Coal mine goaf monitoring data processing method, device, equipment and medium

Abstract

The application relates to the technical field of coal mine monitoring, and discloses a coal mine goaf monitoring data processing method, device, equipment and medium; the method comprises the following steps: collecting monitoring values of each monitoring parameter of a target goaf; for each monitoring parameter, performing value rounding on the monitoring value of the monitoring parameter according to a redundancy rounding table, to obtain a primary rounding value of the monitoring parameter; generating monitoring data of the target goaf according to the primary rounding values of all the monitoring parameters; obtaining task information of all monitoring tasks in a monitoring period; for any monitoring task, extracting each monitoring parameter and a value rounding rule of the monitoring parameter contained in the monitoring task; for each monitoring parameter, extracting the primary rounding value of the monitoring parameter from the monitoring data, and performing value rounding on the primary rounding value according to the value rounding rule of the monitoring parameter, to obtain a rounding value of the primary rounding value; and generating task rounding data according to the rounding values. The application significantly improves the processing performance of the system on the monitoring data.

Description

Technical Field

[0001] This application relates to the field of coal mine monitoring technology, and more specifically, to a method, apparatus, equipment, and medium for processing monitoring data of coal mine goaf areas. Background Technology

[0002] In the field of coal mine monitoring, monitoring needs such as preventing spontaneous combustion of residual coal in goaf areas, determining the lag distance of end paste filling, and controlling filling time all require monitoring of various parameters in goaf areas. However, the requirements for parameter accuracy vary significantly depending on the monitoring needs.

[0003] If monitoring parameters are uniformly processed with lower precision, it will be difficult to obtain accurate information for high-precision monitoring tasks such as accurately determining the lag distance of the filling of the end paste. This will not only reduce the control precision of the filling process and affect the filling quality, but may also bring safety hazards, such as roof collapse due to untimely filling. Similarly, in the monitoring of spontaneous combustion of residual coal in the goaf, insufficient precision will make it difficult to capture subtle changes in key parameters such as the temperature of residual coal, which may lead to missing the best time for fire prevention and extinguishing and increase the risk of spontaneous combustion. Currently, in order to meet the high-precision monitoring requirements, all monitoring parameters are uniformly processed with high precision requirements. Although this approach can meet the high-precision monitoring requirements, it will lead to a waste of resources. For monitoring tasks with low precision requirements, such as only needing to roughly understand the filling time (e.g., accurate to the hour to meet production scheduling), excessive pursuit of high precision will consume a large amount of computing, storage, and transmission resources. This will not only increase the cost and complexity of the monitoring system, but also affect system performance, cause data transmission delays, and even cause storage devices to become saturated prematurely. Summary of the Invention

[0004] In view of the above situation, this application provides a method, apparatus, equipment and medium for processing monitoring data of coal mine goaf areas, which aims to solve the above problems or at least partially solve the above problems.

[0005] In a first aspect, embodiments of this application provide a method for processing monitoring data from coal mine goaf areas, the method comprising: Collect the monitoring values ​​of various monitoring parameters of the target goaf area at the current moment; For each monitoring parameter, the monitoring value of the parameter collected at the current time is rounded according to the pre-stored redundancy rounding table of the monitoring parameter to obtain the initial rounded value of the monitoring parameter. Based on the initial values ​​of all monitoring parameters at the current moment, generate monitoring data for the target goaf area; Obtain task information for all monitoring tasks within a monitoring period that matches the current time. The task information includes monitoring parameters and their rounding rules. For any monitoring task obtained, extract each monitoring parameter and its numerical rounding rule contained in the monitoring task. For each monitoring parameter, extract the initial value of the monitoring parameter from the monitoring data, and perform numerical rounding on the initial value according to the numerical rounding rule of the monitoring parameter to obtain the rounded value of the initial value. After obtaining the rounded value of each monitoring parameter for each monitoring task, the task rounded data for the current moment is generated based on each rounded value and stored.

[0006] Secondly, embodiments of this application also provide a coal mine goaf monitoring data processing device, the device comprising: The data acquisition module is used to collect the monitoring values ​​of various monitoring parameters of the target goaf area at the current time; The rounding module is used to round the monitoring value of each monitoring parameter collected at the current time according to the pre-stored redundant rounding table of the monitoring parameter, so as to obtain the initial rounded value of the monitoring parameter. The generation module is used to generate monitoring data for the target goaf area based on the initial values ​​of all monitoring parameters at the current time. The acquisition module is used to acquire task information for all monitoring tasks within a monitoring period that matches the current time. The task information includes monitoring parameters and their numerical rounding rules. The rounding module is also used to extract each monitoring parameter and its numerical rounding rule for any monitoring task obtained. For each monitoring parameter, the module extracts the initial rounded value of the monitoring parameter from the monitoring data and performs numerical rounding on the initial rounded value according to the numerical rounding rule of the monitoring parameter to obtain the rounded value of the initial rounded value. The generation module is also used to generate and store the task rounding data for the current moment based on the rounding values ​​of each monitoring parameter of each monitoring task after obtaining the rounding values ​​of each monitoring parameter.

[0007] Thirdly, embodiments of this application also provide an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the steps described in the first aspect.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform the steps described in the first aspect.

