Intelligent scheduling method and system for coal mine solid-liquid double-waste cooperative processing

Abstract

The invention relates to the technical field of solid waste treatment, and discloses an intelligent scheduling method and system for coal mine solid-liquid double-waste cooperative processing, and the method comprises the steps: firstly carrying out the comparison and matching of mine scheduling data with a preset rule, carrying out the conflict coordination of a result, obtaining a preliminary scheduling instruction of equipment, extracting the multi-source dynamic operation parameters of the equipment according to the preliminary scheduling instruction, and carrying out the calculation of the multi-source dynamic operation parameters; determining a mine flow tolerance interval after collaborative amplitude limiting, aligning equipment power deviation with a preset equipment priority coefficient in the flow tolerance interval, generating a dynamic regulation and control input vector, mapping the vector to an equipment standard adjustment interval, and obtaining an optimization parameter adjustment value; and balancing the solid-liquid double-waste conveying flow according to the optimized parameter adjustment value to form a final scheduling instruction, and issuing the final scheduling instruction to an equipment execution unit to complete coal mine solid-liquid double-waste cooperative processing intelligent scheduling. According to the invention, the intelligent scheduling efficiency of coal mine solid-liquid double-waste cooperative treatment can be improved.

Description

Technical Field

[0001] This invention relates to the field of solid waste treatment technology, and in particular to an intelligent scheduling method and system for the synergistic treatment of solid and liquid wastes in coal mines. Background Technology

[0002] Existing scheduling methods often rely on manual experience or simple parameter settings for equipment control, failing to fully integrate multi-source scheduling data from the mine with real-time equipment operating status data. When processing data, these methods often lack effective coordination between parameters due to the failure to standardize the processing format and coordinate the limiting of dynamic operating parameters from multiple sources. This makes it impossible to accurately determine the mine's flow tolerance range, which in turn makes it easy for equipment to operate beyond safe or efficient limits, hindering the coordinated and efficient transport of both solid and liquid waste.

[0003] Existing technologies have shortcomings in combining equipment power deviation adjustment with priority coefficients. They fail to establish an effective mapping relationship between the two under reasonable flow constraints, resulting in a lack of scientific basis for the generation of dynamic control input vectors. Consequently, subsequent parameter adjustments and the formulation of final scheduling instructions also lack a reliable basis. This makes it difficult for the solid and liquid waste transport flow to reach a balanced state during equipment operation, which not only affects processing efficiency but may also lead to equipment failure or safety hazards due to flow imbalance. Therefore, it cannot meet the intelligent and efficient requirements for the co-processing of solid and liquid waste in coal mines. Summary of the Invention

[0004] This invention provides an intelligent scheduling method and system for the collaborative treatment of solid and liquid wastes in coal mines. Its main purpose is to solve the problem of low intelligent scheduling efficiency in the collaborative treatment of solid and liquid wastes in coal mines.

[0005] To achieve the above objectives, the present invention provides an intelligent scheduling method for the synergistic treatment of solid and liquid waste in coal mines, comprising:

[0006] The scheduling data of the mine is compared and matched with preset rules, and the results of the comparison and matching are reconciled to obtain the preliminary scheduling instructions for the equipment.

[0007] Based on the preliminary scheduling instructions, the multi-source dynamic operating parameters of the equipment are extracted, and the flow tolerance range of the mine is obtained by coordinating and limiting the multi-source dynamic operating parameters.

[0008] Within the flow tolerance range, the power deviation of the device is aligned with the preset device priority coefficient to obtain the dynamic control input vector of the device;

[0009] The dynamic control input vector is mapped to the standard adjustment range of the device to obtain the optimized parameter adjustment value of the device;

[0010] The final scheduling instruction for the equipment is obtained by balancing the transport flow of the solid and liquid wastes according to the optimized parameter adjustment values.

[0011] The final scheduling instruction is sent to the execution unit of the device.

[0012] In a preferred embodiment, the step of comparing and matching the mine's scheduling data with preset rules, and resolving conflicts in the comparison and matching results to obtain preliminary equipment scheduling instructions includes:

[0013] By integrating the multi-source data and equipment status data of the mine, a scheduling data sequence for the equipment is obtained;

[0014] By comparing the scheduling data sequence with the running constraints in the preset rule base one by one, a conflict identifier set is obtained;

[0015] The conflict identifier set is converted into preliminary scheduling instructions for the device.

[0016] In a preferred embodiment, the step of extracting the multi-source dynamic operating parameters of the equipment based on the preliminary scheduling instruction, and obtaining the flow tolerance range of the mine by coordinating and limiting the multi-source dynamic operating parameters, includes:

[0017] The data format of the multi-source dynamic operating parameters is unified to obtain the standard parameters of the device;

[0018] Based on the initial scheduling instructions, and in conjunction with the rated capacity of the equipment and the historical extreme values ​​of the equipment, dynamic limiting thresholds for various operating parameters of the equipment are set.

[0019] The standard parameters that exceed the dynamic limiting threshold are adjusted to obtain the compliant parameter set of the device;

[0020] The flow tolerance range of the mine is determined based on the set of compliance parameters.

[0021] In a preferred embodiment, the formula for calculating the flow tolerance range is:

[0022]

[0023] in, For traffic tolerance range, For the types of parameters currently being calculated, The total number of parameters. For the first The weighting coefficients of class parameters, For the first Normalized values ​​of class parameters, This is the maximum allowable flow rate for the device.

[0024] In a preferred embodiment, aligning the power deviation of the device with a preset device priority coefficient within the flow tolerance range to obtain the dynamic control input vector of the device includes:

[0025] Obtain the deviation rate between the real-time power and the rated power of the device;

[0026] A mapping relationship is established between the deviation rate and the preset equipment priority coefficient. Based on the mapping relationship, the deviation rate is weighted and corrected to obtain the dynamic control input vector of the equipment.

[0027] In a preferred embodiment, obtaining the deviation rate between the real-time power and the rated power of the device includes:

[0028] Based on the power sensing data of the device, the real-time active power is obtained by integrating the active power.

[0029] The real-time active power of the device is obtained, and the difference between the real-time active power and the rated power is taken as the absolute power deviation.

[0030] The ratio of the absolute power deviation to the rated power is taken as the deviation rate of the equipment.

[0031] In a preferred embodiment, mapping the dynamic control input vector to the standard adjustment range of the device to obtain the optimized parameter adjustment value of the device includes:

[0032] The range corresponding to the independent control parameters in the dynamic control input vector is taken as the original span of the device.

[0033] The width of the standard adjustment range is taken as the target span of the device;

[0034] Based on the proportional relationship between the original span and the target span, adjust the maximum and minimum values ​​of the independent control parameters;

[0035] The adjusted independent control parameters are smoothed and filtered to obtain the optimized parameter adjustment values ​​for the device.

[0036] In a preferred embodiment, the step of balancing the transport flow rates of the solid and liquid wastes according to the optimized parameter adjustment values ​​to obtain the final scheduling instruction for the equipment includes:

[0037] Extract the motor speed control quantity of the solid waste conveyor belt and the pump power control quantity of the liquid waste pumping station from the optimized parameter adjustment values;

[0038] The basic operating frequencies of the motor and the pump are calculated based on the motor speed control quantity and the pump power control quantity;

[0039] Based on the real-time solid-liquid ratio of the mine, the basic operating frequency of the pump is adjusted to obtain the synchronous operating frequency of the liquid waste pumping station;

[0040] The final scheduling command for the equipment is generated based on the basic operating frequency of the motor and the synchronous operating frequency of the pump.

[0041] In a preferred embodiment, the step of issuing the final scheduling instruction to the execution unit of the device includes:

[0042] The final scheduling instruction is encrypted, and the encryption result is sent to the execution unit of the device.

[0043] To address the aforementioned problems, the present invention also provides an intelligent scheduling system for the coordinated treatment of solid and liquid waste in coal mines, the system comprising:

[0044] The preliminary scheduling instruction module is used to compare and match the mine's scheduling data with preset rules, coordinate conflicts in the comparison and matching results, and obtain preliminary scheduling instructions for the equipment.

[0045] The flow tolerance interval module is used to extract the multi-source dynamic operating parameters of the equipment based on the preliminary scheduling instruction, and obtain the flow tolerance interval of the mine by coordinating and limiting the multi-source dynamic operating parameters.

[0046] The dynamic control module is used to align the power deviation of the device with the preset device priority coefficient within the flow tolerance range to obtain the dynamic control input vector of the device.

[0047] The optimization parameter adjustment module is used to map the dynamic control input vector to the standard adjustment range of the device to obtain the optimized parameter adjustment value of the device.

[0048] The final scheduling instruction module is used to balance the transport flow of the solid and liquid waste according to the optimization parameter adjustment value, and obtain the final scheduling instruction of the equipment;

[0049] The execution module is used to send the final scheduling instruction to the execution unit of the device.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. By comparing and matching mine scheduling data with preset rules and coordinating conflicts, a preliminary scheduling instruction is obtained. Based on this instruction, multi-source dynamic operating parameters of the equipment are extracted and coordinated to determine the flow tolerance range. Subsequently, within this range, the equipment power deviation and preset equipment priority coefficient are aligned to generate a dynamic control input vector. This vector can also be mapped to a standard adjustment range to obtain optimized parameter adjustment values. Finally, based on this, the solid and liquid waste transportation flow is balanced to generate the final scheduling instruction. The entire process realizes the refinement and intelligence of equipment scheduling, effectively improves the stability and accuracy of equipment operation in the co-processing of solid and liquid waste, ensures that the equipment can operate efficiently within a reasonable range, and improves the treatment efficiency and transportation coordination of solid and liquid waste.

[0052] 2. In this method, the final scheduling instruction is encrypted before being sent to the equipment execution unit, ensuring the security of the scheduling instruction transmission process and preventing the instruction from being tampered with or intercepted, which could lead to equipment malfunction. Furthermore, the various modules of the intelligent scheduling system have clear division of labor, with the preliminary scheduling instruction module, flow tolerance interval module, etc., each performing their own duties while cooperating with each other, ensuring that the entire scheduling process proceeds in an orderly and efficient manner. This further improves the overall intelligence level and operational reliability of the co-processing of solid and liquid waste in coal mines, providing stable and efficient technical support for the treatment of solid and liquid waste in the coal mine production process. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating an intelligent scheduling method for the synergistic treatment of solid and liquid wastes in coal mines, provided in an embodiment of the present invention.

[0054] Figure 2 This is a functional block diagram of an intelligent scheduling system for the collaborative treatment of solid and liquid waste in coal mines, provided in an embodiment of the present invention.

[0055] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0056] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0057] This application provides an intelligent scheduling method for the collaborative treatment of solid and liquid wastes in coal mines. The executing entity of this intelligent scheduling method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the intelligent scheduling method for the collaborative treatment of solid and liquid wastes in coal mines can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0058] Reference Figure 1 The diagram shown is a flowchart illustrating an intelligent scheduling method for the co-processing of solid and liquid wastes in coal mines, according to an embodiment of the present invention. In this embodiment, the intelligent scheduling method for the co-processing of solid and liquid wastes in coal mines includes:

[0059] In this embodiment of the invention, the step of comparing and matching the mine's scheduling data with preset rules, and coordinating conflicts in the comparison and matching results to obtain preliminary scheduling instructions for the equipment, is specifically used for:

[0060] By integrating the multi-source data and equipment status data of the mine, a scheduling data sequence for the equipment is obtained;

[0061] By comparing the scheduling data sequence with the running constraints in the preset rule base one by one, a conflict identifier set is obtained;

[0062] The conflict identifier set is converted into preliminary scheduling instructions for the device.

