moments t1, t2 and t3, the first moment may be moment t1. At moment t1, the unloading port 302 is loaded at the front end of the carriage. During the loading process, one end of the coal pile is close to the front side wall of the carriage, and the other end of the coal pile is in a slope shape. The shape of the coal pile after loading is coal pile 301.

[0080] For example, Figure 4 It is a schematic diagram of the shape of the third coal pile in the carriage of the transport vehicle at the second moment provided in an embodiment of this specification.

[0081] like Figure 4 As shown, the second moment may be moment t2 included in the above-mentioned time T. At moment t2, the unloading port 402 is loaded at the middle position of the carriage. During the loading process, the loaded coal may fill the slope of the coal pile formed at moment t1 and form a new slope at the other end. The shape of the coal pile after loading is coal pile 401.

[0082] For example, Figure 5 It is a schematic diagram of the shape of the fourth coal pile in the carriage of the transport vehicle at the third moment provided in the embodiment of this specification.

[0083] like Figure 5 As shown, the third moment may be the moment t3 included in the above time T. At the moment t3, the unloading port 502 is loaded at the rear end of the carriage. During the loading process, the coal can fill the remaining space of the carriage. The shape of the coal pile after loading is the coal pile 501.

[0084] pass Figure 3 The coal pile in 301, Figure 4 Coal pile 401 and Figure 5 The schematic diagram of the shape of the overall coal pile formed by the coal pile 501 in the figure shows that the above method is used to load the coal in the moving process of the carriage, and the coal pile formed after stacking can form a basic standard rectangular parallelepiped shape. Figure 2 The coal pile 201 in the vehicle is loaded with coal while moving the carriage, which can greatly improve the accuracy of calculating the volume of the coal pile using the above-mentioned method.

[0085] Optionally, the conversion relationship between coal weight and coal volume can be determined by the following steps: first, use a weighing device to weigh a unit weight of coal, such as 1 ton of coal; then, put the unit weight of coal into a regularly shaped container, such as a rectangular container; then, use a preset tool to level the surface of the coal to a horizontal plane, such as using a scraper to scrape the surface of the coal flat; next, measure the height of the coal in the container; calculate the volume of the coal based on the length, width and height of the container; finally, combine the weighed coal weight and the calculated coal volume to determine the conversion relationship between coal weight and coal volume, for example: xxx tons of coal equals xxx cubic meters of coal volume.

[0086] In the embodiments of this specification, during each coal transportation process, the volume of coal transported by the transport vehicle can be determined according to the above method. After the coal volume is determined, the data will be stored in the storage device. The coal transportation department can determine the total volume of coal transported in the corresponding time period based on the timestamp information carried in the acquisition request issued by the user for obtaining coal data. Then, the density of the sampled coal stored in the time period is obtained from the storage device, and the weight of the coal transported in the time period is estimated based on the determined total coal volume and coal density. Specifically, if there are multiple sampled coal densities stored for the time period in the storage device, the sub-time period corresponding to each sampled coal density is determined, and then the coal sub-volume corresponding to each sub-time period is determined. Based on each coal sub-volume and each sampled coal density, the weight of the coal transported in the time period is estimated.

[0087] In an optional embodiment of the present specification, the coal data may include coal weight data, and the several departments may include a coal logistics department; the way in which the coal logistics department generates the initial coal data may include: obtaining equipment consumption data for coal mining equipment compiled by the coal logistics department, the equipment consumption data including at least one of a first consumption of coal mining drill bits consumed by the coal mining equipment in the process of coal mining, a second consumption of industrial water consumed, and a third consumption of industrial electricity consumed; and estimating the weight data based on the equipment consumption data.

[0088] In the embodiments of this specification, the coal logistics department may be a department for managing energy consumption in coal mining, such as consumable parts, electricity, and water resources involved in the mining process.

[0089] In practical applications, the equipment wear data may be the resource data consumed by the coal mining equipment in the process of coal mining, wherein the equipment wear data may include the first consumption of the mining drill bit consumed by the coal mining equipment. In the process of coal mining, the mining drill bit needs to continuously rub against the coal seam and rock. This friction will cause the surface of the drill bit to gradually wear, especially when mining in rock formations with higher hardness. The wear rate of the drill bit will accelerate, and thus a certain amount of mining drill bits will be consumed during the mining process.