[0009] The above-mentioned at least one technical solution adopted in the embodiments of this application can achieve the following beneficial effects: by first performing initial rounding on the collected monitoring parameter values ​​according to the pre-stored redundancy rounding table to obtain initial rounded values, and then generating monitoring data based on the initial rounded values ​​for transmission and subsequent processing, the amount of monitoring data during transmission can be effectively reduced, data transmission latency can be reduced, and the consumption of computing, storage and transmission resources can be reduced. This avoids the waste of resources caused by excessively pursuing high precision for monitoring tasks with low precision requirements such as filling time control when uniformly using high precision to process all monitoring parameters, reducing the cost and complexity of the monitoring system, and alleviating the problem of premature saturation of storage devices; at the same time, by separately processing different monitoring tasks according to their respective requirements... The corresponding numerical rounding rules perform secondary numerical rounding on the initial values, which can adapt to the different requirements of parameter accuracy for various monitoring needs, such as monitoring the spontaneous combustion of residual coal and determining the lag distance of end paste filling. It can ensure that high-precision monitoring tasks obtain parameter data that meet the accuracy requirements, improve the control accuracy and filling quality of the filling process, and avoid safety hazards such as roof collapse due to untimely filling. It can also accurately capture subtle changes in key parameters such as residual coal temperature, ensure timely grasp of the best fire prevention and extinguishing opportunity, and reduce the risk of spontaneous combustion. Moreover, it does not need to maintain excessively high parameter accuracy for low-precision monitoring tasks. Under the premise of fully meeting the accuracy requirements of different monitoring scenarios, it realizes the rational allocation of monitoring resources and significantly improves the overall processing performance of monitoring data. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This paper shows a schematic diagram of the structure of the coal mine goaf monitoring data processing system provided in an embodiment of this application; Figure 2 A flowchart illustrating the coal mine goaf monitoring data processing method provided in an embodiment of this application is shown. Figure 3 A structural diagram of the coal mine goaf monitoring data processing device provided in an embodiment of this application is shown; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0013] Figure 1 This diagram illustrates the structure of a coal mine goaf monitoring data processing system according to an embodiment of this application. The coal mine goaf monitoring data processing method provided in this embodiment can be implemented through, for example... Figure 1 The system is implemented in this way. It can be applied to multi-target monitoring scenarios in the goaf of fully mechanized mining faces in coal mines, and performs data processing for differentiated monitoring needs such as prevention and control of spontaneous combustion of residual coal in goaf, construction management and control of end-face paste filling, and scheduling of filling production. On-site monitoring and data acquisition devices, including those for temperature, gas concentration, roof displacement, and filling pressure, are deployed at corresponding locations in the target goaf area. The monitoring data from each device is collected and aggregated by the regional monitoring terminal. The acquisition terminal first performs initial rounding and compression of the original monitoring values ​​according to pre-generated redundancy rounding rules, and then uploads the simplified monitoring data to the goaf monitoring platform. After receiving the data, the platform's pre-processing unit matches it with various monitoring tasks within the current monitoring cycle and performs secondary precise rounding of the initial data according to the accuracy requirements of each task. This generates task rounding data suitable for different purposes such as spontaneous combustion early warning, filling distance calculation, and production scheduling, which is simultaneously stored in the monitoring management unit for management personnel to retrieve and analyze. The rounding analysis unit periodically iterates and optimizes the redundancy rounding rules of each monitoring parameter based on historical task log data, continuously adapting to changes in monitoring needs during the goaf mining process. While ensuring the accuracy of high-precision monitoring task data, this significantly reduces the bandwidth consumption of underground data transmission and the consumption of platform storage and computing resources, adapting to the long-term stable monitoring needs in the complex network environment of underground coal mines.

[0014] In this application, the control of the monitoring equipment is based on PLC. The start and stop control signals of all equipment are connected to the power distribution cabinet / frequency converter through DO / DI hard wiring. All sensor monitoring signals are connected to the control system through AI / AO modules. The operating status, power parameters, and regulation control signals fed back by the frequency converter are connected to the control system through the bus.

[0015] In this application, the type and location of the monitoring equipment deployed in any parameter monitoring unit are selected by the coal mine management personnel based on the parameter type of the corresponding monitoring parameter, the structure and geological conditions of the target goaf area, the mining technology, and historical disaster data. In this application, the following specifications are specified for the installation of electrical conduits within the target goaf area: Galvanized steel pipes are used for the protection of electrical wires and cables; connections of conduits embedded in concrete and walls are made using sleeve welding; exposed conduits are connected using threaded or compression fittings; when using threaded connections for steel pipes, the thread length at the pipe end should not be less than 1 / 2 of the pipe joint length, and after connection, 2-3 threads should be exposed. The thread surface should be smooth and free of defects. This applies to the installation of metal conduits under the ground, in damp or corrosive areas, indoors, and in hazardous areas. Exposed threads should be coated with anti-rust paint; steel pipe bending: steel pipes below G25 should be bent using a manual bending machine, those above G32 should be bent using a hydraulic pipe bending machine, and those above G70 should be processed by a professional factory. The bending of steel pipes of all the above specifications can be prefabricated in the factory. The bending of the pipes should meet the requirements of the "specification"; when installing electrical conduits, the pipe ends should be smooth and free of burrs, and the cut ends should be flat; the opening of the electrical conduit entering the floor distribution box should be 50mm-80mm higher than the concrete surface, and the opening of the electrical conduit to the equipment should be 200mm higher than the concrete surface. When multiple pipes enter a distribution box, they should be arranged neatly, with the pipe openings at the same height inside the box. Cables and conduits leading out of or into distribution boxes, control boxes, etc., must be reliably connected to the box shell and the grounding grid. Before concrete pouring, pipe plugs or caps that meet the construction requirements and site conditions should be installed, and the conduits should be kept dry, free of water and dirt. The inlets of conduits passing through concrete walls or the bottom of the floor from the outside should be temporarily sealed and waterproofed.