[0063] Specifically, the multi-source data of the mine covers solid waste discharge data, liquid waste discharge data, production progress data of different work areas, operational status data of various conveying pipelines within the mine, and temperature and humidity data of the external environment. Collection is achieved by installing dedicated data acquisition equipment at various relevant monitoring points in the mine. These devices automatically acquire various data from the corresponding monitoring points at set fixed time intervals and transmit the acquired data in real time to a dedicated data storage server for temporary storage. Next, equipment status data is collected. This data mainly includes the operational status information of various equipment involved in the treatment and transportation of solid and liquid waste, such as solid waste conveyor belts, liquid waste pumping stations, and various valves. Specifically, this includes the equipment's start-up and shutdown status, vibration during operation, surface temperature, and operating time. This is achieved by installing corresponding status sensors on each piece of equipment, such as vibration sensors, temperature sensors, and limit switches. These sensors continuously monitor the equipment's status information and convert the monitored information into transmittable data signals, which are then transmitted in real time to the aforementioned data storage server via the mine's internal communication network and stored together with the multi-source data.

[0064] Specifically, the specific content of the operational constraints in the preset rule base needs to be determined. The preset rule base is pre-established based on the safety operation requirements, process standards, and mine production specifications of the coal mine solid-liquid dual waste co-treatment equipment. The operational constraints cover categories such as equipment operating parameter constraints, solid-liquid dual waste conveying capacity constraints, and equipment co-operation sequence constraints. For example, the equipment operating parameter constraints clearly specify the maximum operating temperature of the solid waste conveyor belt motor and the maximum continuous operating time of the liquid waste pump station pump. The solid-liquid dual waste conveying capacity constraints clearly specify the minimum conveying capacity of the solid waste conveyor belt and the maximum conveying capacity of the liquid waste pump station during different operating periods. The equipment co-operation sequence constraints clearly specify the start-up sequence requirements of the solid waste conveyor belt and the liquid waste pump station. These operational constraints need to be retrieved one by one from the preset rule base to form a clear list of constraints to ensure that no omissions are made during subsequent comparisons. Next, the generated equipment scheduling data sequence is retrieved. The scheduling data sequence is formed by integrating multi-source data from the mine and equipment status data. It contains multi-source data and equipment status data related to the operation of each piece of equipment and is organized in chronological order. The scheduling data sequence of the corresponding equipment is accurately found from the data storage system according to the equipment identification information. It is completely extracted and loaded into the comparison processing module. During the loading process, the integrity of the scheduling data sequence needs to be verified to confirm that there are no data missing or damaged issues. If any problems are found, the data sequence is immediately retrieved from the backup data to ensure that the scheduling data sequence used for comparison is accurate.

[0065] Specifically, the conflict identifier set is categorized and organized. Each conflict identifier in the set contains the device number where the conflict occurred, the timestamp of the corresponding data, the category of operational constraints involved in the conflict, and the specific content of the conflict. All conflict identifiers are grouped according to the device number involved in the conflict, so that all conflict identifiers corresponding to the same device are grouped together. For example, all conflict identifiers related to device A are integrated into one group, and all conflict identifiers related to device B are integrated into another group. After grouping, the conflict identifiers within each device group are further subdivided according to the category of operational constraints involved in the conflict. For example, conflict identifiers belonging to motor temperature constraints and conflict identifiers belonging to delivery quantity constraints within device A group are classified separately to ensure that all types of conflicts for each device can be clearly distinguished, laying the foundation for subsequent targeted generation of scheduling instructions.

[0066] Furthermore, after collecting multi-source data and equipment status data, the two types of data stored in the data server are processed to unify their formats. Since the multi-source data and equipment status data come from different acquisition devices and monitoring points, there may be inconsistencies in data formats. For example, some data may be stored in text format and some in binary format. In this case, a dedicated data format conversion tool is used to convert all data into the same format according to a pre-set unified data format standard. For example, it is uniformly converted into text format that conforms to the mine's internal data management specifications. During the conversion process, the format of each piece of data is checked one by one to ensure that the converted data can accurately reflect the information of the original data and that there is no data loss or distortion. Subsequently, the multi-source data and equipment status data after standardization are correlated and matched. Based on the time node of data generation, the corresponding work area, and the associated equipment number, the data related to a certain equipment in the multi-source data are correlated with the status data of that equipment. For example, the solid waste discharge data and liquid waste discharge data of a specific work area within a certain time period are correlated with the status data of the solid waste conveyor belt and liquid waste pumping station responsible for the solid and liquid waste transportation in that area. This ensures that each set of correlated data can accurately reflect the correspondence between equipment operation and related production data in a specific scenario. Through such correlation and matching, a preliminary data set with equipment as the core is formed.

[0067] Furthermore, following a preset comparison order, each piece of data in the scheduling data sequence is compared one by one with the corresponding operational constraints in the preset rule base. The comparison order is logically determined according to the time sequence of equipment operation: first, the relevant data during the equipment startup phase is compared; then, the data during the stable operation phase is compared; and finally, the data during the equipment shutdown phase is compared. When comparing a single piece of data, the corresponding equipment operation dimension is first identified, such as whether it belongs to equipment temperature data, conveying volume data, or operation sequence data. Then, the operational constraints corresponding to that dimension are found from the constraint list. The actual data value in the scheduling data sequence is directly compared with the standard range or requirements specified in the constraints. For example, if the temperature data of the solid waste conveyor belt motor at a certain moment in the scheduling data sequence is X, and the maximum operating temperature constraint of the motor in the preset rule base is Y, if X is greater than Y, it is determined that the data conflicts with the operational constraints; if X is less than or equal to Y, it is determined that the data meets the operational constraints.

[0068] Furthermore, for each type of conflict within each equipment group, the causes of the conflicts and corresponding solutions are analyzed. Based on the pre-defined conflict handling logic, it is determined which operational adjustments to the equipment are needed to eliminate each type of conflict. For example, for a conflict within equipment group A involving a motor temperature exceeding its maximum limit, analysis shows that the current motor load is too high, leading to the temperature increase; the corresponding solution is to reduce the motor load. For a conflict involving a liquid waste pump station exceeding its maximum conveying capacity, analysis reveals that the pump operating frequency is too high; the solution is to reduce the pump operating frequency. During the analysis, the specific conflict content of each conflict identifier must be verified one by one to ensure that the analysis results completely match the actual conflict situation, avoiding invalid subsequent scheduling instructions due to analysis deviations. Then, based on the solutions determined by the analysis, specific equipment operation instructions are formulated. The operation instructions must clearly define the specific actions the equipment needs to perform, the time requirements for the actions, and the target state after the actions are performed. The formulation process must consider the actual operating capabilities of the equipment to ensure that the instructions are feasible and will not cause additional damage to the equipment.

[0069] In summary, the initial dataset after association and matching is arranged in an orderly manner. All associated data for each device is sorted sequentially according to the chronological order of data generation, strictly adhering to the timestamp information during the sorting process to ensure the temporal continuity and accuracy of the data sequence. Simultaneously, a unique device identifier and data sequence number are added to the sorted dataset for each device to facilitate subsequent management and retrieval. After this orderly arrangement, a scheduling data sequence is finally formed for each device. This scheduling data sequence fully encompasses multi-source data and device status data related to the device's operation, and the data is organized in chronological order, allowing it to be directly used for subsequent comparison operations with the operating constraints in the preset rule base.

[0070] In summary, after comparing each piece of data with its corresponding operational constraints, conflict identifiers are generated for conflicting comparison results. Each conflicting piece of data is assigned a unique conflict identifier, which includes the device number where the conflict occurred, the timestamp of the data, the category of operational constraints involved, and the specific content of the conflict. After generating the conflict identifiers, all conflict identifiers are organized according to the time sequence of the comparisons to ensure that each conflict identifier clearly corresponds to the specific conflicting data and related constraints in the scheduling data sequence. This ultimately forms a complete set of conflict identifiers. This set accurately records all cases where the scheduling data sequence conflicts with the operational constraints in the preset rule base and can be directly used for subsequent conversion into preliminary device scheduling instructions.

[0071] In summary, the established individual equipment operation instructions are integrated, and all operation instructions for the same equipment are arranged in an orderly manner according to the order of equipment operation time and operation logic to form a preliminary scheduling instruction draft for the equipment. When arranging, operation instructions corresponding to urgent conflicts should be given priority. For example, if the equipment temperature is too high and may cause damage, operation instructions related to reducing the temperature should be placed first to ensure that the equipment can handle urgent problems first. At the same time, it is necessary to check whether there are logical contradictions between the instructions. For example, if one instruction requires increasing the motor speed, another instruction requires reducing the motor load. If there are contradictions, they should be adjusted immediately to ensure that all operation instructions in the instruction draft can be executed in a coordinated manner without interfering with each other. The draft preliminary scheduling instructions are verified. Verification includes checking whether the instructions accurately correspond to all conflicts in the conflict identifier set, whether the instruction content complies with equipment operation safety specifications, and whether the instruction execution steps are operable. During verification, each conflict in the conflict identifier set is checked one by one to confirm that each conflict has a corresponding operation instruction for resolution. At the same time, the preset equipment safety operation standards are referenced to check whether the equipment operations required in the instructions are within the safe range. If the verification finds that the instructions have missing conflicts, do not comply with safety specifications, or are inoperable, the process is immediately reversed and improved until all verification items pass. Finally, a formal preliminary equipment scheduling instruction is formed. This preliminary scheduling instruction fully covers all conflict resolution needs of the equipment and is safe and feasible, and can be used for subsequent operations to extract multi-source dynamic operating parameters of the equipment.

[0072] In this embodiment of the invention, when extracting the multi-source dynamic operating parameters of the equipment based on the preliminary scheduling instruction, and obtaining the flow tolerance range of the mine by coordinating and limiting the multi-source dynamic operating parameters, the specific method is as follows:

[0073] The data format of the multi-source dynamic operating parameters is unified to obtain the standard parameters of the device;

[0074] Based on the initial scheduling instructions, and in conjunction with the rated capacity of the equipment and the historical extreme values ​​of the equipment, dynamic limiting thresholds for various operating parameters of the equipment are set.

[0075] The standard parameters that exceed the dynamic limiting threshold are adjusted to obtain the compliant parameter set of the device;

[0076] The flow tolerance range of the mine is determined based on the set of compliance parameters.

[0077] Specifically, the sources and types of multi-source dynamic operating parameters are clearly defined. These parameters are extracted from the equipment during operation based on the initial scheduling instructions and cover real-time temperature data, real-time speed data, real-time power data, and real-time transport flow data. Different types of parameters come from different monitoring sensors on the equipment. For example, temperature data comes from a temperature sensor, speed data from a speed sensor, power data from a power sensor, and transport flow data from a flow sensor. All these multi-source dynamic operating parameters of different sources and types need to be collected into a dedicated data processing terminal. During the collection process, wired communication between the terminal and each sensor is used to ensure stable parameter transmission and no data loss. At the same time, the acquisition time and equipment location information corresponding to each type of parameter are recorded to provide a basis for subsequent format unification.

