Methods, devices, and computer equipment for determining the operating status of kilns

Abstract

This application relates to a method, apparatus, and computer device for determining the operating status of a kiln. The method includes: acquiring operating parameters of at least two components of the kiln equipment within a preset time period; processing the operating parameters of each component using a preset algorithm including the 3σ principle and / or correlation coefficient method to obtain the quantitative results of the operation of each component; and determining whether there are any abnormalities in the operating status of the kiln equipment based on the quantitative results of the operation of each component. This method enables a comprehensive and accurate assessment of the operating status of the kiln equipment based on the operating parameters of different components, thus determining the health status of the kiln equipment. It improves the reliability of existing technologies for assessing a single component.

Description

Technical Field

[0001] This application relates to the field of kiln detection technology, and in particular to a method, apparatus and computer equipment for determining the operating status of a kiln. Background Technology

[0002] With the development of lithium battery technology, furnaces, as key production equipment for lithium battery cathode materials, are also constantly evolving. The health status of the furnace directly affects the heating and chemical reaction effects of the materials, thus impacting the production quality of lithium battery cathode materials. Therefore, monitoring and evaluating the health status of the furnace is crucial.

[0003] In traditional techniques, the health status of kiln equipment is typically monitored and evaluated by collecting operating parameters from a specific component of the kiln. For example, the health status is assessed based on the temperature of the kiln lining.

[0004] However, traditional methods for assessing the health status of kiln equipment suffer from inaccurate results. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, and computer equipment for accurately assessing the kiln operating status of kiln equipment to address the aforementioned technical problems.

[0006] Firstly, this application provides a method for understanding the operating status of a kiln. The method includes:

[0007] Obtain the operating parameters of at least two components of the kiln equipment within a preset time period;

[0008] The operating parameters of each component are processed using a preset algorithm to obtain the quantitative results of each component's operation; the preset algorithm includes the 3σ principle method and / or the correlation coefficient method.

[0009] Based on the quantitative results of the operation of each component, determine whether there are any abnormalities in the operating status of the kiln equipment.

[0010] In one embodiment, the preset algorithm includes the 3σ principle method and the correlation coefficient method. The preset algorithm is used to obtain the execution quantization results corresponding to each execution parameter, including:

[0011] The operating parameters of each component are processed using the 3σ principle method to obtain the first quantization value of each component;

[0012] The operating parameters of each component are processed using the correlation coefficient method to obtain the second quantization value of each component;

[0013] The operational quantization result of each component is determined based on the first quantization value and the second quantization value of each component.

[0014] In one embodiment, the operational quantization result of each component is determined based on a first quantization value and a second quantization value of each component, including:

[0015] The first quantization value of each component, the weight corresponding to the first quantization value, the second quantization value of each component, and the weight corresponding to the second quantization value are weighted and summed to obtain the quantization result of each component.

[0016] In one embodiment, determining whether there are any abnormalities in the operating status of the kiln equipment based on the quantitative results of the operation of each component includes:

[0017] The quantitative results of the operation of each component and the weight of each component are weighted and summed to obtain the target quantitative results of the kiln equipment;

[0018] Based on the target quantification results and the preset evaluation threshold range, determine whether there are any abnormalities in the operating status of the kiln equipment.

[0019] In one embodiment, the quantitative results of the operation of each component and the weight of each component are weighted and summed to obtain the target quantitative result of the kiln equipment, including:

[0020] The operational quantification results of each component are normalized to obtain the standardized quantification results of each component;

[0021] The standardized quantitative results of each component and the weight of each component are weighted and summed to obtain the target quantitative result of the kiln equipment.

[0022] In one embodiment, determining whether there is an abnormality in the operating status of the kiln equipment based on the target quantification results and a preset evaluation threshold range includes:

[0023] If the target quantification result is greater than the maximum value of the preset evaluation threshold range, the kiln equipment is in normal operating status.

[0024] If the target quantification result is less than the minimum value of the preset evaluation threshold range, the kiln equipment is in an abnormal operating state.

[0025] In one embodiment, the method for determining the kiln operating status further includes:

[0026] If the target quantification result is within the preset evaluation threshold range, an alarm message will be output, which will be used to indicate that the kiln equipment needs to be monitored.

[0027] In one embodiment, the operating parameters of at least two components include at least two of the following: temperature data of each fan, current data of each motor, operating status data of each roller section, current data of each heating rod, air flow rate in the pipeline, total oxygen flow rate in the pipeline, and pressure data inside the furnace.