[0016] In this application, the cable laying specifications within the target goaf area are as follows: Before laying the cable, an inspection should be carried out, including: the product's technical documents should be complete; the cable model, specifications, length, and cable accessories should meet the design and ordering requirements; the cable's appearance should be undamaged, the insulation layer should be in good condition, and the cable head should be tightly sealed; cables below 1kV should undergo insulation resistance testing, and high-voltage cables should also undergo withstand voltage and leakage current testing; before laying the cable, cable trays, metal troughs, and cable protection pipes should be cleaned up, and any obstructive work should be completed; spare length should be left near the cable's termination and intermediate joints. When burying cables directly, a small margin should be left along the entire length. Cable laying should be carried out manually combined with mechanical traction. For vertical transportation of cables above ±0m, a truck crane used in civil engineering construction can be used for hoisting. When laying cables, first set up the cable reel on the cable laying frame, and release the cable end from the top of the reel. During the laying process, the tension should be uniform. Someone should be on the cable tray to adjust the cable laying pulleys to prevent the cable from slipping off the pulleys. When the cable is long, the middle can be selected as the starting point and laid out towards both ends. In this case, the cable length must be measured accurately. When laying cables along the cable tray, they should be laid in a single layer, and each cable should be laid, tidied, and secured. Vertically laid cables should be fixed every 1.5m-2m. Horizontally laid cables should be fixed at both ends, bends, and every 5m-10m. Cables in the cable tray should have labels at the beginning, end, and bends, indicating the number, model, and start and end points. The markings should be clear and complete, and the cables should be neatly hung without any omissions. Before laying the cables, the gravel in the cable trench should be cleaned up. The bottom of the cable trench should be made of homogeneous fine sand with a sand equivalent greater than 20, and the sand layer should be 0.1m thick. The covering layer should also be 0.1m thick. After the covering layer is covered with protective tiles, the soil should be backfilled. Warning signs should be placed above the buried cables. During and after the laying process, the bending radius of the cables should meet the specified requirements. The bending radius of the cable at the bend should be based on the allowable bending radius of the cable with the largest cross-section. The total cross-section of the power cables in the cable tray (including the outer sheath) should not exceed 40% of the cross-section of the cable tray, and the cross-section of the control cables should not exceed 50%. After the cables are laid and when they are installed in areas where other projects have not yet been completed, protective measures should be taken to prevent the cables from being damaged. After the laying is completed, debris should be cleaned up in time, and the covers should be put on where available. Final adjustments should be made.

[0017] Figure 2 This document illustrates a flowchart of a coal mine goaf monitoring data processing method provided in an embodiment of this application. Figure 2 It can be seen that this application includes at least steps S101-S106: Step S101: Collect the monitoring values ​​of each monitoring parameter of the target goaf area at the current time.

[0018] Specifically, various monitoring and data acquisition devices deployed at the target goaf site are used to synchronously collect raw, full-precision values ​​of multiple monitoring parameters corresponding to the goaf at fixed times. The monitoring parameters can cover the temperature of residual coal in the goaf, the concentration of marker gases, the amount of roof displacement, the pressure of the backfill, the advancing distance, etc., corresponding to different monitoring directions such as preventing spontaneous combustion of residual coal, monitoring roof stability, and controlling backfill construction.

[0019] Step S102: For each monitoring parameter, the monitoring value of the current monitoring parameter is rounded according to the pre-stored redundancy rounding table of the monitoring parameter to obtain the initial rounded value of the monitoring parameter.

[0020] The redundancy rounding table is generated by rounding analysis of task log data from multiple historical monitoring periods. After generation, it is used for initial value rounding during the data acquisition phase. It records the maximum number of redundant bits that can be safely removed from the end of the original value for each monitoring parameter, while ensuring the final accuracy of all monitoring tasks. This step performs preliminary simplification of the original high-precision monitoring values ​​according to this rule, removing invalid redundant bits without affecting the accuracy of the subsequent final rounding, thus obtaining the initial rounded value. Its core function is to compress the volume of a single set of data from the source, reducing the data load for subsequent transmission and computation.

[0021] It is worth noting that the historical task log data used to generate the redundancy rounding table must come from monitoring periods where the initial rounding mechanism has not yet been enabled. If the historical data itself has already undergone initial rounding, then: the monitoring values ​​have lost some low-order precision; calculating the maximum number of deletable bits at this point will yield a smaller result, possibly even zero; the resulting redundancy rounding table cannot accurately reflect the redundancy capability of the original data relative to each monitoring task, potentially leading to over-rounding or under-rounding when subsequent parameter monitoring units perform initial rounding based on this table, affecting the final accuracy. Therefore, it is essential to use original, unrounded, high-precision monitoring data to construct the redundancy rounding table, and this data can only come from early monitoring periods where the initial rounding mechanism has not yet been enabled.

[0022] Step S103: Generate monitoring data for the target goaf area based on the initial values ​​of all monitoring parameters at the current time.

[0023] Specifically, the initial revised values ​​of all monitoring parameters at the same time are integrated into a complete target goaf monitoring dataset in a unified format for transmission to the data processing terminal. Since all parameters have undergone initial rounding and compression, the overall size of the integrated monitoring data is much smaller than the original full-precision data packet, which can reduce bandwidth usage and transmission delay during transmission, and is suitable for the actual scenario of limited network bandwidth and complex transmission environment in coal mines.

[0024] Step S104: Obtain task information for all monitoring tasks within the monitoring cycle that matches the current time.

[0025] The task information includes monitoring parameters and their numerical rounding rules. In some embodiments, monitoring tasks are periodically formulated by coal mine managers based on an assessment plan for the target goaf. This assessment plan includes, but is not limited to, geological and structural stability assessments, hydrogeological assessments, safety risk assessments, ecological impact assessments, and social impact assessments. Examples include tasks such as early warning of spontaneous combustion of residual coal, calculation of the lag distance for end-face paste backfilling, and backfilling production scheduling. Each task corresponds to different monitoring parameters and final accuracy requirements, i.e., numerical rounding rules.