[0078] Specifically, key operational requirements are extracted from the preliminary scheduling instructions. These instructions, generated previously through conflict identifier sets, clearly define the specific operations the equipment must perform, the execution time, and the target state. Requirements related to equipment operating parameters are selected from these instructions. Simultaneously, the time limits for parameter adjustments and the description of the target state are recorded to ensure that subsequent dynamic threshold setting aligns with the instructions and avoids conflicts between thresholds and scheduling objectives. Next, the rated capacity data of the equipment is retrieved. This data consists of core performance parameters provided by the manufacturer at the time of equipment delivery and stored in the basic database of the equipment management system. It includes the rated values ​​of various operating parameters, such as the rated power and rated speed of motors, the rated flow and rated pressure of pumps, and the rated conveying capacity of conveyor belts. The rated capacity data of the corresponding equipment is accurately located in the database using the equipment's unique identifier. This data is then completely exported and organized into a table. The table must clearly indicate the parameter name, rated value, and corresponding unit to ensure the rated capacity data is clearly traceable and provides a benchmark for setting thresholds.

[0079] Specifically, the standard parameters of the equipment are matched one by one with the set dynamic limiting thresholds. The standard parameters are obtained after unifying the data format of multi-source dynamic operating parameters, including various parameters such as the real-time temperature, speed, power, and flow rate of the equipment. The dynamic limiting thresholds specify the allowable range of each type of parameter. During the matching, the parameters are matched one by one according to their types. For example, the motor temperature parameter in the standard parameters is matched with the motor temperature threshold in the dynamic limiting thresholds, and the pump flow rate parameter in the standard parameters is matched with the pump flow rate threshold in the dynamic limiting thresholds. This ensures that each standard parameter can find its corresponding dynamic limiting threshold, laying the foundation for subsequent judgment on whether the threshold is exceeded. Next, each matched standard parameter is subjected to threshold exceedance judgment. During the judgment process, the actual value of the standard parameter is directly compared with the upper and lower limits of the corresponding dynamic limiting threshold. If the actual value of the standard parameter is higher than the upper limit of the dynamic limiting threshold or lower than the lower limit of the dynamic limiting threshold, the standard parameter is determined to exceed the dynamic limiting threshold. If the actual value of the standard parameter is between the upper and lower limits of the dynamic limiting threshold, the standard parameter is determined to meet the threshold requirements. After the judgment is completed, all standard parameters that exceed the dynamic limiting threshold are filtered out separately to form an exceedance parameter list. The list must clearly record the type of exceedance parameter, the actual value, the corresponding upper and lower limits of the dynamic limiting threshold, and the direction of exceedance to ensure that the exceedance parameter information is complete and searchable.

[0080] Specifically, core parameters related to mine flow rate are extracted from the compliance parameter set. The compliance parameter set is obtained after adjusting the standard parameters that exceed the dynamic limit threshold. It includes all operating parameters of the equipment that meet the dynamic limit threshold requirements. Among them, the core parameters related to mine flow rate mainly include the conveying efficiency parameters of the solid waste conveyor belt, the pump displacement parameters of the liquid waste pump station, and the flow capacity parameters of the conveying pipeline. During extraction, the parameters are accurately filtered in the compliance parameter set through parameter classification tags. These core parameters are exported separately and a flow-related parameter list is established. The list must clearly indicate the name, current value, corresponding equipment component, and parameter attributes of each core parameter to ensure that the extracted parameters directly affect the conveying flow rate of both solid and liquid waste in the mine, providing key data support for the subsequent determination of the flow tolerance range.

[0081] Furthermore, a unified data format standard needs to be established. This standard should be formulated in conjunction with the parameter management requirements of coal mine solid-liquid dual-waste co-treatment equipment, clearly defining the data storage structure, numerical representation method, unit labeling rules, and data identification format. For example, in terms of storage structure, each parameter data entry should include a fixed field of "equipment number - parameter type - acquisition time - value - unit"; in terms of numerical representation method, all values ​​should be in decimal integer form or decimal number with two decimal places, avoiding fractions or scientific notation; in terms of unit labeling rules, temperature data should be uniformly represented in degrees Celsius, rotational speed data in revolutions per minute, power data in kilowatts, and flow rate data in cubic meters per hour; in terms of data identification format, a combination of letters and numbers should be used, such as temperature parameters identified as "T-equipment number" and rotational speed parameters identified as "N-equipment number", ensuring that all parameters have a unified format and clear identifiability.

[0082] Furthermore, historical operating extreme values ​​of the equipment are obtained. These are the maximum and minimum values ​​reached by various parameters during a period of operation and are stored in the equipment's operating history database. By inputting the equipment identification number and parameter type through a data query tool, historical operating data for each type of parameter can be filtered out. Similarly, the historical maximum and minimum values ​​of various parameters such as motor power, speed, pump flow rate, and pressure are identified. These extreme values ​​are recorded according to parameter type, and the occurrence time and equipment operating conditions at that time are also marked, providing practical operational references for subsequent threshold setting. Combining the key operational requirements of the initial scheduling instruction, the equipment's rated capacity, and historical operating extreme values, the dynamic limiting threshold ranges for various operating parameters are determined. For each type of parameter, the rated value in the equipment's rated capacity data is first used as the baseline, and then the actual fluctuation range of the parameter is determined by referring to the historical operating extreme values. At the same time, the threshold range is adjusted in conjunction with the operational requirements in the initial scheduling instruction.

[0083] Furthermore, for each out-of-limit parameter in the list, a specific limiting adjustment plan is formulated based on its out-of-limit direction and dynamic limiting threshold. If the actual value of the out-of-limit parameter is higher than the upper limit of the dynamic limiting threshold, the value of the parameter is adjusted to the upper limit of the dynamic limiting threshold; if the actual value of the out-of-limit parameter is lower than the lower limit of the dynamic limiting threshold, the value of the parameter is adjusted to the lower limit of the dynamic limiting threshold. During the adjustment process, adjustment commands must be sent through the control interface of the equipment. The commands must clearly indicate the type of parameter to be adjusted, the target adjustment value, and the confirmation requirements after the adjustment is completed, to ensure that the equipment can accurately execute the adjustment operation. At the same time, the execution time of the adjustment operation and the operator information are recorded for subsequent traceability.

[0084] Furthermore, the importance of the extracted flow-related parameters was assessed. Using the safe and efficient operation of the co-treatment of solid and liquid waste in coal mines as the evaluation criterion, the influence of each parameter on mine flow was analyzed. Specifically, the conveying efficiency parameter of the solid waste conveyor belt directly determines the unit time conveying volume of solid waste, having the highest impact on flow; the pump displacement parameter of the liquid waste pump station directly determines the unit time conveying volume of liquid waste, with a second-highest impact; and the flow capacity parameter of the conveying pipeline affects the transmission speed of solid and liquid waste within the pipeline, with a slightly lower impact than the previous two. A corresponding weight coefficient was assigned to each parameter according to its degree of influence; the higher the degree of influence, the larger the weight coefficient. After allocation, the weight coefficient and evaluation basis for each parameter were recorded to form a parameter weight allocation table. This ensures that the weight coefficient setting conforms to the actual influence law of parameters on flow in operation, avoiding deviations in subsequent flow tolerance interval calculations due to unreasonable weight allocation. Then, the values ​​of the flow-related parameters are standardized. Since the units and value ranges of different flow-related parameters differ, directly using the original values ​​will affect subsequent analysis. Therefore, all parameter values ​​need to be converted into a unified dimensionless value range. During the conversion, the value range of the parameter within the rated capacity range of the equipment is used as the benchmark. The current parameter value is compared with the maximum value of the range. If the larger the parameter value is more beneficial to the flow, the current value is divided by the rated maximum value of the parameter to obtain the standardized value. If the smaller the parameter value is more beneficial to the flow, the difference between the rated maximum value and the current value is divided by the rated maximum value to obtain the standardized value. After completing the standardization of all parameters, a standardized parameter table is formed to ensure that all parameters can be comprehensively calculated in the same dimension.

[0085] In summary, each collected multi-source dynamic operating parameter undergoes a format check. Following a defined unified data format standard, the storage structure, numerical representation, unit, and data identifier of each parameter are verified sequentially. If a parameter is found to have missing fields in its storage structure, such as a missing acquisition time, the acquisition time corresponding to that parameter is retrieved from the sensor's historical storage data using the data processing terminal's backtracking function and then supplemented. If the value is found to be in scientific notation, it is converted to a decimal integer or a decimal number with two decimal places using the terminal's built-in format conversion function. If the data identifier does not conform to the rules, it is regenerated according to the format "parameter type abbreviation - device number" and the original identifier is replaced. After completing the format checks and corrections of all parameters, a consistency verification is performed on the corrected parameters. During the verification, multiple data points of the same type from the same device are sorted by acquisition time to check whether the numerical change trend of the parameters conforms to the operating rules of the device. At the same time, it is verified that the units and data identifiers of the parameters are completely consistent across all entries. If there are abnormal jumps, it is further verified whether there is a sensor malfunction during the parameter acquisition process. If it is confirmed that the abnormal data is caused by a sensor malfunction, the abnormal data is removed and the normal speed data at the corresponding time point is retrieved from the backup sensor to ensure the accuracy of the parameters. At the same time, the units and data identifiers of all speed parameters are reconfirmed to ensure that there are no inconsistencies. After the consistency verification, the standard parameters of the device that conform to the unified format standard are finally obtained. These standard parameters can be directly used in subsequent operations to set dynamic limit thresholds for various operating parameters of the device in conjunction with the initial scheduling instructions.

[0086] In summary, the final dynamic limiting thresholds are verified. Verification involves comparing the thresholds with the target state in the initial scheduling instructions, the safe range of the equipment's rated capacity, and historical stable operating parameters. This verifies whether the thresholds ensure the equipment does not exceed safe operating boundaries during scheduling instructions and whether the objectives in the instructions are achieved. It also checks whether the motor temperature stabilizes within the threshold range after load adjustment according to the instructions. Furthermore, it verifies whether the motor power threshold meets the load reduction requirements in the instructions and whether it is higher than the minimum power required for basic equipment operation to prevent equipment shutdown. If the verification reveals that a certain parameter's threshold does not meet requirements, such as a lower power threshold setting that could cause equipment shutdown, it is readjusted by combining the minimum operating power requirement in the equipment's rated capacity with the historical minimum stable operating power. This adjustment continues until the dynamic limiting thresholds for all parameters pass verification. Finally, the dynamic limiting thresholds for various operating parameters of the equipment are determined, and these thresholds can be directly used for subsequent limiting adjustments of standard parameters that exceed the range.

[0087] In summary, after adjusting all out-of-limit parameters, the adjusted parameters and the original standard parameters that met the threshold requirements are collected and categorized according to parameter type. For example, all temperature parameters are grouped together, and all flow parameters are grouped together. During the categorization process, the value of each parameter is checked again to confirm that the adjusted out-of-limit parameters are within the dynamic limit threshold range, and that the values ​​of the non-out-of-limit parameters remain unchanged and still meet the threshold requirements, avoiding any omissions in the adjustment or situations where the adjusted parameters still exceed the limits. Finally, all categorized parameters undergo compliance verification. Verification includes checking whether the parameter values ​​are all within the corresponding dynamic limit threshold range, whether the parameter units are consistent with the standard parameter requirements, and whether the corresponding equipment identification is accurate. Each parameter is checked one by one during verification. If a parameter is found to still exceed the limit, the previous step is returned to re-adjust it according to the limit adjustment plan. If inconsistent parameter units or incorrect equipment identification are found, they are corrected according to the standard parameter format requirements until all parameters meet the compliance requirements, ultimately forming a set of compliant parameters for the equipment.