[0090] In practical applications, equipment wear and tear data can also include a secondary consumption of industrial water used by coal mining equipment. During the coal mining process, industrial water is used to cool mine equipment, particularly machinery, ventilation systems, and power facilities. The operation of a large number of mechanical equipment during coal mining generates significant heat, and water effectively helps maintain this equipment at a suitable operating temperature. Furthermore, industrial water is used to lubricate mining equipment, reducing wear and tear. Therefore, a certain amount of industrial water is consumed during the coal mining process.

[0091] In actual applications, equipment consumption data can also include the third consumption of industrial electricity consumed by coal mining equipment. During the coal mining process, the coal mining equipment involved, such as coal mining machines, tunneling machines, drilling rigs and other equipment, will consume a certain amount of industrial electricity.

[0092] In the embodiments of this specification, during the coal mining process, equipment wear data can be used to estimate the weight of mined coal. In practical applications, a correlation between equipment wear data and the weight of mined coal can be established. For example, by calculating drill bit wear and the weight of mined coal over a specific time period, a corresponding relationship between coal weight and drill bit wear can be established. Similarly, a correlation between coal weight and industrial water consumption can be established, as well as a correlation between coal weight and industrial electricity consumption.

[0093] In the embodiments of this specification, a pre-established correlation between equipment wear and tear data and coal weight can be used to determine whether the weight of coal mined during a specific time period is consistent with the equipment wear and tear consumed. If the weight of coal mined during that time period does not correspond to an appropriate amount of equipment wear and tear, the system can generate an alarm, prompting the user to perform equipment inspections.

[0094] In practical applications, the equipment wear value associated with the coal weight can be set as a standard wear value. For a predetermined weight of coal mined, if the actual equipment wear value exceeds the standard wear value—for example, if the mining equipment's actual power consumption exceeds the standard wear value—a first alarm is generated. Users can use this alarm to check the equipment's status, such as whether the drill bit is severely worn. This allows them to identify any equipment faults and improve the safety of the coal mining process. If the actual equipment wear value falls below the standard wear value, for example, if the mining equipment's actual water consumption falls below the standard wear value, a second alarm is generated. This alarm is used to check whether the equipment is operating properly. If so, the preset standard water consumption can be appropriately lowered. This adjustment process allows workers to realize that, under current operating conditions, using less water than the standard consumption can still ensure the equipment remains in normal operation. This allows for reduced industrial water consumption when mining the same weight of coal, thereby improving water utilization efficiency.

[0095] In an optional embodiment of the present specification, the coal data may include coal weight data, and the several departments may include a coal sales department; the way in which the coal sales department generates the initial coal data may include: obtaining the coal weight information measured by the coal sales department based on weighing equipment during the warehousing process; and estimating the weight data based on the coal weight information.

[0096] In the embodiment of this specification, the coal sales department can be equipped with a warehouse for storing coal, and a load-bearing device can be installed in the warehouse to measure the weight of the coal. Before the coal enters the warehouse, it must first be weighed by the load-bearing device to accurately record the weight of the coal.

[0097] It should be noted that the transport vehicles transporting coal to the warehouse can be equipped with coal identification information. This information can record relevant data about the transport of coal from the coal production department to the warehouse, such as the coal mining time, transportation time, and transport vehicle information. After obtaining the weight of the coal through the weighing equipment, the coal identification information can also be collected to establish a correlation between the coal identification information and its weight. In this way, when the coal sales department subsequently inquires about the weight of the coal stored in the warehouse, it can use the time data in the coal identification information to accurately determine the corresponding coal weight.

[0098] In the embodiment of this specification, the coal weight determined by the coal sales department is based on data actually measured by the weighing equipment, so that the coal weight data provided by the coal sales department can be data with high accuracy.

[0099] In the embodiments of this specification, when a user needs coal data, this data is not provided by a single department, but requires the collaborative work of multiple departments. For example, when a user wants to obtain the coal weight data of the coal production department, it is not provided by the coal production department alone, but involves the joint participation of the coal production department, coal transportation department, coal logistics department and coal sales department. And each department provides data from a different perspective: the coal production department generates coal weight data through the conveying rate of the coal conveyor belt; the coal transportation department generates coal weight based on the volume data of the coal in the carriage; the coal logistics department estimates the coal weight through the energy consumption data of the equipment; and the coal sales department generates coal weight data based on the test results of the weighing equipment. In this way, the large language model can integrate data from multiple dimensions, perform intelligent analysis, and ultimately generate accurate coal data. This method avoids the errors and potential malicious falsification that may be caused by data provided by a single department, thereby improving the accuracy of coal data.