[0026] Furthermore, at each monitoring cycle interval, task information for all monitoring tasks in the corresponding monitoring cycle is pre-entered and stored. This pre-entry mechanism ensures that the task configuration for the current monitoring cycle is ready before the cycle begins. When the front-end processing unit receives monitoring data at any time, it only needs to locate the monitoring cycle to which it belongs based on the current time. It can then directly retrieve the rounding rules for all monitoring tasks in that cycle from the stored task information, without needing to query in real time or generate task configurations temporarily during data processing. This significantly reduces the computational overhead and latency of real-time data processing. At the same time, since different monitoring cycles can be flexibly configured with different monitoring tasks and accuracy requirements according to actual evaluation needs, this mechanism provides strong support for cycle-based differentiated monitoring strategies, effectively improving the system's adaptability and response efficiency to different goaf monitoring scenarios.

[0027] Step S105: For any monitoring task obtained, extract each monitoring parameter and its numerical rounding rule contained in the monitoring task. For each monitoring parameter, extract the initial value of the monitoring parameter from the monitoring data, and perform numerical rounding on the initial value according to the numerical rounding rule of the monitoring parameter to obtain the rounded value of the initial value.

[0028] Numerical rounding rules refer to the process of omitting the last few digits of the original value and adjusting the remaining last digits before performing specific numerical calculations, so that the final value is as close as possible to the original value. For example, when the assessment plan is a safety risk assessment, based on potential risks such as flammable and explosive hazards, toxic and harmful gases, it is necessary to collect gas concentration changes of several gases in the target goaf area, and the collected gas concentrations only need to be accurate to two decimal places. The collected gas concentration changes need to be compared with corresponding preset warning values; in this case, the accuracy of the collected gas concentrations only needs to be slightly higher than or equal to the accuracy of the warning values.

[0029] Since different monitoring tasks have different accuracy requirements for the same parameter, this step takes a single monitoring task as a unit, extracts the initial revised value of the corresponding parameter from the summarized monitoring data as needed, and then performs a second precise rounding according to the numerical rounding rules specific to the task to obtain the final rounded value that just meets the accuracy requirements of the task.

[0030] Step S106: After obtaining the rounding value of each monitoring parameter for each monitoring task, generate and store the task rounding data for the current moment based on each rounding value.

[0031] Based on a single monitoring task, the final rounded values ​​of all parameters corresponding to that task are integrated into independent task rounded data and stored separately. The final stored data retains only the effective precision required for the corresponding task, without storing unnecessary redundant bits, which can significantly save storage resources and avoid premature saturation of storage devices caused by long-term storage of full-precision data; at the same time, different business positions can directly retrieve and use the rounded data of the corresponding task without secondary processing, improving the efficiency of monitoring data retrieval.

[0032] from Figure 1 As can be seen from the method shown, this application first performs initial rounding on the collected monitoring parameters based on a pre-stored redundancy rounding table to obtain initial rounded values, and then generates monitoring data based on the initial rounded values ​​for transmission and subsequent processing. This effectively reduces the amount of monitoring data during transmission, lowers data transmission latency, and reduces the consumption of computing, storage, and transmission resources. It avoids the resource waste caused by excessively pursuing high precision for monitoring tasks with low precision requirements such as fill time control when uniformly using high precision to process all monitoring parameters. This reduces the cost and complexity of the monitoring system and alleviates the problem of premature saturation of storage devices. At the same time, by applying the corresponding numerical rounding rules to different monitoring tasks, this method also addresses the issue of data loss. By performing secondary numerical rounding on the initial values, the system can adapt to the different requirements of parameter accuracy for various monitoring needs, such as monitoring the spontaneous combustion of residual coal and determining the lag distance of end-point paste filling. This ensures that high-precision monitoring tasks obtain parameter data that meets the accuracy requirements, improves the control accuracy and filling quality of the filling process, and avoids safety hazards such as roof collapse due to untimely filling. It can also accurately capture subtle changes in key parameters such as the temperature of residual coal, ensuring timely grasp of the best fire prevention and extinguishing opportunity and reducing the risk of spontaneous combustion. Moreover, it does not require maintaining excessively high parameter accuracy for low-precision monitoring tasks. Under the premise of fully meeting the accuracy requirements of different monitoring scenarios, it achieves reasonable allocation of monitoring resources and significantly improves the overall processing performance of monitoring data.

[0033] In some embodiments of this application, the method for determining the redundant rounding table in the above method includes: S11: Select a monitoring parameter of the target goaf as the parameter to be rounded, obtain all monitoring tasks corresponding to the task information containing the parameter to be rounded and remove duplicates, and mark the deduplicated monitoring tasks as A1, A2, ..., Aa, where a≥1; It should be noted that during the deduplication process, only one of several monitoring tasks with identical task information is retained.

[0034] S12: Retrieve all task log data containing task rounding data of monitoring task A1 in the order of storage, and label them as B1, B2, ..., Bb, where b≥1; Among them, the number of monitoring tasks containing numerical rounding rules for parameters to be rounded in any task rounding data is greater than or equal to 1, and when any task rounding data in task log data B1, B2, ..., Bb is stored in the monitoring management unit, there are no redundant rounding tables for the corresponding monitoring parameters stored in any parameter monitoring unit.