[0088] In summary, by combining the parameter weighting table and the standardized parameter table, the lower limit of the flow tolerance interval is calculated. The calculation first multiplies the standardized value of each flow-related parameter by its corresponding weight coefficient to obtain the weighted contribution value of each parameter. Then, the maximum allowable flow rate of the equipment is obtained, and the total weighted contribution value is multiplied by the maximum allowable flow rate of the equipment. The result is the lower limit of the flow tolerance interval. Next, the upper limit of the flow tolerance interval is determined. The upper limit of the flow tolerance interval is directly taken from the maximum allowable flow rate of the equipment. This is because the maximum allowable flow rate of the equipment is the highest flow limit set by the manufacturer based on factors such as equipment structural strength and operational safety. Exceeding this limit will cause equipment overload and trigger safety failures. Therefore, no additional calculation is needed; the determined maximum allowable flow rate of the equipment is directly retrieved from the equipment basic information database as the upper limit of the flow tolerance interval. Finally, the lower and upper limits of the flow tolerance interval are integrated to form the complete mine flow tolerance interval.

[0089] In this embodiment of the invention, the calculation formula for the flow tolerance interval is specifically used for:

[0090]

[0091] in, For traffic tolerance range, For the types of parameters currently being calculated, The total number of parameters. For the first The weighting coefficients of class parameters, For the first Normalized values ​​of class parameters, This is the maximum allowable flow rate for the device.

[0092] Specifically, the parameters for the flow tolerance range need to be determined in conjunction with the intelligent scheduling process for the co-processing of solid and liquid waste in coal mines. The maximum allowable flow rate of the equipment is a core performance parameter clearly marked by the manufacturer when the equipment leaves the factory. It is stored in the equipment's basic information database and can be directly retrieved through the equipment's unique identification number, ensuring that it reflects the maximum flow rate limit for safe operation of the equipment. The weighting coefficients of class parameters are set based on the degree of influence of the parameter on the flow rate. After extracting the multi-source dynamic operating parameters of the equipment, and combining the requirements of coal mine production processes and equipment operating experience, the importance of each parameter is evaluated. The higher the degree of influence, the larger the weighting coefficient. After the evaluation is completed, a weighting coefficient table is generated and stored in the parameter configuration module. The normalized values ​​of class parameters are derived from the processing of multi-source dynamic operating parameters. First, the data format of the multi-source dynamic operating parameters is standardized to obtain standard parameters. Then, the standard parameters are compared with the rated range of each parameter, and through fixed logic, they are converted into dimensionless values ​​between 0 and 1, i.e., normalized values. These values ​​are updated as the equipment's real-time operating status changes and are stored in the real-time parameter database. The total number of parameters is determined after extracting and classifying the multi-source dynamic operating parameters. The total number of parameters is the number of all parameter types involved in the flow tolerance range calculation. For example, when parameters include conveyor belt efficiency, pump displacement, and pipeline flow capacity, the total number of parameters is the number of these types. The statistical results are adjusted in real-time as the number of parameter types increases or decreases.

[0093] Furthermore, this range represents the flow rate range within which the equipment can safely and efficiently transport both solid and liquid waste. The calculation first involves weighting and summing the normalized values ​​of each parameter using weighted coefficients in the numerator, then dividing by the sum of these coefficients to obtain an average coefficient reflecting the overall state of all parameters. This average coefficient is then multiplied by the equipment's maximum allowable flow rate to obtain the lower limit of the flow tolerance range. The upper limit of the range is directly taken from the equipment's maximum allowable flow rate. The resulting flow tolerance range ensures that when the equipment operates within this range, it will not suffer from insufficient processing efficiency due to excessively low flow rates, nor will it exceed the equipment's safe operating capacity due to excessively high flow rates. This provides a clear flow constraint basis for subsequently aligning equipment power deviations with preset equipment priority coefficients and generating dynamic control input vectors within the range, ensuring the stability and safety of the coal mine's solid-liquid co-processing of waste.

[0094] In general, the trend of the formula is reflected in the impact of changes in various parameters on the lower limit of the flow tolerance range. When the normalized value of a class parameter increases, if the weighting coefficient of that parameter is fixed, the weighted sum of the numerator will increase accordingly, leading to an increase in the average coefficient. This, in turn, raises the lower limit obtained after multiplying it by the maximum allowable flow rate of the equipment, causing the lower limit of the flow tolerance interval to move closer to the upper limit and the interval range to shrink. When the first... When the normalized value of the class parameter decreases, the weighted sum of the numerator decreases accordingly, the average coefficient decreases, the lower limit value decreases, the lower limit of the flow tolerance interval moves further away from the upper limit, and the interval range expands. When the first... When the weight coefficient of a class parameter increases, if the normalized value of that parameter is fixed, its contribution to the weighted sum of the numerator will increase. If the normalized value is greater than other parameters, it will increase the average coefficient, raise the lower limit, and narrow the range. If the normalized value is less than other parameters, it will decrease the average coefficient, lower the lower limit, and widen the range. The maximum allowable flow rate of the equipment remains constant; its variation is determined solely by the equipment's performance and is unrelated to other parameters. When the maximum allowable flow rate increases, the upper and lower limits of the flow tolerance range will rise synchronously, and the range will expand proportionally. When the maximum allowable flow rate decreases, the upper and lower limits will decrease synchronously, and the range will narrow proportionally. Changes in the total number of parameters affect the number of terms in the summation of the numerator and denominator. If the product of the normalized value and weight coefficient of a newly added parameter is higher than the original average level, it will increase the average coefficient, raise the lower limit, and narrow the range. If the product of the newly added parameter is lower than the original average level, it will decrease the average coefficient, lower the lower limit, and widen the range. Conversely, removing a parameter will reverse the trend.

[0095] In this embodiment of the invention, when aligning the power deviation of the device with a preset device priority coefficient within the flow tolerance range to obtain the dynamic control input vector of the device, the specific method is as follows:

[0096] Obtain the deviation rate between the real-time power and the rated power of the device;

[0097] A mapping relationship is established between the deviation rate and the preset equipment priority coefficient. Based on the mapping relationship, the deviation rate is weighted and corrected to obtain the dynamic control input vector of the equipment.

[0098] Specifically, the system acquires power sensing data from the equipment. This data is collected in real-time by power sensors installed in the equipment's power supply circuit. These sensors continuously monitor electrical parameters such as current, voltage, and power factor during equipment operation. The collected power sensing data is transmitted in real-time to a data acquisition server via an industrial Ethernet network within the mine. The TCP / IP protocol is used during transmission to ensure data stability and integrity. The data acquisition server categorizes and stores the power sensing data according to equipment number and acquisition time, forming an equipment power data log for subsequent extraction and processing. The system extracts current, voltage, and power factor data from the equipment power data log, including the current moment and the previous few seconds. During extraction, the target equipment number and the time range of the previous few seconds are entered into a data query tool. The system automatically filters out all power sensing data for that equipment within the corresponding time range. This data is then exported to dedicated data processing software. After export, the data undergoes an integrity check to ensure that no current, voltage, or power factor data is missing for each second. If any data is missing, the data acquisition server immediately retrieves the missing time period's power sensing data to supplement it, ensuring the accuracy and completeness of the power data used for calculation.

[0099] Specifically, the system first retrieves the preset equipment priority coefficients. These coefficients are pre-set based on the importance of each piece of equipment in the coal mine solid-liquid dual-waste co-treatment system and are stored in the system's priority configuration database. This database stores coefficients according to equipment type, such as solid waste conveyor belts, liquid waste pumping stations, and conveying pipeline valves. Each equipment type corresponds to a unique priority coefficient. For example, solid waste conveyor belts, as the core equipment for solid waste transportation, have a higher priority coefficient, while liquid waste pumping stations, as the core equipment for liquid waste transportation, have a second-highest priority coefficient. When retrieving the coefficients, the system inputs the type information of the target equipment through the equipment management system. The system automatically matches and retrieves the preset equipment priority coefficients corresponding to that equipment from the priority configuration database. After retrieval, the numerical range of the coefficients and the corresponding equipment type descriptions need to be verified to ensure that the retrieved preset equipment priority coefficients completely match the target equipment. Next, the standard for dividing the deviation rate into intervals is determined. The deviation rate is the ratio between the real-time power of the equipment and the rated power calculated previously. Its value may be positive, negative or zero. The interval division standard needs to be formulated in combination with the equipment's operational safety requirements and power adjustment needs. Specifically, it is divided into multiple continuous and non-overlapping intervals. After the interval division is completed, a deviation rate interval table is formed. The table needs to indicate the upper and lower limits of each interval and the description of the degree of deviation to ensure that there is a clear interval basis when establishing the mapping relationship later.

[0100] Furthermore, real-time active power is calculated based on the extracted current, voltage, and power factor data. The calculation first performs instantaneous active power calculation for each set of current and voltage data. Instantaneous active power is calculated by multiplying the current and voltage values ​​at the same moment by the power factor at that moment. For example, if the current is I, the voltage is U, and the power factor is cosφ at a certain moment, then the instantaneous active power at that moment is I×U×cosφ. The instantaneous active power for each second in the next few seconds is calculated in this way. These instantaneous active power values ​​are then added together to obtain the total active power. Finally, the total active power is divided by the number of seconds to obtain the average active power over the next few seconds. This average active power is the real-time active power of the equipment. After the calculation is completed, the real-time active power value and calculation time are recorded to form a real-time active power record sheet. Next, the rated power data of the equipment is retrieved. The rated power data of the equipment is a core performance parameter clearly marked by the manufacturer when the equipment leaves the factory. It is stored in the equipment basic information database of the mine equipment management system. This database is classified and managed according to equipment type and equipment number. When querying, the unique number of the target equipment is entered into the equipment management system. The system will automatically retrieve and display the detailed basic information of the equipment, filter out the rated power parameter, record the rated power value, and at the same time confirm that the unit of rated power is consistent with the unit of real-time active power. If the units are inconsistent, the units need to be converted according to the preset unit conversion standard to ensure that the units of the two are consistent and to avoid deviations in subsequent calculations.

[0101] Furthermore, a mapping relationship between deviation rate and preset equipment priority coefficient is established. Based on the deviation rate interval table and the extracted preset equipment priority coefficients, corresponding preset equipment priority coefficient application rules are assigned to each deviation rate interval. The rule setting logic is: the more severe the power deviation corresponding to the deviation rate, the greater the weight of the preset equipment priority coefficient on the deviation rate. The preset equipment priority coefficient is directly multiplied by the deviation rate as the weighting basis. The application rules corresponding to each deviation rate interval are organized into a mapping relationship table, which includes the deviation rate interval, the description of the deviation degree, and the preset equipment priority coefficient application rules, thus completing the establishment of the mapping relationship between deviation rate and preset equipment priority coefficient. Next, the interval to which the current equipment deviation rate belongs is determined. The deviation rate value of the current equipment is extracted from the previously calculated deviation rate record, and this value is compared with the deviation rate intervals in the mapping relationship table one by one to determine which interval it falls into. During the determination process, the upper and lower limits of the interval must be strictly defined. If the deviation rate value is exactly equal to the upper or lower limit of the interval, it is uniformly classified into a higher-level interval. After determining the interval, the preset equipment priority coefficient application rule corresponding to the interval is recorded to provide a specific basis for subsequent weighting correction.