[0028] Secondly, this application also provides a device for determining the operating status of a kiln. The device includes:

[0029] The acquisition module is used to acquire the operating parameters of at least two components of the kiln equipment within a preset time period;

[0030] The quantization result determination module is used to process the operating parameters of each component using a preset algorithm to obtain the quantization results of each component's operation; the preset algorithm includes the 3σ principle method and / or the correlation coefficient method;

[0031] The operation status determination module is used to determine whether there are any abnormalities in the operation status of the kiln equipment based on the quantitative results of the operation of each component.

[0032] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method in any of the embodiments of the first aspect described above.

[0033] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the method in any of the embodiments of the first aspect described above.

[0034] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method in any of the embodiments of the first aspect described above.

[0035] The aforementioned method, apparatus, and computer equipment for determining the operating status of a kiln acquire operating parameters of at least two components of the kiln equipment within a preset time period. They then process these parameters using preset algorithms, including the 3σ principle and / or correlation coefficient method, to obtain quantitative results for each component's operation. Based on these quantitative results, the system determines whether any abnormalities exist in the kiln equipment's operating status. This approach enables a comprehensive and accurate assessment of the kiln equipment's operating status based on the operating parameters of different components, thus determining the kiln equipment's health condition. It improves the reliability of existing technologies that assess a single component. Attached Figure Description

[0036] Figure 1 This is an application environment diagram of the kiln operation status determination method in one embodiment;

[0037] Figure 2 This is a flowchart illustrating a method for determining the operating status of a kiln in one embodiment;

[0038] Figure 3This is a flowchart illustrating the method for determining the kiln operating status in another embodiment;

[0039] Figure 4 This is a flowchart illustrating the method for determining the kiln operating status in another embodiment;

[0040] Figure 5 This is a flowchart illustrating the method for determining the kiln operating status in another embodiment;

[0041] Figure 6 This is a structural block diagram of a kiln operating status determination device in one embodiment;

[0042] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] The kiln operating status determination method provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown includes a kiln device 104 and a terminal 102. The kiln device 104 includes multiple data acquisition devices: a temperature sensor, a current sensor, a broken rod detection device, a flow meter, a pressure sensor, etc. The terminal 102 communicates with the kiln device 104 via a network. After acquiring the operating parameters of at least two components collected by the data acquisition devices in the kiln device, the operating status of the kiln device can be determined based on the operating parameters of each component. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc.

[0045] In one embodiment, such as Figure 2 As shown, a method for determining the operating status of a kiln is provided, which can be applied to... Figure 1 Taking the terminal in the example, the explanation includes the following steps:

[0046] S202, Obtain the operating parameters of at least two components of the kiln equipment within a preset time period.

[0047] The operating parameters of at least two components of the kiln equipment include at least two of the following: temperature data of each fan, current data of each motor, operating status data of each roller section, current data of each heating rod, air flow rate in the pipeline, total oxygen flow rate in the pipeline, and pressure data inside the kiln. The preset time period can be a pre-set time period after the kiln equipment starts operating.

[0048] It should be noted that the kiln equipment is equipped with devices for acquiring the operating parameters of various components. These may include temperature sensors, current sensors, broken rod detection devices, flow meters, pressure sensors, etc.

[0049] Specifically, temperature data from each fan can be collected using temperature sensors; current data from each motor and heating rod can be collected using current sensors; operating status data from each section of rollers can be collected using a broken rod detection device; air flow and total oxygen flow in the pipeline can be collected using flow meters; and pressure data from different areas within the kiln can be collected using pressure sensors. The pressure data from different areas within the kiln can include pressure data from the top, bottom, and sides of the kiln. Once the data acquisition device obtains the operating parameters of at least two components, it sends these parameters to the terminal, thus acquiring the operating parameters of at least two components of the kiln within a preset time period. Optionally, a sliding window method can be used to sequentially extract data from a second preset time period from the operating parameter data of different components within the preset time period as standard operating parameter data. For example, this could be the operating parameters from the first hour before data acquisition begins. The operating parameters from the same time period following the second preset time period are then used as test parameter data.

[0050] S204, using a preset algorithm to process the operating parameters of each component to obtain the quantitative results of the operation of each component; wherein the preset algorithm includes the 3σ principle method and / or the correlation coefficient method.