[0035] S13: Extract all monitoring values ​​C1, C2, ..., Cc of the parameter to be rounded from the task rounding data of task A1 in task log data B1, where c≥1, and the corresponding rounding values ​​D1, D2, ..., Dc; S14: Compare the number of bits of the monitoring value C1 and its rounded value D1 to determine the precision redundancy bit E1 of the monitoring value C1 relative to the monitoring task A1. The precision redundancy bit refers to the maximum number of bits that can be deleted so that the monitoring value C1 can obtain the rounded value D1 according to the numerical rounding rules of the parameter to be rounded in the monitoring task A1 under the monitoring precision of its corresponding monitoring equipment. For example, if the monitored value C1 is 0.0137012, and its rounding rule is to round to two decimal places, then its rounded value is 0.01, and the precision redundancy bits are 5. If its rounding rule is to round to three decimal places, then its rounded value is 0.014.

[0036] The explanation is as follows: When the numerical rounding rule is to round to two decimal places, all characters on the right side of the monitored value C1 can be deleted starting from the 5th position from the right. After deletion, the rounded value remains 0.01, with no change. When the numerical rounding rule is to round to three decimal places, deleting the fourth digit from the right will change the final rounded value, while deleting the fifth digit will not change the final rounded value. Therefore, the precision redundancy is 4 bits.

[0037] S15: Determine the precision redundancy bits of the monitoring values ​​C2 relative to D2, C3 relative to D3, ..., Cc relative to Dc in sequence according to S13 to S14, and select the precision redundancy bit with the smallest value as the standard redundancy bit E1 of the rounding parameter to be used by monitoring task A1 based on task log data B1. S16: Obtain the standard redundancy bits E2, E3, ..., Eb of the parameters to be rounded in monitoring task A1 based on task log data B2, B3, ..., Bb in sequence according to S12 to S15; select the standard redundancy bit with the smallest value from the standard redundancy bits E1, E2, ..., Eb as the redundancy rounding bit of the parameters to be rounded in monitoring task A1; at the same time, take the interval between the end time of the monitoring period corresponding to task log data B1 and the start time of the monitoring period corresponding to task log data B2 as the redundancy rounding duration of the parameters to be rounded in monitoring task A1. It should be noted that since they belong to the same monitoring task and their monitoring cycles are fixed, the interval between the end time of the monitoring cycle corresponding to task log data B1 and the start time of the monitoring cycle corresponding to task log data B2, the interval between the end time of the monitoring cycle corresponding to task log data B2 and the start time of the monitoring cycle corresponding to task log data B3, ..., and the interval between the end time of the monitoring cycle corresponding to task log data Bb-1 and the start time of the monitoring cycle corresponding to task log data Bb are all consistent. Only one of them needs to be used as a representative.

[0038] S17: Calculate and obtain the redundant rounding bits and redundant rounding duration of the parameters to be rounded in monitoring tasks A2, A3, ..., Aa in sequence according to S11 to S16; generate a redundant rounding table of the parameters to be rounded based on the redundant rounding bits and redundant rounding duration of the parameters to be rounded in monitoring tasks A1, A2, ..., Aa. S18: Select all monitoring parameters of the target goaf as parameters to be rounded in sequence, and generate a redundant rounding table of all monitoring parameters of the target goaf according to S11 to S17.

[0039] In this embodiment, a two-level numerical rounding architecture is adopted, consisting of initial pre-rounding at the acquisition end and secondary precise rounding at the platform end based on the task. By pre-eliminating invalid precision redundancy bits in the original monitoring values ​​through a redundant rounding table derived from historical monitoring data, the amount of transmitted data can be effectively reduced, and the transmission delay and bandwidth consumption of underground coal mine monitoring data can be reduced. At the same time, corresponding rounding rules are matched differently according to the precision requirements of different monitoring tasks, and the precision results are output on demand, avoiding the waste of computing and storage resources caused by uniform processing of full precision. Moreover, the derivation of the redundant rounding table adopts a double strict constraint mechanism, which can ensure that the secondary rounding results are completely consistent with the full precision processing results, eliminating the risk of precision distortion. In addition, some rounding calculations are pre-processed at the acquisition end, reducing the computing load of the backend platform and improving the overall data processing performance of the system. The redundant rounding table can also be dynamically updated according to the mining process and monitoring needs, adapting to the monitoring changes in different stages of the goaf, and has good engineering practicality and operational stability.

[0040] In some embodiments of this application, in the above method, for a parameter monitoring unit, after receiving the redundancy rounding table of the corresponding monitoring parameter, the step of determining the rounding index of the monitoring parameter at the next moment based on the receiving time is as follows: S21: Obtain the time J1 when the redundancy rounding table of the monitoring parameters is received, and trace back to time P2 from time J1. Based on the traced time P2, extract several monitoring tasks from the monitoring tasks corresponding to all task information stored in the preprocessing unit within the time P2. P2 is the redundancy rounding time with the largest redundancy rounding time value among all monitoring tasks containing monitoring parameters.

[0041] In all extracted monitoring tasks, the monitoring parameters are contained within the task information of any given task. Furthermore, at least one of the extracted monitoring tasks has its task information stored only once within the backtracking timeframe P2. If a task is stored repeatedly within the P2 timeframe, it becomes difficult to accurately determine which storage corresponds to an independent monitoring cycle. The existence of at least one task that is "stored only once" provides a definitive reference point for judging the timeliness of each task relative to the current moment within that backtracking period. This allows for reliable calculation based on a clear storage time point when determining whether a task is a "valid rounding task for the next moment," avoiding ambiguity or invalidation of the judgment benchmark due to the same task information appearing multiple times within the backtracking timeframe, and ensuring the accuracy and stability of the rounding index determination.