[0100] In an optional embodiment of the present specification, before inputting the initial coal data and the prompt word information used to generate the coal data into the large language model, the method may also include: obtaining a training sample set, wherein a training sample in the training sample set includes an initial coal data set sample and label result data corresponding to the initial coal data sample sample; inserting the initial coal data set sample into a prompt word template containing thought chain information to obtain sample prompt word information; the thought chain information is used to represent the logical information for generating the final coal data based on the initial coal data set sample; inputting the sample prompt word information into a pre-trained large language model to obtain the prediction result data generated by the large language model according to the logical information; and fine-tuning the large language model based on the difference between the prediction result data and the label result data to obtain a trained large language model.

[0101] In an embodiment of the present specification, the training sample set may be data obtained from a database, and each training sample may include an initial coal data set sample composed of multiple initial coal data, and label result data corresponding to the initial coal data set sample, wherein the label result data may be standard final coal data determined based on each initial coal data in the initial coal data set sample.

[0102] In the embodiment of this specification, the prompt word template may include a location area for inserting training sample data, and the initial coal data set sample is inserted into the prompt word template to obtain sample prompt word information.

[0103] In actual applications, the prompt word template can also include thought chain information for representing the generation of final coal data based on the initial coal data set sample. Based on the prompt word template, sample prompt word information containing thought chain information can be obtained, so that when the large language model generates prediction result data based on the sample prompt word information, the result data can be generated according to the logical steps reflected by the thought chain information in the sample prompt word information, thereby improving the accuracy of the generated prediction result data.

[0104] In the embodiments of this specification, fine-tuning training is performed on the large language model based on the degree of difference between the labeled result data and the predicted result data to obtain a trained large language model. The large language model is iteratively trained using each training sample in the training sample set. If the degree of difference between the labeled result data and the predicted result data is less than a preset threshold, training of the large language model is terminated. Alternatively, if the number of training cycles for the large language model meets a preset threshold, training of the large language model is terminated.

[0105] Based on the same idea, the embodiments of this specification also provide a device corresponding to the above method. Figure 6 This is a schematic diagram of the structure of a coal data generation device based on the Internet of Things platform provided in the embodiment of this specification. Figure 6 As shown, the device may include: An acquisition request receiving module 602 is configured to receive an acquisition request sent by a user for acquiring coal data of a preset department; a department determination module 604 for determining a number of departments that can provide the coal data based on an acquisition dimension of the coal data, wherein the acquisition dimension includes at least one of a production dimension, a transportation dimension, a sales dimension, and a logistics dimension; An acquisition request sending module 606 is configured to send the acquisition request to each of the multiple departments; The coal data receiving module 608 is configured to receive the initial coal data fed back by the departments in response to the acquisition request; Input module 610 is configured to input the initial coal data and prompt word information for generating final coal data into a large language model, so that the large language model generates final coal data based on the initial coal data according to the thought chain information in the prompt word information; the thought chain information is used to reflect the logical steps of the large language model in generating the final coal data based on the initial coal data; The coal data feedback module 612 is configured to feed back the final coal data generated by the large language model to the user.

[0106] based on Figure 6The present specification also provides some specific implementation plans of the method, which are described below.

[0107] Optionally, the coal data includes coal weight data, and the several departments include a coal production department; the way in which the coal production department generates the initial coal data may include: obtaining the conveying rate of the conveyor belt used to convey coal, which is counted by the coal production department, and the conveyor belt is a belt that transports the mined coal to the coal transport vehicle; based on the conveying rate, estimating the weight data.

[0108] Optionally, the coal data includes coal weight data, and the several departments include a coal transportation department; the way in which the coal transportation department generates the initial coal data may include: obtaining the coal volume of coal transported by the transport vehicles used to transport coal, which is counted by the coal transportation department, and the coal volume is the volume counted in the carriage of the transport vehicle; based on the coal volume, estimating the weight data.