[0051] Specifically, after obtaining the operating parameters of each component, the 3σ principle method can be used to calculate the operating parameters of each component to obtain the quantitative results of the operation of different components. For example, after obtaining the temperature data of each fan, the current data of each motor, and the total air flow in the pipeline within a preset time period, the 3σ principle method is used to calculate and process the temperature data of each fan to obtain the quantitative results of the operation of each fan. Simultaneously, the 3σ principle method is used to calculate and process the current data of each motor to obtain the quantitative results of the operation of each motor. The 3σ principle method is also used to calculate and process the total air flow in the pipeline to obtain the quantitative results of the pipeline operation. Optionally, after obtaining the quantitative results of the operation of each fan, the quantitative results of the operation of each fan can be further summed or weighted to obtain the quantitative results of the operation of the fan. Similarly, after obtaining the quantitative results of the operation of each motor, the quantitative results of the operation of each motor can be further summed or weighted to obtain the quantitative results of the operation of the motor.

[0052] Alternatively, the correlation coefficient method can be used to calculate the operating parameters of each component, obtaining the quantitative results of different component operations. For example, after obtaining the temperature data of each fan, the current data of each motor, and the total airflow in the pipeline within a preset time period, the correlation coefficient method is used to calculate the temperature data of each fan to obtain the quantitative results of each fan's operation. Simultaneously, the correlation coefficient method is used to calculate the current data of each motor to obtain the quantitative results of each motor's operation. The correlation coefficient method is also used to calculate the total airflow in the pipeline to obtain the quantitative results of the pipeline's operation. Optionally, after obtaining the quantitative results of each fan's operation, the results can be further summed or weighted to obtain the quantitative results of the fan's operation. Similarly, after obtaining the quantitative results of each motor's operation, the results can be further summed or weighted to obtain the quantitative results of the motor's operation.

[0053] Alternatively, the 3σ principle method can be used to calculate the operating parameters of each component, yielding the first operational quantification result for each component. Then, the correlation coefficient method can be used to calculate the operating parameters of each component, yielding the second operational quantification result for each component. The first and second operational quantification results for each component are then weighted and summed to obtain the operational quantification result for each component.

[0054] S206. Based on the quantitative results of the operation of each component, determine whether there are any abnormalities in the operating status of the kiln equipment.

[0055] Specifically, after obtaining the operational quantification results of each component, the operational quantification results of each component can be weighted according to a preset weighting rule, and then summed to obtain the comprehensive quantification result of the kiln equipment. Alternatively, the operational quantification results of each component can be directly summed to obtain the comprehensive quantification result of the kiln equipment. The comprehensive quantification result of the kiln equipment is compared with a preset quantification result threshold to determine whether there is any abnormality in the operating status of the kiln equipment. This can be done by comparing the comprehensive quantification result of the kiln equipment with the preset quantification result threshold; if the comprehensive quantification result of the kiln equipment is greater than or equal to the preset quantification result threshold, the operating status of the kiln equipment is normal; if the comprehensive quantification result of the kiln equipment is greater than the preset quantification result threshold, the operating status of the kiln equipment is abnormal. Alternatively, the comprehensive quantification result of the kiln equipment can be subtracted from the preset quantification result threshold; if the difference is greater than or equal to 1, the operating status of the kiln equipment is normal; if the difference is less than 1, the operating status of the kiln equipment is abnormal.

[0056] As another possible implementation, the comprehensive quantitative result of the kiln equipment can be compared with a preset quantitative result threshold range to determine whether there is any abnormality in the operating status of the kiln equipment. If the comprehensive quantitative result of the kiln equipment is greater than the maximum value of the preset quantitative result threshold range, the operating status of the kiln equipment is normal; if the comprehensive quantitative result of the kiln equipment is less than the minimum value of the preset quantitative result threshold range, the operating status of the kiln equipment is abnormal. Optionally, if the comprehensive quantitative result of the kiln equipment is within the preset quantitative result threshold range, an alarm message can be output to instruct personnel to monitor the kiln equipment.

[0057] The aforementioned method for determining the operating status of a kiln involves acquiring the operating parameters of at least two components of the kiln equipment within a preset time period. A preset algorithm, including the 3σ principle and / or correlation coefficient method, is used to process these parameters, resulting in a quantitative assessment of the operating status of each component. Based on this quantitative assessment, the method determines whether any abnormalities exist in the kiln equipment's operating status. This approach enables a comprehensive and accurate evaluation of the kiln equipment's operating status based on the operating parameters of different components, thus determining the kiln's health condition. It improves the reliability of existing technologies that assess a single component.