[0042] S22: Based on the task information of each monitoring task, deduplicate all extracted monitoring tasks and label all deduplicated monitoring tasks as H1, H2, ..., Hh, h≥1; S23: Obtain the time J2, which is the closest time to the current time, from all the extracted monitoring tasks to the time when the task information is stored in the pre-processing unit; S24: Determine whether monitoring task H1 is a valid rounding task for the monitoring parameters at the next moment. If J1-J2≤P3, then monitoring task H1 is determined to be a valid rounding task for the monitoring parameters at the next moment; otherwise, monitoring task H1 is determined not to be a valid rounding task for the monitoring parameters at the next moment. P3 is the preset interval of the monitoring cycle. S25: Sequentially determine whether monitoring tasks H2, H3, ..., Hh are valid rounding tasks for the monitoring parameters at the next time step, obtain all valid rounding tasks that are determined to be the monitoring parameters at the next time step, extract the redundant rounding bits of all valid rounding tasks from the redundant rounding table of the monitoring parameters, and select the redundant rounding bit with the smallest value as the rounding index of the monitoring parameters at the next time step.

[0043] When the next moment arrives, after collecting the monitoring values ​​of the monitoring parameters for the next moment, the monitoring values ​​are rounded according to the rounding index of the next moment to obtain the initial revised values ​​of the monitoring parameters for the next moment.

[0044] It should be noted that, for any parameter monitoring unit, after obtaining the initial value of the monitoring parameter at each time point, the rounding index of the monitoring parameter at the next time point is determined based on the time point. The determination process is consistent with the step of determining the rounding index at the next time point based on time point J1. When extracting several monitoring tasks, time point P2 is traced back from time point J1.

[0045] In this embodiment, by using the moment when the parameter monitoring unit receives the redundant rounding table as a benchmark, the maximum redundant rounding duration of all relevant monitoring tasks is traced back. Based on the task information stored during the traceback period, deduplication and timeliness are determined, thereby accurately selecting the rounding tasks that are still valid at the next moment. Then, the redundant rounding bit with the smallest value is selected as the rounding index. This ensures that the initial rounding accuracy of each monitoring parameter meets the minimum accuracy requirements of all currently valid monitoring tasks while maximizing the removal of redundant bits. This minimizes the amount of data transmitted in a single transmission while ensuring the accuracy of data from each monitoring task, effectively reducing transmission delay and storage occupation. At the same time, the parameter monitoring unit dynamically updates the rounding index every moment, allowing it to adaptively adjust with changes in monitoring tasks without manual intervention. This ensures the system's continuous adaptability to different monitoring cycles and task configurations during long-term operation, significantly improving the overall efficiency and intelligence level of coal mine goaf monitoring data processing.

[0046] In some embodiments of this application, a coal mine goaf monitoring data processing device is provided, which corresponds one-to-one with the coal mine goaf monitoring data processing method described in the above embodiments. For example... Figure 3 As shown, the coal mine goaf monitoring data processing device includes a data acquisition module 101, a rounding module 102, a generation module 103, and an acquisition module 104. The acquisition module 101 is used to acquire the monitoring values ​​of various monitoring parameters of the target goaf area at the current time; The rounding module 102 is used to round the monitoring value of the monitoring parameter collected at the current time according to the pre-stored redundant rounding table of the monitoring parameter for each monitoring parameter, so as to obtain the initial rounded value of the monitoring parameter. The generation module 103 is used to generate monitoring data of the target goaf area based on the initial values ​​of all monitoring parameters at the current time. The acquisition module 104 is used to acquire task information of all monitoring tasks in the monitoring cycle that matches the current time. The task information includes monitoring parameters and their numerical rounding rules. The rounding module 102 is also used to extract each monitoring parameter and its numerical rounding rule contained in any monitoring task for any acquired monitoring task. For each monitoring parameter, the initial rounding value of the monitoring parameter is extracted from the monitoring data, and the initial rounding value is numerically rounded according to the numerical rounding rule of the monitoring parameter to obtain the rounded value of the initial rounding value. The generation module 103 is also used to generate and store the task rounding data at the current moment based on the rounding values ​​of each monitoring parameter of each monitoring task after obtaining the rounding values ​​of each monitoring parameter of each monitoring task.

[0047] In some embodiments of this application, in the above-described apparatus, the redundant rounding table is generated by performing rounding analysis on task log data from multiple historical monitoring periods, and is used for initial value rounding during the monitoring data acquisition phase.

[0048] In some embodiments of this application, the method for determining the redundant rounding table in the above-described apparatus includes: S11: Select a monitoring parameter of the target goaf as the parameter to be rounded, obtain all monitoring tasks corresponding to the task information containing the parameter to be rounded and remove duplicates, and mark the deduplicated monitoring tasks as A1, A2, ..., Aa, where a≥1; S12: Retrieve all task log data containing task rounding data of monitoring task A1 in the order of storage, and label them as B1, B2, ..., Bb, where b≥1; S13: Extract all monitoring values ​​C1, C2, ..., Cc of the parameter to be rounded from the task rounding data of task A1 in task log data B1, where c≥1, and the corresponding rounding values ​​D1, D2, ..., Dc; S14: Compare the number of bits of the monitoring value C1 and its rounded value D1 to determine the precision redundancy bit E1 of the monitoring value C1 relative to the monitoring task A1. The precision redundancy bit refers to the maximum number of bits that can be deleted so that the monitoring value C1 can obtain the rounded value D1 according to the numerical rounding rules of the parameter to be rounded in the monitoring task A1 under the monitoring precision of its corresponding monitoring equipment. S15: Determine the precision redundancy bits of the monitoring values ​​C2 relative to D2, C3 relative to D3, ..., Cc relative to Dc in sequence according to S13 to S14, and select the precision redundancy bit with the smallest value as the standard redundancy bit E1 of the rounding parameter to be used by monitoring task A1 based on task log data B1. S16: Obtain the standard redundancy bits E2, E3, ..., Eb of the parameters to be rounded in monitoring task A1 based on task log data B2, B3, ..., Bb in sequence according to S12 to S15; select the standard redundancy bit with the smallest value from the standard redundancy bits E1, E2, ..., Eb as the redundancy rounding bit of the parameters to be rounded in monitoring task A1; at the same time, take the interval between the end time of the monitoring period corresponding to task log data B1 and the start time of the monitoring period corresponding to task log data B2 as the redundancy rounding duration of the parameters to be rounded in monitoring task A1. S17: Calculate and obtain the redundant rounding bits and redundant rounding duration of the parameters to be rounded in monitoring tasks A2, A3, ..., Aa in sequence according to S11 to S16; generate a redundant rounding table of the parameters to be rounded based on the redundant rounding bits and redundant rounding duration of the parameters to be rounded in monitoring tasks A1, A2, ..., Aa. S18: Select all monitoring parameters of the target goaf as parameters to be rounded in sequence, and generate a redundant rounding table of all monitoring parameters of the target goaf according to S11 to S17.