[0109] Optionally, the coal data includes coal weight data, and the several departments include a coal logistics department; the way in which the coal logistics department generates the initial coal data may include: obtaining equipment consumption data for coal mining equipment compiled by the coal logistics department, the equipment consumption data including at least one of a first consumption of coal mining drill bits consumed by the coal mining equipment in the process of coal mining, a second consumption of industrial water consumed, and a third consumption of industrial electricity consumed; and estimating the weight data based on the equipment consumption data.

[0110] Optionally, the coal data includes coal weight data, and the several departments include a coal sales department; the way in which the coal sales department generates the initial coal data may include: obtaining the coal weight information measured by the coal sales department based on weighing equipment during the warehousing process; and estimating the weight data based on the coal weight information.

[0111] Optionally, generating the final coal data based on the initial coal data may include: determining the weight value corresponding to each initial coal data, the larger the weight value, the greater the impact of the initial coal data on the final result of the final coal data; and determining the final coal data by weighted summation based on each initial coal data and the weight value.

[0112] Optionally, the device may further include: The acquisition module is used to acquire a training sample set, wherein a training sample in the training sample set includes an initial coal data set sample and label result data corresponding to the initial coal data sample sample.

[0113] The insertion module is used to insert the initial coal data set sample into the prompt word template containing thought chain information to obtain sample prompt word information; the thought chain information is used to represent the logical information of generating the final coal data based on the initial coal data set sample.

[0114] The second input module is used to input the sample prompt word information into a pre-trained large language model to obtain prediction result data generated by the large language model according to the logical information.

[0115] A fine-tuning module is used to fine-tune the large language model based on the difference between the prediction result data and the label result data to obtain a trained large language model.

[0116] Based on the same idea, the embodiments of this specification also provide devices corresponding to the above methods.

[0117] Figure 7 This is a schematic diagram of the structure of a coal data generation device based on the Internet of Things platform provided in the embodiment of this specification. Figure 7 As shown, the device 700 may include: at least one processor 710; and, A memory 730 in communication with the at least one processor; wherein, The memory 730 stores instructions 720 that can be executed by the at least one processor 710. The instructions are executed by the at least one processor 710 to enable the at least one processor 710 to: Acquire three-dimensional scene data of a mine; the three-dimensional scene data includes at least a plurality of tunnel information; receiving an acquisition request sent by a user for acquiring coal data of a preset department; Determining, based on acquisition dimensions of the coal data, several departments that can provide the coal data, wherein the acquisition dimensions include at least one of a production dimension, a transportation dimension, a sales dimension, and a logistics dimension; Sending the acquisition request to each of the several departments; receiving various initial coal data fed back by the various departments in response to the acquisition request; Inputting the initial coal data and prompt word information for generating final coal data into a large language model, so that the large language model generates final coal data based on the initial coal data according to the thought chain information in the prompt word information; the thought chain information is used to reflect the logical steps of the large language model in generating the final coal data based on the initial coal data; The final coal data generated by the large language model is fed back to the user.

[0118] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. Figure 7 As for the device shown, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0119] In the 1990s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using physical hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly performed using software called a "logic compiler." This is similar to the software compilers used during program development. Before compilation, the original code must be written in a specific programming language, called a Hardware Description Language (HDL). There are many types of HDL, including ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that simply by programming a method flow in one of these hardware description languages and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0120] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the memory control logic. Those skilled in the art will also appreciate that, in addition to implementing the controller purely in computer-readable program code, the controller can also be implemented in the form of logic gates, switches, an application-specific integrated circuit, a programmable logic controller, an embedded microcontroller, etc. by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the means for implementing the various functions included therein can also be considered as structures within the hardware component. Alternatively, the means for implementing the various functions can be considered both a software module implementing the method and a structure within the hardware component.

[0121] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0122] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0123] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0127] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0128] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0129] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0130] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0131] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0132] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0133] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A coal data generation method based on the Internet of Things platform, characterized in that: include: receiving an acquisition request sent by a user for acquiring coal data of a preset department; Determining, based on acquisition dimensions of the coal data, several departments that can provide the coal data, wherein the acquisition dimensions include at least one of a production dimension, a transportation dimension, a sales dimension, and a logistics dimension; Sending the acquisition request to each of the several departments; receiving various initial coal data fed back by the various departments in response to the acquisition request; Inputting the initial coal data and prompt word information for generating final coal data into a large language model, so that the large language model generates final coal data based on the initial coal data according to the thought chain information in the prompt word information; the thought chain information is used to reflect the logical steps of the large language model in generating the final coal data based on the initial coal data; The final coal data generated by the large language model is fed back to the user.