[0102] In summary, the absolute power deviation of the equipment is calculated by subtracting the rated power of the equipment from the obtained real-time active power value. If the real-time active power is greater than the rated power, the absolute power deviation is positive, indicating that the current operating power of the equipment exceeds the rated power; if the real-time active power is less than the rated power, the absolute power deviation is negative, indicating that the current operating power of the equipment is lower than the rated power; if the two are equal, the absolute power deviation is zero. After calculation, the value and sign of the absolute power deviation are recorded to form an absolute power deviation record. The deviation rate of the equipment is calculated by dividing the obtained absolute power deviation value by the rated power of the equipment. The result is the deviation rate. During the calculation, it is important to retain two decimal places in the result.

[0103] In summary, the deviation rate is weighted and corrected according to the mapping relationship. Following the established rules for applying preset equipment priority coefficients, the preset equipment priority coefficients are multiplied by the deviation rate. Two decimal places are retained during the calculation to ensure the accuracy of the correction result. Each step of the calculation, along with the application rules, is recorded to form a weighted correction record sheet for subsequent traceability and verification. Finally, a dynamic control input vector for the equipment is constructed. This vector uses the weighted and corrected deviation rate as its core element, while also integrating the equipment's current operating status identifier and equipment type code. These three elements are combined in a fixed order: equipment type code – weighted and corrected deviation rate – operating status identifier, forming an ordered dynamic control input vector. After combination, the accuracy of each element in the vector is verified to ensure the correct equipment type code, consistency between the weighted and corrected deviation rate and the calculated result, and a match between the operating status identifier and the deviation rate. Once verification is successful, the final dynamic control input vector for the equipment is obtained. This vector can be directly used for subsequent mapping to the equipment's standard adjustment range.

[0104] In this embodiment of the invention, the step of obtaining the deviation rate between the real-time power and the rated power of the device is specifically used for:

[0105] Based on the power sensing data of the device, the real-time active power is obtained by integrating the active power.

[0106] The real-time active power of the device is obtained, and the difference between the real-time active power and the rated power is taken as the absolute power deviation.

[0107] The ratio of the absolute power deviation to the rated power is taken as the deviation rate of the equipment.

[0108] Specifically, the equipment's power sensing data acquisition program is initiated. The equipment's power sensing data is collected by a dedicated power sensor installed in the equipment's power supply circuit. This sensor has the function of real-time monitoring of current, voltage, and power factor. After the acquisition program is started, the sensor will continuously collect the instantaneous current value, instantaneous voltage value, and instantaneous power factor value of the equipment at a fixed frequency of once per second. During the acquisition process, the sensor will filter the raw electrical signal to remove noise signals caused by electromagnetic interference, ensuring that the collected current, voltage, and power factor data can accurately reflect the actual power consumption of the equipment. Each set of data collected will be automatically stamped with a timestamp and a unique equipment identifier, and sent to the power data storage module in real time through a dedicated data transmission link inside the mine. The system extracts power sensing data within a specified time period from the power data storage module. The specified time period is defined as a few seconds prior to the current time. During extraction, the unique identifier of the target device and the time range from a few seconds before the current time are entered in the data query interface. The system will automatically filter out all power sensing data of the device within the corresponding time period, including the instantaneous current value, instantaneous voltage value, and instantaneous power factor value per second. After extraction, the data integrity needs to be verified, and the timestamps are checked one by one to ensure that the data is complete and without duplication. If data for a certain time period is found to be missing, the historical data backtracking function of the sensor is immediately triggered to retrieve the backup data for that time period to supplement it, ensuring that the power sensing data used for calculation is complete and accurate.

[0109] Specifically, the functional module related to real-time active power acquisition in the equipment power monitoring system is activated. This module establishes a stable communication connection with the power sensors installed in the equipment power supply circuit. The power sensors continuously collect current, voltage, and power factor data during equipment operation and transmit the collected raw electrical signals to the power monitoring system multiple times per second. After receiving the signals, the system performs noise reduction processing to remove interference signals generated by the complex electromagnetic environment of the mine, ensuring that the data transmitted to the functional module can accurately reflect the actual power operation status of the equipment and provide reliable raw data support for subsequent calculation of real-time active power. In the functional modules of the power monitoring system, instantaneous active power is calculated on the processed current, voltage, and power factor data. During the calculation, the current data, voltage data, and power factor data at the same acquisition time are multiplied sequentially to obtain the instantaneous active power at that time. For example, the current data at a certain acquisition time is the current operating current of the equipment, the voltage data is the current voltage across the equipment, and the power factor data is the current power factor of the equipment. The result of multiplying the three is the instantaneous active power consumed by the equipment at that time. After the calculation is completed, the system will automatically record the value of the real-time active power, the calculation time, and the corresponding equipment number to form a real-time active power record sheet.

[0110] Specifically, the system retrieves the calculated absolute power deviation data from the data storage module. This data was previously obtained by comparing the real-time active power with the rated power. When storing the data, it is associated with the device number, calculation time, and the corresponding real-time active power and rated power values. When retrieving the data, the system enters the unique number of the target device and the data type identifier of the absolute power deviation in the data query interface. The system will automatically filter out the latest absolute power deviation value of the device and retrieve the rated power value corresponding to the calculation. This ensures that both values ​​come from the same calculation period and avoids errors in subsequent ratio calculations due to mismatched data periods. After retrieval, the absolute power deviation value and the rated power value are recorded in the corresponding columns of the deviation rate calculation worksheet. The retrieved absolute power deviation value and rated power value are checked for unit consistency. The unit of the absolute power deviation is consistent with the unit of the real-time active power and rated power, which is kilowatt. During the check, the unit label of the two values ​​in the worksheet is checked to confirm that they are both kilowatts. If the unit label is missing or inconsistent, the process is immediately traced back to the absolute power deviation calculation stage and the rated power retrieval stage to check the unit record in the data recording process. If the record is missing, it is added. If the unit is incorrect, the absolute power deviation is recalculated until the units of the two values ​​are completely consistent, so as to eliminate the interference factor of unit for subsequent ratio calculation.

[0111] Furthermore, the instantaneous active power is calculated for each set of extracted power sensing data. During the calculation, the instantaneous current value, instantaneous voltage value, and instantaneous power factor value corresponding to the same timestamp are multiplied. For example, if the instantaneous current value is I, the instantaneous voltage value is U, and the instantaneous power factor value is cosφ at a certain timestamp, I is multiplied by U to obtain the instantaneous apparent power. Then, the instantaneous apparent power is multiplied by cosφ to obtain the instantaneous active power corresponding to that timestamp. Following this method, the instantaneous active power values ​​corresponding to multiple sets of power sensing data are calculated sequentially. During the calculation process, attention should be paid to the uniformity of data units. The current unit is uniformly uniformly ampere, the voltage unit is uniformly uniformly volt, the power factor is a dimensionless value, and the calculated instantaneous active power unit is watt. After the calculation is completed, each instantaneous active power value is associated with its corresponding timestamp and stored to form an instantaneous active power sequence table.

[0112] Furthermore, the rated power data of the target equipment is retrieved from the equipment management database. The equipment management database stores the basic performance parameters of all coal mine solid-liquid dual waste co-treatment equipment, including equipment number, equipment type, rated power, rated speed, and other information. When retrieving the data, the unique number of the target equipment is entered in the database query interface, and the system will automatically retrieve and display the detailed basic parameters of the equipment. The rated power parameter is then selected from these parameters. At the same time, it is confirmed that the unit of the rated power is consistent with the unit of the real-time active power. After ensuring that the units of the two are consistent, the value of the rated power is recorded in the rated power information table to avoid errors due to unit issues when calculating the absolute power deviation in the future.

[0113] Furthermore, the ratio of absolute power deviation to rated power is calculated. In the calculation, the value of absolute power deviation is used as the dividend and the value of rated power is used as the divisor. For example, if the value of absolute power deviation is a specific kilowatt and the value of rated power is another specific kilowatt, the former value is divided by the latter value, and the quotient is the preliminary ratio result. Four decimal places must be retained in the calculation process to ensure the accuracy of subsequent result conversion.

[0114] In summary, active power integration is performed based on the instantaneous active power sequence table to obtain real-time active power. The core of active power integration is to calculate the cumulative value of instantaneous active power within a specified time period and take the average value. In specific operation, multiple instantaneous active power values ​​in the instantaneous active power sequence table are added together in sequence, and then the total cumulative value of active power is divided by the time length to obtain the average active power per unit time. This average active power is the real-time active power of the equipment.

[0115] In summary, the difference between the real-time active power value in the real-time active power record and the rated power value in the rated power information table is calculated. The calculation involves subtracting the rated power value from the real-time active power value. If the real-time active power value is greater than the rated power value, the result is positive, representing the absolute power deviation where the current operating power exceeds the rated power. If the real-time active power value is less than the rated power value, the result is negative, representing the absolute power deviation where the current operating power is lower than the rated power. If the two values ​​are equal, the result is zero, indicating that the current operating power is consistent with the rated power, with no absolute power deviation. After the calculation, the calculated absolute power deviation value, the corresponding real-time active power value, the rated power value, and the calculation time are integrated and recorded to obtain the final absolute power deviation of the equipment.

[0116] In summary, the initial ratio result is converted into a deviation rate by multiplying the initial ratio result by 100 and converting it into a percentage. This percentage value is the equipment's deviation rate. During the conversion process, the sign of the value must remain unchanged: a positive sign indicates that the equipment's real-time power exceeds the rated power, and a negative sign indicates that the equipment's real-time power is lower than the rated power. After the conversion, the deviation rate value is verified by dividing the deviation rate by 100 and then multiplying it by the rated power. The result is checked to see if it matches the absolute power deviation value. If they match, the deviation rate calculation is correct. If they do not match, the division operation and percentage conversion process are rechecked to find and correct any calculation errors, ultimately yielding the equipment's deviation rate.

[0117] In this embodiment of the invention, the step of mapping the dynamic control input vector to the standard adjustment range of the device to obtain the optimized parameter adjustment value of the device is specifically used for:

[0118] The range corresponding to the independent control parameters in the dynamic control input vector is taken as the original span of the device.

[0119] The width of the standard adjustment range is taken as the target span of the device;

[0120] Based on the proportional relationship between the original span and the target span, adjust the maximum and minimum values ​​of the independent control parameters;

[0121] The adjusted independent control parameters are smoothed and filtered to obtain the optimized parameter adjustment values ​​for the device.

[0122] Specifically, the generated dynamic control input vector for the equipment is retrieved. This vector was previously obtained by establishing a mapping relationship between the deviation rate and the preset equipment priority coefficient and then weighting and correcting the deviation rate. The vector contains elements such as equipment type code, weighted and corrected deviation rate, operating status identifier, and independent control parameters. During retrieval, the dynamic control input vector of the corresponding equipment is accurately located using the equipment's unique identifier number, ensuring that the retrieved vector is the latest generated version and completely matches the target equipment, avoiding subsequent operational deviations due to vector mismatch. After retrieval, the vector is completely imported into the parameter analysis tool, displaying all elements within the vector in a structured form, facilitating subsequent screening of independent control parameters. Independent control parameters in the dynamic control input vector are screened. Independent control parameters refer to parameters that directly affect the adjustment of the equipment's operating status and can be adjusted independently, including equipment motor speed control parameters, pump power adjustment parameters, conveyor belt tension control parameters, etc. During screening, a preset independent control parameter classification list is used to compare each element in the structured elements of the dynamic control input vector, identifying parameter types that meet the list's definition, ensuring that the extracted parameters are all core control parameters in the vector that can be operated independently.