[0049] In some embodiments of this application, the historical task log data used to generate the redundant rounding table in the above-described apparatus is collected from historical monitoring periods when the initial rounding mechanism is not enabled.

[0050] In some embodiments of this application, in the above-described apparatus, for a parameter monitoring unit, after receiving the redundancy rounding table of the corresponding monitoring parameter, the step of determining the rounding index of the monitoring parameter at the next moment based on the moment of receipt is as follows: S21: Obtain the time J1 when the redundancy rounding table of the monitoring parameters is received, and trace back to time P2 from time J1. Based on the traced time P2, extract several monitoring tasks from the monitoring tasks corresponding to all task information stored in the preprocessing unit within the time P2. P2 is the redundancy rounding time with the largest redundancy rounding time value among all monitoring tasks containing monitoring parameters. S22: Based on the task information of each monitoring task, deduplicate all extracted monitoring tasks, and label all deduplicated monitoring tasks as H1, H2, ..., Hh, h≥1; S23: Obtain the time J2, which is the closest time to the current time, from all the extracted monitoring tasks to the time when the task information is stored in the pre-processing unit; S24: Determine whether monitoring task H1 is a valid rounding task for the monitoring parameters at the next moment. If J1-J2≤P3, then monitoring task H1 is determined to be a valid rounding task for the monitoring parameters at the next moment; otherwise, monitoring task H1 is determined not to be a valid rounding task for the monitoring parameters at the next moment. P3 is the preset interval of the monitoring cycle. S25: Sequentially determine whether monitoring tasks H2, H3, ..., Hh are valid rounding tasks for the monitoring parameters at the next time step, obtain all valid rounding tasks that are determined to be the monitoring parameters at the next time step, extract the redundant rounding bits of all valid rounding tasks from the redundant rounding table of the monitoring parameters, and select the redundant rounding bit with the smallest value as the rounding index of the monitoring parameters at the next time step.

[0051] In some embodiments of this application, in the above-described apparatus, the task information of any one of the extracted monitoring tasks contains the monitoring parameters, and at least one of the extracted monitoring tasks stores the task information of the monitoring task only once during the backtracking P2 time period.

[0052] In some embodiments of this application, in the above-described device, task information of all monitoring tasks for the corresponding monitoring period is pre-recorded and stored every monitoring cycle.

[0053] It should be noted that any of the above-mentioned coal mine goaf monitoring data processing devices can implement the aforementioned coal mine goaf monitoring data processing method one by one, which will not be elaborated here.

[0054] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Figure 4 As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0055] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0056] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0057] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a coal mine goaf monitoring data processing device at the logical level. The processor executes the program stored in memory and specifically performs the aforementioned methods.

[0058] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0059] This electronic device can execute the coal mine goaf monitoring data processing method provided in several embodiments of this application, and realize the coal mine goaf monitoring data processing device in... Figure 3 The functions of the embodiments shown are not described in detail here.

[0060] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform the coal mine goaf monitoring data processing method provided in several embodiments of this application.

[0061] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0066] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0067] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0068] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0069] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for processing monitoring data of coal mine goaf areas, characterized in that, The method includes: Collect the monitoring values ​​of various monitoring parameters of the target goaf area at the current moment; For each monitoring parameter, the monitoring value of the parameter collected at the current time is rounded according to the pre-stored redundancy rounding table of the monitoring parameter to obtain the initial rounded value of the monitoring parameter. Based on the initial values ​​of all monitoring parameters at the current moment, generate monitoring data for the target goaf area; Obtain task information for all monitoring tasks within a monitoring period that matches the current time. The task information includes monitoring parameters and their rounding rules. For any monitoring task obtained, extract each monitoring parameter and its numerical rounding rule contained in the monitoring task. For each monitoring parameter, extract the initial value of the monitoring parameter from the monitoring data, and perform numerical rounding on the initial value according to the numerical rounding rule of the monitoring parameter to obtain the rounded value of the initial value. After obtaining the rounded value of each monitoring parameter for each monitoring task, the task rounded data for the current moment is generated based on each rounded value and stored.

2. The method according to claim 1, characterized in that, The redundant rounding table is generated by performing rounding analysis on task log data from multiple historical monitoring periods. After generation, it is used for initial value rounding during the monitoring data collection phase.