[0058] The above embodiments illustrate a method for determining the operating status of a kiln. The most crucial aspect of this method is the quantitative evaluation of the operating parameters of each component. An embodiment will now be used to illustrate how to use the 3σ principle and correlation coefficient method to quantitatively evaluate the operating parameters of each component. In one embodiment, such as... Figure 3 As shown, the preset algorithm includes the 3σ principle method and the correlation coefficient method. The preset algorithm is used to obtain the execution quantization results corresponding to each execution parameter, including:

[0059] S302 uses the 3σ principle to process the operating parameters of each component to obtain the first quantization value of each component.

[0060] Specifically, after obtaining the operating parameters of each component, the 3σ principle is used to process these parameters to obtain the first quantified value for each component. For example, the operating parameters of each component may include: the temperature data T of each fan. i (i = 1, 2, 3...n), where n is the number of fans; current data I1 for each motor i (i = 1, 2, 3...n), where n is the number of motors; current data I2 for each heating rod. i (i = 1, 2, 3...n), where n is the number of heating rods; state data G for each section of rollers. i (i = 1, 2, 3...n), where n is the number of rollers; total air flow rate F in the pipeline. A And total oxygen flow rate data in the pipeline F O Pressure data P in different furnace zones i (i = 1, 2, 3...n), where n is the number of regions.

[0061] For example, processing the operating parameters of different components may include: measuring the temperature data T of three fans. i Taking (i = 1, 2, 3) as an example, the operating parameters of the first hour of operation within the preset time period are used as the temperature standard state data ST. i (i = 1, 2, 3), the data from the first hour after the initial run is used as the temperature measurement data. The formula for calculating the first quantization value based on the 3σ principle is:

[0062] In formula (1), μ[ST i [ST represents standard temperature data] i The mean of (i = 1, 2, 3), σ[ST i [ST represents standard temperature data] i The standard deviation of (i = 1, 2, 3), μ[T] i [T represents temperature measurement data] i The mean of (i = 1, 2, 3).

[0063] To measure the current data I1 of 8 motors i Taking (i = 1, 2, ..., 8) as an example, the standard state data of the motor current for the first hour of operation is SI1. i (i = 1, 2, ..., 8), the data from the first hour of operation is used as the motor current measurement data. The formula for calculating the first quantization value based on the 3σ principle is:

[0064]

[0065] In formula (4), μ[SI1] i SI1 is the standard state data for motor current. i The mean of (i = 1, 2, ..., 8), σ[SI1 i SI1 is the standard state data for motor current. i The standard deviation of (i = 1, 2, ..., 8), μ[I1] i [I1 is the motor current measurement data] i The mean of (i = 1, 2, ..., 8).

[0066] To measure the current data I2 of 44 heating rods i Taking (i = 1, 2, ..., 44) as an example, the standard state data of the heating rod current for the first hour of operation is SI2. i (i = 1, 2, ..., 44), the data from the first hour of operation is used as the heating rod current measurement data. The formula for calculating the first quantization value based on the 3σ principle is:

[0067]

[0068] In formula (7), μ[SI2] i [SI2 is the standard state data for heating rod current] i The mean of (i = 1, 2, ..., 44), σ[SI2 i [SI2 is the standard state data for heating rod current] i The standard deviation of (i = 1, 2, ..., 44), μ[I2] i [I2 is the current measurement data for the heating rod] i The mean of (i = 1, 2, ..., 44).

[0069] Based on the measured condition data G of the 12 roller sections i Taking (i = 1, 2, ..., 12) as an example, the standard state data of the roller bar during its first hour of operation is SG. i (i = 1, 2, ..., 12), the data from the first hour of operation are used as the roller measurement data. The formula for calculating the first quantization value based on the 3σ principle is:

[0070]

[0071] In formula (10-a), μ[SG] i ] represents the standard state data for the roller bar (SG). i The mean of (i = 1, 2, ..., 12), σ[SG] i ] represents the standard state data for the roller bar (SG). iThe standard deviation of (i = 1, 2, ..., 12), μ[G] i [G] is the measurement data for the roller. i The mean of (i = 1, 2, ..., 12).

[0072] The measured total air flow rate F in the pipeline A For example, the standard state data for the total airflow in the first hour of operation is SF. A The data from the first hour of operation is used as the total airflow measurement data. The formula for calculating the first quantization value based on the 3σ principle is as follows:

[0073]

[0074] In formula (11), μ[SF A [SF] represents the standard state data for total airflow. A The mean, σ[SF A [SF] represents the standard state data for total airflow. A Standard deviation, μ[F A [F represents the total airflow measurement data] A The mean.