2. The method according to claim 1, wherein The coal data includes coal weight data, and the several departments include a coal production department; the coal production department generates the initial coal data in the following manner: Obtaining a conveying rate of a conveyor belt used to convey coal, calculated by the coal production department, wherein the conveyor belt is a belt that conveys mined coal to a coal transport vehicle; Based on the delivery rate, the weight data is estimated.

3. The method according to claim 1, wherein The coal data includes coal weight data, and the several departments include a coal transportation department; the coal transportation department generates the initial coal data in the following manner: Obtaining the volume of coal transported by the transport vehicle used to transport coal, as calculated by the coal transportation department, wherein the volume of coal is the volume calculated in the compartment of the transport vehicle; The weight data is estimated based on the coal volume.

4. The method according to claim 1, wherein The coal data includes coal weight data, and the several departments include a coal logistics department; the coal logistics department generates the initial coal data in the following manner: Obtaining equipment consumption data for coal mining equipment compiled by the coal logistics department, the equipment consumption data comprising at least one of a first consumption amount of coal mining drill bits consumed by the coal mining equipment during coal mining, a second consumption amount of industrial water consumed, and a third consumption amount of industrial electricity consumed; The weight data is estimated based on the equipment wear data.

5. The method according to claim 1, wherein The coal data includes coal weight data, and the several departments include a coal sales department; the coal sales department generates the initial coal data in the following manner: Obtaining coal weight information calculated by the coal sales department and measured by weighing equipment during the coal storage process; The weight data is estimated based on the coal weight information.

6. The method according to claim 1, wherein Generating final coal data based on the respective initial coal data includes: Determine a weight value corresponding to each of the initial coal data, wherein the greater the weight value, the greater the influence of the initial coal data on the final result of the final coal data; The final coal data is determined by weighted summation based on the initial coal data and the weight values.

7. The method according to claim 1, wherein Before inputting the initial coal data and the prompt word information for generating the coal data into the large language model, the method further includes: Acquire a training sample set, wherein a training sample in the training sample set includes an initial coal data set sample and label result data corresponding to the initial coal data sample sample; Inserting the initial coal data set sample into a prompt word template containing thought chain information to obtain sample prompt word information; the thought chain information is used to represent logical information for generating final coal data based on the initial coal data set sample; Inputting the sample prompt word information into a pre-trained large language model to obtain prediction result data generated by the large language model according to the logic information; Based on the difference between the prediction result data and the label result data, the large language model is fine-tuned to obtain a trained large language model.

8. A coal data generation device based on the Internet of Things platform, characterized in that: include: An acquisition request receiving module, configured to receive an acquisition request sent by a user for acquiring coal data of a preset department; a department determination module, configured to determine a number of departments that can provide the coal data based on an acquisition dimension of the coal data, wherein the acquisition dimension includes at least one of a production dimension, a transportation dimension, a sales dimension, and a logistics dimension; An acquisition request sending module, configured to send the acquisition request to each of the multiple departments; A coal data receiving module, configured to receive each initial coal data fed back by each department in response to the acquisition request; An input module is configured to input the initial coal data and prompt word information for generating final coal data into a large language model, so that the large language model generates final coal data based on the initial coal data according to thought chain information in the prompt word information; the thought chain information is configured to reflect the logical steps of the large language model in generating the final coal data based on the initial coal data; A coal data feedback module is used to feed back the final coal data generated by the large language model to the user.

9. A coal data generation device based on the Internet of Things platform, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: receiving an acquisition request sent by a user for acquiring coal data of a preset department; Determining, based on acquisition dimensions of the coal data, several departments that can provide the coal data, wherein the acquisition dimensions include at least one of a production dimension, a transportation dimension, a sales dimension, and a logistics dimension; Sending the acquisition request to each of the several departments; receiving various initial coal data fed back by the various departments in response to the acquisition request; Inputting the initial coal data and prompt word information for generating final coal data into a large language model, so that the large language model generates final coal data based on the initial coal data according to the thought chain information in the prompt word information; the thought chain information is used to reflect the logical steps of the large language model in generating the final coal data based on the initial coal data; The final coal data generated by the large language model is fed back to the user.

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