[0123] Specifically, the system retrieves the standard adjustment range of the equipment. This standard adjustment range is pre-set based on the safe operation threshold, process requirements, and factory technical specifications of the coal mine solid-liquid dual waste co-treatment equipment. The database stores the standard adjustment ranges of each piece of equipment according to its unique identification number. Each range contains the upper and lower limits of the corresponding control parameters. For example, the standard adjustment range for the motor speed of the solid waste conveyor belt contains the upper and lower limits of the speed, and the standard adjustment range for the pump power of the liquid waste pumping station contains the upper and lower limits of the power. When retrieving the standard adjustment range, the system inputs the unique identification number of the target equipment, and the system automatically matches and extracts the standard adjustment ranges corresponding to all control parameters of the equipment. After extraction, it is necessary to verify whether the parameter type of the range is consistent with the actual control requirements of the equipment to ensure that the retrieved standard adjustment range is dedicated to the equipment and there is no mismatch.

[0124] Specifically, the system retrieves the original span and target span of the equipment. The original span is the result of determining the range corresponding to the independent control parameters in the dynamic control input vector, while the target span is the result of determining the width of the standard adjustment interval. Both are stored according to the type of independent control parameter. When retrieving the data, the system inputs the unique identifier of the target equipment, and automatically matches and extracts the original span and target span values ​​corresponding to all independent control parameters of that equipment. The system calculates the ratio between the original span and the target span for each independent control parameter. The calculation uses the target span as a benchmark, dividing the original span value by the target span value to obtain the ratio coefficient. During the calculation, the units of the original span and the target span must be consistent. If a unit inconsistency occurs, the system immediately backtracks to the determination stage of the original span and target span, checks the unit records, corrects them to a unified unit, and recalculates to ensure that the ratio coefficient accurately reflects the numerical relationship between the two. After the calculation is completed, the ratio coefficient of each parameter is recorded in the corresponding column of the span comparison table.

[0125] Specifically, the adjusted independent control parameters are retrieved. These parameters are the results of previous optimization adjustments based on the ratio between the original span and the target span, including motor speed control parameters, pump power adjustment parameters, etc. Each parameter has a recorded adjusted numerical sequence. During retrieval, the unique identifier of the target equipment and the data type of the adjusted independent control parameters are entered. The system automatically matches and extracts the complete numerical sequence of all independent control parameters for that equipment. After extraction, the temporal continuity of the numerical sequence needs to be verified to ensure that the value of each parameter is recorded according to the set acquisition frequency, without any time breaks or missing data. These numerical sequences are then imported into the smoothing and filtering tool in the parameter type-numerical sequence format. The size of the filtering window is set in the smoothing and filtering tool. The size of the filtering window is determined based on the acquisition frequency of the independent control parameters and the stability requirements of the equipment operation. It is uniformly set to a window of multiple data points; that is, each filtering calculation is centered on the current data point, including the previous and next few data points, forming a complete calculation window of continuous data points. If the current data point is at the beginning of the numerical sequence, a window of complete data points is formed by the current data point and the next few data points. If the current data point is at the end of the numerical sequence, a window of complete data points is formed by the current data point and the previous few data points, ensuring that each data point can be included in the corresponding filtering window for processing, avoiding data omissions due to the beginning or end of the sequence. The numerical sequence of each independent control parameter is calculated by sliding the filtering window. During calculation, the values ​​of the data points within each filtering window are added together to obtain the sum of the values ​​within that window. The sum is then divided by the number of data points to obtain the average value of that window. This average value is used as the smoothed value of the center data point of the current window. The filtering window is slid sequentially across the entire numerical sequence, from the beginning to the end, calculating the corresponding filtered value for each data point, forming the smoothed filtered numerical sequence of that independent control parameter.

[0126] Furthermore, for each parameter in the independent control parameter list, determine its corresponding numerical range in the dynamic control input vector. The numerical range is determined by tracing the generation process of the parameter, specifically by examining the parameter's value boundaries when considering the weighted correction deviation rate and the equipment operating status identifier. For example, the numerical range of the motor speed control parameter is set based on the equipment's rated speed and deviation rate correction results. Retrieve the maximum and minimum values ​​of the parameter from the parameter generation log and record them as the parameter's numerical range. For example, the maximum value of the motor speed control parameter may be a specific value in revolutions per minute, and the minimum value may be another specific value in revolutions per minute, forming a "minimum value - maximum value" interval record for the parameter. In this way, determine the numerical range of all parameters in the independent control parameter list in turn, ensuring that the value boundaries of each parameter are clearly traceable. Calculate the range for each independent control parameter. The range is calculated by subtracting the minimum value from the maximum value in the parameter's range. During the calculation, it is necessary to ensure that the units of the maximum and minimum values ​​are consistent. If there is a unit inconsistency, immediately backtrack to the value range determination stage, check the unit records in the parameter generation log, correct them to a unified unit, and recalculate the range to avoid errors in the range result due to unit issues. After the calculation is completed, record the range of each independent control parameter in the range calculation table.

[0127] Furthermore, for each interval in the standard adjustment interval list, the calculation logic for its corresponding interval width is clarified. The interval width refers to the difference between the upper and lower limits within the standard adjustment interval. Before calculation, it is necessary to confirm whether the units of the upper and lower limits within each interval are consistent. After confirming that the units are correct, the upper and lower limits of each interval are recorded to prepare for subsequent interval width calculations. The interval width of each standard adjustment interval is calculated by directly subtracting the lower limit from the upper limit. During the calculation, the same numerical precision as the upper and lower limits must be maintained to ensure that the interval width accurately reflects the actual range of the standard adjustment interval. After the calculation is completed, the interval width of each interval is associated with the corresponding parameter type and recorded to form an interval width detail table.

[0128] Furthermore, the current maximum and minimum values ​​of each independent control parameter are retrieved, i.e., the maximum and minimum values ​​of that parameter in the dynamic control input vector. These maximum and minimum values ​​are the results recorded when the numerical range of the independent control parameters was previously determined and are stored in the independent control parameter list. During retrieval, based on the parameter type in the span comparison table, the current maximum and minimum values ​​of the corresponding parameters are found in the independent control parameter list, and these maximum and minimum value data are added to the span comparison table to provide basic data for subsequent adjustment of the maximum and minimum values. The maximum and minimum values ​​of the independent control parameters are adjusted according to the proportional relationship. The adjustment logic is as follows: if the proportional coefficient is less than 1, it means that the original span is smaller than the target span, and the value range of the independent control parameter needs to be expanded; if the proportional coefficient is greater than 1, it means that the original span is larger than the target span, and the value range of the independent control parameter needs to be narrowed; if the proportional coefficient is equal to 1, no adjustment is needed. Specifically, during adjustment, the median value between the current maximum and minimum values ​​is first calculated, then the width of the adjusted value range is determined according to the proportional relationship. Then, the adjusted maximum value is obtained by adding half of the adjusted width to the median value, and the adjusted minimum value is obtained by subtracting half of the adjusted width from the median value. During the adjustment process, it is necessary to ensure that the adjusted threshold does not exceed the limit range for safe operation of the equipment. If it does, the equipment safety limit value should be used as the adjusted threshold to avoid operational risks to the equipment.

[0129] Furthermore, the rationality of the smoothed filtered numerical sequence is verified. During verification, the filtered values ​​are compared with the adjusted maximum and minimum values ​​of the independent control parameters to ensure that all filtered values ​​are between the adjusted maximum and minimum values, with no abnormal values ​​exceeding the maximum / minimum range. Simultaneously, the trend of the filtered numerical sequence is observed to confirm that the fluctuation range is significantly reduced compared to the original adjusted numerical sequence and conforms to the normal variation pattern of the equipment operating parameters. If a filtered value is found to exceed the maximum / minimum range or exhibits abnormal jumps, the corresponding filtered window data points are re-examined to confirm whether the error is due to incorrect original data. If the original data is incorrect, the average of adjacent data points is used, and the filtered value is recalculated. If the issue is with window settings, the window size is adjusted appropriately, and the filtering is repeated until the smoothed filtered numerical sequence fully meets the rationality requirements.

[0130] In summary, the original span of the equipment is determined by integrating the ranges of all independent control parameters. The original span is a comprehensive reflection of the ranges of all independent control parameters. During integration, the range values ​​of all independent control parameters in the range calculation table are classified and summarized according to parameter type. For example, the ranges of independent control parameters of speed are grouped into one group, and the ranges of independent control parameters of power are grouped into another group. After summarizing, the original span details are organized in the form of "parameter type - range value". The details table must fully present the range of each independent control parameter and its corresponding parameter type to ensure that the original span can fully reflect the value fluctuation range of the independent control parameters in the dynamic control input vector. After the sorting is completed, the details table is verified to check whether the calculation process of each range value is correct and whether the parameter type classification is accurate. If errors are found, they are corrected in time to finally obtain the original span of the equipment.

[0131] In summary, all interval widths in the interval width details table are integrated to determine the target span of the equipment. The target span is a comprehensive reflection of the standard adjustment interval widths corresponding to all control parameters of the equipment. During integration, the interval widths are classified and summarized according to parameter type. For example, the interval widths of all speed parameters are grouped into one group, and the interval widths of all power parameters are grouped into another group to ensure that the target span can fully reflect the standard adjustment range of all control parameters of the equipment. After the list is completed, it is verified to check whether the calculation process of each interval width is correct and whether the parameter type classification is accurate. If an error is found in the calculation of a certain interval width, it is immediately recalculated and corrected. Finally, the target span of the equipment with the interval width of the standard adjustment interval as the core is obtained.

[0132] In summary, the adjusted extreme values ​​of the independent control parameters are verified. Verification includes checking whether the difference between the adjusted maximum and minimum values ​​equals the target span, whether the adjusted extreme values ​​are within the safe operating range of the equipment, and whether the adjustment logic conforms to the proportional relationship requirements. During verification, each parameter is checked individually. If the difference between the adjusted extreme values ​​of a certain parameter does not equal the target span, or exceeds the safe range of the equipment, the proportional coefficient calculation, intermediate value calculation, and adjustment process are re-examined to identify and correct errors until the adjusted extreme values ​​of all parameters meet the requirements, thus completing the adjustment of the extreme values ​​of the independent control parameters.

[0133] In summary, the smoothed filtered numerical sequences of all independent control parameters are integrated to form the optimized parameter adjustment values ​​for the equipment. During integration, the smoothed filtered numerical sequence of each parameter is linked to the corresponding control dimension according to parameter type, ensuring that each optimized parameter adjustment value clearly corresponds to the specific control part and operational requirements of the equipment. After integration, all optimized parameter adjustment values ​​are finally confirmed to verify whether the parameter type, numerical range, and trend of change meet the subsequent operation and control requirements of the equipment. After confirmation, the final optimized parameter adjustment values ​​for the equipment are obtained. These optimized parameter adjustment values ​​can be directly used for subsequent operations based on the balancing solid-liquid dual waste conveying flow rate to generate the final scheduling instructions for the equipment.