3. The method according to claim 1 or 2, characterized in that, The method for determining the redundant rounding table includes: S11: Select a monitoring parameter of the target goaf as the parameter to be rounded, obtain all monitoring tasks corresponding to the task information containing the parameter to be rounded and remove duplicates, and mark the deduplicated monitoring tasks as A1, A2, ..., Aa, where a≥1; S12: Retrieve all task log data containing task rounding data of monitoring task A1 in the order of storage, and label them as B1, B2, ..., Bb, where b≥1; S13: Extract all monitoring values ​​C1, C2, ..., Cc of the parameter to be rounded from the task rounding data of task A1 in task log data B1, where c≥1, and the corresponding rounding values ​​D1, D2, ..., Dc; S14: Compare the number of bits of the monitoring value C1 and its rounded value D1 to determine the precision redundancy bit E1 of the monitoring value C1 relative to the monitoring task A1. The precision redundancy bit refers to the maximum number of bits that can be deleted so that the monitoring value C1 can obtain the rounded value D1 according to the numerical rounding rules of the parameter to be rounded in the monitoring task A1 under the monitoring precision of its corresponding monitoring equipment. S15: Determine the precision redundancy bits of the monitoring values ​​C2 relative to D2, C3 relative to D3, ..., Cc relative to Dc in sequence according to S13 to S14, and select the precision redundancy bit with the smallest value as the standard redundancy bit E1 of the rounding parameter to be used by monitoring task A1 based on task log data B1. S16: Obtain the standard redundancy bits E2, E3, ..., Eb of the parameters to be rounded in monitoring task A1 based on task log data B2, B3, ..., Bb in sequence according to S12 to S15; select the standard redundancy bit with the smallest value from the standard redundancy bits E1, E2, ..., Eb as the redundancy rounding bit of the parameters to be rounded in monitoring task A1; at the same time, take the interval between the end time of the monitoring period corresponding to task log data B1 and the start time of the monitoring period corresponding to task log data B2 as the redundancy rounding duration of the parameters to be rounded in monitoring task A1. S17: Calculate and obtain the redundant rounding bits and redundant rounding duration of the parameters to be rounded in monitoring tasks A2, A3, ..., Aa in sequence according to S11 to S16; generate a redundant rounding table of the parameters to be rounded based on the redundant rounding bits and redundant rounding duration of the parameters to be rounded in monitoring tasks A1, A2, ..., Aa. S18: Select all monitoring parameters of the target goaf as parameters to be rounded in sequence, and generate a redundant rounding table of all monitoring parameters of the target goaf according to S11 to S17.

4. The method according to claim 3, characterized in that, Historical task log data used to generate redundant rounding tables is collected from historical monitoring periods when the initial rounding mechanism was not enabled.

5. The method according to claim 1, characterized in that, For a parameter monitoring unit, after receiving the redundancy rounding table of the corresponding monitoring parameter, the steps for determining the rounding index of the monitoring parameter at the next moment based on the time of receipt are as follows: S21: Obtain the time J1 when the redundancy rounding table of the monitoring parameters is received, and trace back to time P2 from time J1. Based on the traced time P2, extract several monitoring tasks from the monitoring tasks corresponding to all task information stored in the preprocessing unit within the time P2. P2 is the redundancy rounding time with the largest redundancy rounding time value among all monitoring tasks containing monitoring parameters. S22: Based on the task information of each monitoring task, deduplicate all extracted monitoring tasks and label all deduplicated monitoring tasks as H1, H2, ..., Hh, h≥1; S23: Obtain the time J2, which is the closest time to the current time, from all the extracted monitoring tasks to the time when the task information is stored in the pre-processing unit; S24: Determine whether monitoring task H1 is a valid rounding task for the monitoring parameters at the next moment. If J1-J2≤P3, then monitoring task H1 is determined to be a valid rounding task for the monitoring parameters at the next moment; otherwise, monitoring task H1 is determined not to be a valid rounding task for the monitoring parameters at the next moment. P3 is the preset interval of the monitoring cycle. S25: Sequentially determine whether monitoring tasks H2, H3, ..., Hh are valid rounding tasks for the monitoring parameters at the next time step, obtain all valid rounding tasks that are determined to be the monitoring parameters at the next time step, extract the redundant rounding bits of all valid rounding tasks from the redundant rounding table of the monitoring parameters, and select the redundant rounding bit with the smallest value as the rounding index of the monitoring parameters at the next time step.

6. The method according to claim 5, characterized in that, In S21, among all the extracted monitoring tasks, the task information of any one monitoring task contains the monitoring parameters, and among all the extracted monitoring tasks, at least one monitoring task's task information is stored only once during the backtracking P2 time period.

7. The method according to claim 1, characterized in that, At each monitoring cycle interval, task information for all monitoring tasks in the corresponding monitoring cycle is pre-entered and stored.

8. A data processing device for monitoring coal mine goaf areas, characterized in that, The device includes: The data acquisition module is used to collect the monitoring values ​​of various monitoring parameters of the target goaf area at the current time; The rounding module is used to round the monitoring value of each monitoring parameter collected at the current time according to the pre-stored redundant rounding table of the monitoring parameter, so as to obtain the initial rounded value of the monitoring parameter. The generation module is used to generate monitoring data for the target goaf area based on the initial values ​​of all monitoring parameters at the current time. The acquisition module is used to acquire task information for all monitoring tasks within a monitoring period that matches the current time. The task information includes monitoring parameters and their numerical rounding rules. The rounding module is also used to extract each monitoring parameter and its numerical rounding rule for any monitoring task obtained. For each monitoring parameter, the module extracts the initial rounded value of the monitoring parameter from the monitoring data and performs numerical rounding on the initial rounded value according to the numerical rounding rule of the monitoring parameter to obtain the rounded value of the initial rounded value. The generation module is also used to generate and store the task rounding data for the current moment based on the rounding values ​​of each monitoring parameter of each monitoring task after obtaining the rounding values ​​of each monitoring parameter.

9. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the coal mine goaf monitoring data processing method as described in any one of claims 1-7.

10. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the steps of the coal mine goaf monitoring data processing method as described in any one of claims 1-7.

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