[0134] In this embodiment of the invention, when balancing the transport flow of the solid-liquid dual waste according to the optimized parameter adjustment value to obtain the final scheduling instruction of the equipment, it is specifically used for:

[0135] Extract the motor speed control quantity of the solid waste conveyor belt and the pump power control quantity of the liquid waste pumping station from the optimized parameter adjustment values;

[0136] The basic operating frequencies of the motor and the pump are calculated based on the motor speed control quantity and the pump power control quantity;

[0137] Based on the real-time solid-liquid ratio of the mine, the basic operating frequency of the pump is adjusted to obtain the synchronous operating frequency of the liquid waste pumping station;

[0138] The final scheduling command for the equipment is generated based on the basic operating frequency of the motor and the synchronous operating frequency of the pump.

[0139] Specifically, the system retrieves the optimized parameter adjustment values ​​for the equipment. These values ​​are the result of smoothing and filtering the previously adjusted independent control parameters. They include smoothed filter value sequences categorized by parameter type, with each sequence associated with a corresponding equipment component identifier. During retrieval, the system inputs the mine number to which the target equipment belongs and the data category of the optimized parameter adjustment values. The system automatically filters all optimized parameter adjustment value data for that equipment and displays them in a structured table. The table clearly lists the parameter name, corresponding equipment component, smoothed filter value sequence, and data generation time, ensuring that the retrieved optimized parameter adjustment values ​​are complete and accurately match the target equipment, avoiding data confusion or omissions. Parameter filtering rules are established in the structured table. These rules are set based on the parameter characteristics of the motor speed control quantity of the solid waste conveyor belt and the pump power control quantity of the liquid waste pumping station. Specifically, parameters whose names contain motor speed and whose corresponding equipment component is labeled as solid waste conveyor belt are identified as parameters related to the motor speed control quantity of the solid waste conveyor belt; parameters whose names contain pump power and whose corresponding equipment component is labeled as liquid waste pumping station are identified as parameters related to the pump power control quantity of the liquid waste pumping station. After the filtering rules are established, they are entered into the rule configuration module of the parameter filtering tool to ensure that the tool can automatically identify the target parameters according to these rules, without the need for manual judgment, thus improving the efficiency and accuracy of filtering.

[0140] Specifically, the rated speed parameters of the solid waste conveyor belt motor and the rated power parameters of the liquid waste pump station pump are retrieved. These parameters are stored in the equipment basic database of the mine equipment management system. The database manages core performance parameters by equipment type and number. After retrieval, it is necessary to verify whether the units of the rated speed and the motor speed control quantity are consistent, and whether the units of the rated power and the pump power control quantity are consistent. If there is a unit difference, it is immediately converted using the system's built-in unit conversion function to ensure that the units of the rated parameters and control quantity parameters are completely matched. Subsequently, the rated speed and rated power are recorded in the basic parameter tables of the motor and pump, respectively.

[0141] Specifically, the real-time solid-liquid ratio of the mine is obtained from the mine solid-liquid ratio monitoring system. This monitoring system collects data by installing weight sensors and flow sensors at the mine solid waste discharge outlet and liquid waste discharge outlet, respectively. The weight sensors collect the solid waste discharge volume once per minute, and the flow sensors collect the liquid waste discharge volume once per minute. The system calculates the ratio of the solid waste discharge volume and the liquid waste discharge volume collected in the same minute to obtain the real-time solid-liquid ratio for that minute, forming a real-time solid-liquid ratio sequence table. The table includes timestamps, solid waste discharge volume, liquid waste discharge volume, and the corresponding solid-liquid ratio, ensuring that the obtained solid-liquid ratio data can reflect the actual output of solid and liquid waste in the mine in the recent period.

[0142] Specifically, the synchronous operating frequency of the pump is retrieved from the synchronous operating frequency calculation record of the liquid waste pumping station. This frequency is the result obtained after adjusting the basic operating frequency of the pump based on the real-time solid-liquid ratio of the mine. The record includes the synchronous operating frequency value, adjustment coefficient, calculation process, and verification results. During retrieval, the corresponding synchronous operating frequency value is found in the record based on the equipment identification of the liquid waste pumping station. It is confirmed that the value does not exceed the maximum allowable operating frequency of the pump and meets the liquid waste transportation requirements under the real-time solid-liquid ratio. If there is an abnormal value, the frequency adjustment coefficient or real-time solid-liquid ratio data is rechecked, corrected, and the synchronous operating frequency is confirmed again. Then, the frequency value is recorded in the pump parameter column of the final dispatch instruction generation table to ensure consistency with the time dimension of the motor's basic operating frequency.

[0143] Furthermore, the parameter filtering tool is activated to filter the structured table of optimized parameter adjustment values. The tool iterates through each parameter record in the table, comparing the parameter name with the corresponding equipment component information and preset filtering rules. If a record's parameter name is motor speed and the equipment component is solid waste conveyor belt, the tool automatically marks the record as a solid waste conveyor belt motor speed control quantity and extracts its corresponding smoothed filtered value sequence. Similarly, if a record's parameter name is pump power and the equipment component is liquid waste pumping station, the tool automatically marks the record as a liquid waste pumping station pump power control quantity and extracts its smoothed filtered value sequence. During the filtering process, the tool generates a filtering log in real time, recording the extraction time, parameter location, and value sequence length of each target parameter, facilitating subsequent verification of the completeness of the filtering process.

[0144] Furthermore, the basic operating frequency of the solid waste conveyor belt motor is calculated. First, all motor speed control values ​​within the same time period are selected from the motor speed control value sequence table. These values ​​are then summed to obtain the total motor speed control value. Next, the sum is divided by the number of values ​​to obtain the average value of the motor speed control value within that time period. Then, the average value of the motor speed control value is divided by the rated speed of the motor to obtain the ratio of the average value to the rated speed. Finally, this ratio is multiplied by the rated frequency of the motor, and the result is the basic operating frequency of the motor.

[0145] Furthermore, the previously calculated basic operating frequency of the pumps is retrieved. This basic operating frequency is calculated based on the pump power control of the liquid waste pumping station and is stored in the equipment basic operating frequency summary table. The table clearly records the value of the pump basic operating frequency, the calculation time period, and the corresponding calculation basis. When retrieving, the summary table is opened, and the pump basic operating frequency values ​​within the same time period are filtered according to the real-time solid-liquid ratio time range. If the pump basic operating frequency within the time period is a fixed value, then the fixed value is directly extracted; if it is a series of changing values, then the average value of the series is calculated as the pump basic operating frequency used for this adjustment. A correspondence is established between the real-time solid-liquid ratio and the pump frequency adjustment coefficient. This correspondence is set based on the process requirements of the co-transportation of solid and liquid waste in coal mines. The specific rules are as follows: when the liquid waste ratio in the real-time solid-liquid ratio is higher than the preset standard ratio, the pump operating frequency needs to be increased to increase the liquid waste transportation volume. At this time, the frequency adjustment coefficient is set to be greater than 1. When the liquid waste ratio is lower than the preset standard ratio, the pump operating frequency needs to be decreased to reduce the liquid waste transportation volume. At this time, the frequency adjustment coefficient is set to be less than 1. When the liquid waste ratio is equal to the preset standard ratio, there is no need to adjust the pump operating frequency. At this time, the frequency adjustment coefficient is set to be equal to 1.

[0146] Furthermore, the basic format of the final dispatch instruction is determined. This format must include the instruction identifier, equipment type, control parameter name, control parameter value, execution time, safety requirements, and instruction validity period. The instruction identifier is a unique code. The equipment type is labeled as solid waste conveyor belt motor and liquid waste pump station pump, respectively. The control parameter name corresponds to the basic operating frequency and synchronous operating frequency. The safety requirements specify the safety thresholds such as temperature and vibration that the equipment must meet during operation.

[0147] In summary, the validity of the extracted numerical sequences of solid waste conveyor belt motor speed control and liquid waste pump station pump power control was verified. Verification included checking if the sequence length met the preset acquisition period, if the values ​​were within the safe operating range of the equipment, and if the trend of the values ​​was consistent with the characteristics after smoothing and filtering. If the verification revealed that a numerical sequence was too short, the process was immediately traced back to the parameter adjustment value generation stage to check for any interruptions in data acquisition. If an interruption was found, backup data for the missing time period was supplemented. If the values ​​were found to exceed the safe range, the smoothing and filtering process was re-examined to confirm whether it was due to abnormal original data. After correction, the numerical sequence was regenerated. If abnormal trends in the values ​​were found, the filter window size was adjusted, and smoothing and filtering was performed again until both numerical sequences passed the validity verification.

[0148] In general, to calculate the basic operating frequency of a liquid waste pumping station, the calculation first involves selecting all pump power control values ​​from the pump power control value sequence table for the same time period as the motor's basic operating frequency calculation. These values ​​are then summed to obtain the total pump power control value. The sum is then divided by the number of values ​​to obtain the average value of the pump power control value for that time period. Next, the average value of the pump power control value is divided by the pump's rated power value to obtain the ratio of the average value to the rated power. This ratio is then multiplied by the pump's rated frequency to obtain the pump's basic operating frequency.

[0149] In summary, the frequency adjustment coefficient is determined based on the real-time solid-liquid ratio. The average real-time solid-liquid ratio is calculated from the real-time solid-liquid ratio sequence table. This average is compared with the solid-liquid ratio intervals in the ratio-coefficient correspondence table. Once a matching interval is found, the corresponding frequency adjustment coefficient is extracted. After extraction, the description of the solid-liquid ratio interval corresponding to the adjustment coefficient needs to be checked again to confirm consistency with the actual real-time solid-liquid ratio average, avoiding deviations in adjustment coefficient selection due to interval matching errors. The determined adjustment coefficient is then recorded in the pump frequency adjustment record table and associated with the pump's basic operating frequency. The synchronous operating frequency of the liquid waste pumping station is calculated by multiplying the pump's basic operating frequency by the determined frequency adjustment coefficient. The result is the synchronous operating frequency. Two decimal places must be retained during the calculation to ensure the accuracy of the frequency value meets the pump's operation control requirements. Simultaneously, it is necessary to check whether the calculated result is within the pump's maximum allowable operating frequency range. If the calculated synchronous operating frequency exceeds the maximum allowable value, the maximum allowable operating frequency is used as the final synchronous operating frequency to avoid damage to the pump due to over-frequency operation. If it is within the range, the calculated result is directly used as the synchronous operating frequency.

[0150] In summary, the final scheduling instructions are filled in according to the basic format. For solid waste conveyor belt motors, the instruction identifier is filled in the instruction template, the equipment type is labeled "solid waste conveyor belt motor," the control parameter name is filled in as the basic operating frequency, and the control parameter value is filled in as the previously extracted basic operating frequency of the motor. For liquid waste pump station pumps, the same method is used, the equipment type is labeled "liquid waste pump station pump," the control parameter name is filled in as the synchronous operating frequency, and the control parameter value is filled in as the pump synchronous operating frequency. The execution time, safety requirements, and effective period are consistent with the motor instructions to ensure that the scheduling instructions for the two devices are coordinated and matched. The completed final scheduling instructions are then checked for completeness and logic. The completeness check checks for any missing fields in the instructions; if any are missing, they are immediately added. The logic check confirms that the ratio of the conveying volume corresponding to the basic operating frequency of the motor and the synchronous operating frequency of the pump is consistent with the real-time solid-liquid ratio, and also checks whether the effective period of the instruction covers the current work cycle and whether the execution time is reasonable.

[0151] In this embodiment of the invention, when the final scheduling instruction is sent to the execution unit of the device, it is specifically used for:

[0152] Specifically, the final scheduling instruction for the equipment is retrieved from the final scheduling instruction generation record. This instruction was previously generated based on the basic operating frequency of the motor and the synchronous operating frequency of the pump. It contains complete information such as instruction identifier, equipment type, control parameters, execution time, safety requirements, and effective period. The corresponding final scheduling instruction text is accurately located in the scheduling instruction database and extracted. It is confirmed that there are no missing parameters or format errors in the text. If any abnormalities are found, the process is traced back to the final scheduling instruction generation stage for correction. Subsequently, the extracted final scheduling instruction text is imported into the encryption queue of the encryption processing system to ensure that the instruction text is complete and can be recognized by the encryption system.

[0153] Further, the encryption processing system is activated, and a preset symmetric encryption algorithm is selected to encrypt the final dispatch instruction. This encryption algorithm is a dedicated algorithm based on the data security standards of the coal mining industry. The encryption process requires inputting an encryption key into the system first. After key input, the system automatically verifies the key's integrity. Once it confirms that the key has no missing or incorrect characters, it begins encrypting the final dispatch instruction text. During encryption, the system divides the instruction text into multiple data blocks of fixed length. For each data block, the system sequentially applies the encryption algorithm's processing steps, including data obfuscation, character substitution, and bitwise operations, ensuring that each data block is converted into ciphertext that cannot be directly read. After all data blocks are processed, the system concatenates the ciphertext blocks in their original order to form a complete encryption result. Simultaneously, an encryption checksum is generated. The encryption result and the checksum are associated and stored to obtain the encrypted final dispatch instruction, i.e., the encrypted result.

[0154] In summary, the integrity of the encrypted result is verified. During verification, the verification function is invoked in the encryption processing system. The system inputs the verification code corresponding to the encrypted result, automatically recalculates the verification code, and compares it with the original code. If they match perfectly, the encrypted result is considered intact and unaltered. If they do not match, an anomaly is detected in the encryption process, requiring the original final scheduling instruction text to be retrieved and the encryption operation to be performed again until the verification code of the encrypted result passes the comparison. Simultaneously, the format of the encrypted result must be checked to ensure it meets the receiving requirements of the device execution unit. If the format does not match, the encryption system's format conversion function is used to adjust it to a format supported by the execution unit, ensuring the encrypted result can be correctly parsed by the execution unit. After successful verification, the encrypted result is saved to the encryption instruction storage module, awaiting distribution. The encrypted result is sent to the device's execution unit via a dedicated communication link. During transmission, the corresponding device execution unit communication address is selected in the encryption processing system. After clicking the transmission command, the system splits the encrypted result into fixed data packets of varying sizes and transmits them to the execution unit one packet at a time. After each packet is transmitted, the execution unit sends a reception confirmation signal. The system only transmits the next packet after receiving the confirmation signal. If no confirmation signal is received within a preset time, the system automatically retransmits the data packet until the execution unit successfully receives it. After all data packets have been transmitted, the execution unit performs an integrity check on the received encrypted result and sends a successful reception signal to the encryption processing system. Upon receiving this signal, the system records the transmission time, execution unit address, encryption result identifier, and other information to create a transmission log, completing the operation of sending the encrypted result to the device execution unit.

[0155] like Figure 2 The diagram shown is a functional block diagram of an intelligent scheduling system for the collaborative treatment of solid and liquid waste in coal mines, provided by an embodiment of the present invention.

[0156] The intelligent scheduling system 100 for the co-processing of solid and liquid wastes in coal mines, as described in this invention, can be installed in an electronic device. Depending on the functions implemented, the intelligent scheduling system 100 may include a preliminary scheduling instruction module 101, a preliminary scheduling instruction module 102, a dynamic control module 103, an optimization parameter adjustment module 104, a final scheduling instruction module 105, and an execution module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0157] In this embodiment, the functions of each module / unit are as follows:

[0158] The preliminary scheduling instruction module is used to compare and match the mine's scheduling data with preset rules, coordinate conflicts in the comparison and matching results, and obtain preliminary scheduling instructions for the equipment.

[0159] The preliminary scheduling instruction module is used to extract the multi-source dynamic operating parameters of the equipment based on the preliminary scheduling instruction, and obtain the flow tolerance range of the mine by coordinating and limiting the multi-source dynamic operating parameters.

[0160] The dynamic control module is used to align the power deviation of the device with the preset device priority coefficient within the flow tolerance range to obtain the dynamic control input vector of the device.

[0161] The optimization parameter adjustment module is used to map the dynamic control input vector to the standard adjustment range of the device to obtain the optimization parameter adjustment value of the device;

[0162] The final scheduling instruction module is used to balance the transport flow of the solid and liquid waste according to the optimization parameter adjustment value, and obtain the final scheduling instruction of the equipment.

[0163] The execution module is used to send the final scheduling instruction to the execution unit of the device.

[0164] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0165] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0167] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0168] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent scheduling method for the synergistic treatment of solid and liquid wastes in coal mines, characterized in that, The method includes: The scheduling data of the mine is compared and matched with preset rules, and the results of the comparison and matching are reconciled to obtain the preliminary scheduling instructions for the equipment. Based on the preliminary scheduling instructions, the multi-source dynamic operating parameters of the equipment are extracted, and the flow tolerance range of the mine is obtained by coordinating and limiting the multi-source dynamic operating parameters. Within the flow tolerance range, the power deviation of the device is aligned with the preset device priority coefficient to obtain the dynamic control input vector of the device; The dynamic control input vector is mapped to the standard adjustment range of the device to obtain the optimized parameter adjustment value of the device; The final scheduling instruction for the equipment is obtained by balancing the transport flow of the solid and liquid wastes according to the optimized parameter adjustment values. The final scheduling instruction is sent to the execution unit of the device.

2. The intelligent scheduling method for the synergistic treatment of solid and liquid wastes in coal mines as described in claim 1, characterized in that, The process of comparing and matching mine scheduling data with preset rules, and resolving conflicts in the comparison and matching results to obtain preliminary equipment scheduling instructions includes: By integrating the multi-source data and equipment status data of the mine, a scheduling data sequence for the equipment is obtained; By comparing the scheduling data sequence with the running constraints in the preset rule base one by one, a conflict identifier set is obtained; The conflict identifier set is converted into preliminary scheduling instructions for the device.

3. The intelligent scheduling method for the synergistic treatment of solid and liquid wastes in coal mines as described in claim 2, characterized in that, The process of extracting multi-source dynamic operating parameters of the equipment based on the preliminary scheduling instructions, and obtaining the flow tolerance range of the mine by coordinating and limiting the multi-source dynamic operating parameters, includes: The data format of the multi-source dynamic operating parameters is unified to obtain the standard parameters of the device; Based on the initial scheduling instructions, and in conjunction with the rated capacity of the equipment and the historical extreme values ​​of the equipment, dynamic limiting thresholds for various operating parameters of the equipment are set. The standard parameters that exceed the dynamic limiting threshold are adjusted to obtain the compliant parameter set of the device; The flow tolerance range of the mine is determined based on the set of compliance parameters.

4. The intelligent scheduling method for the synergistic treatment of solid and liquid wastes in coal mines as described in claim 3, characterized in that, The formula for calculating the flow tolerance range is: ； in, For traffic tolerance range, For the types of parameters currently being calculated, The total number of parameters. For the first The weighting coefficients of class parameters, For the first Normalized values ​​of class parameters, This is the maximum allowable flow rate for the device.

5. The intelligent scheduling method for the synergistic treatment of solid and liquid wastes in coal mines as described in claim 1, characterized in that, The step of aligning the power deviation of the device with the preset device priority coefficient within the flow tolerance range to obtain the dynamic control input vector of the device includes: Obtain the deviation rate between the real-time power and the rated power of the device; A mapping relationship is established between the deviation rate and the preset equipment priority coefficient. Based on the mapping relationship, the deviation rate is weighted and corrected to obtain the dynamic control input vector of the equipment.

6. The intelligent scheduling method for the synergistic treatment of solid and liquid wastes in coal mines as described in claim 5, characterized in that, The step of obtaining the deviation rate between the real-time power and the rated power of the device includes: Based on the power sensing data of the device, the real-time active power is obtained by integrating the active power. The real-time active power of the device is obtained, and the difference between the real-time active power and the rated power is taken as the absolute power deviation. The ratio of the absolute power deviation to the rated power is taken as the deviation rate of the equipment.

7. The intelligent scheduling method for the synergistic treatment of solid and liquid wastes in coal mines as described in claim 1, characterized in that, The step of mapping the dynamic control input vector to the standard adjustment range of the device to obtain the optimized parameter adjustment value of the device includes: The range corresponding to the independent control parameters in the dynamic control input vector is taken as the original span of the device. The width of the standard adjustment range is taken as the target span of the device; Based on the proportional relationship between the original span and the target span, adjust the maximum and minimum values ​​of the independent control parameters; The adjusted independent control parameters are smoothed and filtered to obtain the optimized parameter adjustment values ​​for the device.

8. The intelligent scheduling method for the synergistic treatment of solid and liquid wastes in coal mines as described in claim 1, characterized in that, The step of balancing the transport flow rate of the solid-liquid dual waste according to the optimized parameter adjustment value to obtain the final scheduling instruction of the equipment includes: Extract the motor speed control quantity of the solid waste conveyor belt and the pump power control quantity of the liquid waste pumping station from the optimized parameter adjustment values; The basic operating frequencies of the motor and the pump are calculated based on the motor speed control quantity and the pump power control quantity; Based on the real-time solid-liquid ratio of the mine, the basic operating frequency of the pump is adjusted to obtain the synchronous operating frequency of the liquid waste pumping station; The final scheduling command for the equipment is generated based on the basic operating frequency of the motor and the synchronous operating frequency of the pump.

9. The intelligent scheduling method for the synergistic treatment of solid and liquid wastes in coal mines as described in claim 1, characterized in that, The step of issuing the final scheduling instruction to the execution unit of the device includes: The final scheduling instruction is encrypted, and the encryption result is sent to the execution unit of the device.

10. An intelligent scheduling system for the synergistic treatment of solid and liquid waste in coal mines, characterized in that, The system includes: The preliminary scheduling instruction module is used to compare and match the mine's scheduling data with preset rules, coordinate conflicts in the comparison and matching results, and obtain preliminary scheduling instructions for the equipment. The flow tolerance interval module is used to extract the multi-source dynamic operating parameters of the equipment based on the preliminary scheduling instruction, and obtain the flow tolerance interval of the mine by coordinating and limiting the multi-source dynamic operating parameters. The dynamic control module is used to align the power deviation of the device with the preset device priority coefficient within the flow tolerance range to obtain the dynamic control input vector of the device. The optimization parameter adjustment module is used to map the dynamic control input vector to the standard adjustment range of the device to obtain the optimized parameter adjustment value of the device. The final scheduling instruction module is used to balance the transport flow of the solid and liquid waste according to the optimization parameter adjustment value, and obtain the final scheduling instruction of the equipment; The execution module is used to send the final scheduling instruction to the execution unit of the device.

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