[0075] The measured total oxygen flow rate F in the pipeline O For example, the standard state data for the total oxygen flow rate in the first hour of operation is SF6. O The data from the first hour of operation is used as the total oxygen flow measurement data. The formula for calculating the first quantification value based on the 3σ principle is as follows:

[0076]

[0077] In formula (14), μ[SF O [This refers to the standard state data for total oxygen flow rate SF] O The mean, σ[SF O [This refers to the standard state data for total oxygen flow rate SF] O Standard deviation, μ[F O [F represents the total oxygen flow rate measurement data] O The mean.

[0078] To measure the pressure P in 5 different furnace zones i Taking (i = 1, 2, ..., 5) as an example, its initial 1-hour pressure standard state data is SP. i (i = 1, 2, ..., 5), the data from the first hour of operation is used as the pressure measurement data. The formula for calculating the first quantization value based on the 3σ principle is:

[0079]

[0080] In formula (17), μ[SP i [SP is the standard pressure condition data] i The mean of (i = 1, 2, ..., 5), σ[SP i [SP is the standard pressure condition data] i The standard deviation of (i = 1, 2, ..., 5), μ[P] i [P is the pressure measurement data] i The mean of (i = 1, 2, ..., 5).

[0081] S304 uses the correlation coefficient method to process the operating parameters of each component to obtain the second quantization value of each component.

[0082] Specifically, after obtaining the operating parameters of each component, the correlation coefficient method is used to process the operating parameters of each component to obtain the first quantified value of each component. For example, the operating parameters of each component may include: the temperature data T of each fan. i (i = 1, 2, 3...n), where n is the number of fans; current data I1 for each motor i (i = 1, 2, 3...n), where n is the number of motors; current data I2 for each heating rod. i (i = 1, 2, 3...n), where n is the number of heating rods; state data G for each section of rollers. i (i = 1, 2, 3...n), where n is the number of rollers; total air flow rate F in the pipeline. A And total oxygen flow rate data in the pipeline F O Pressure data P in different furnace zones i (i = 1, 2, 3...n), where n is the number of regions.

[0083] For example, processing the operating parameters of different components may include: measuring the temperature data T of three fans. i Taking (i = 1, 2, 3) as an example, the operating parameters of the first hour of operation within the preset time period are used as the temperature standard state data ST. i (i = 1, 2, 3), the temperature measurement data is taken from the first hour after the initial operation. Formula (2) is used: The second quantization value of the wind turbine is calculated; where Cov(T) i ST i ) for T i With ST i The covariance, Var[T i ] is T i The variance, Var[ST i ST i The variance.

[0084] To measure the current data I1 of 8 motors i Taking (i = 1, 2, ..., 8) as an example, the standard state data of the motor current for the first hour of operation is SI1. i (i = 1, 2, ..., 8), the data from the first hour of operation is used as the motor current measurement data. The formula for calculating the second quantization value based on the correlation coefficient method is:

[0085]

[0086] In formula (5), Cov(I1) i SI1 i ) is I1 i With SI1 i The covariance, Var[I1 i ] for I1 i The variance, Var[SI1 i SI1 i The variance.

[0087] To measure the current data I2 of 44 heating rods i (i = 1, 2, ..., 44), the standard state data of the heating rod current for the first hour of operation is SI2. i (i = 1, 2, ..., 44), the data from the first hour of operation is used as the heating rod current measurement data. The formula for calculating the second quantization value based on the correlation coefficient method is as follows:

[0088]

[0089] In formula (8), Cov(I2) i SI2 i ) is I2 i With SI2 i The covariance, Var[I2 i ] is I2 i The variance, Var[SI2] i SI2 i The variance.

[0090] Based on the measured condition data G of the 12 roller sections i (i = 1, 2, ..., 12), the standard state data of the roller bar during its first hour of operation is SG. i (i = 1, 2, ..., 12), the data from the first hour of operation are used as the roller measurement data. The formula for calculating the second quantization value based on the correlation coefficient method is as follows:

[0091]

[0092] In formula (8), Cov(G)i ,SG i ) is G i With SG i The covariance, Var[G i ] for G i The variance, Var[SG i ] is SG i The variance.

[0093] The measured total air flow rate F in the pipeline A For example, the standard state data for the total airflow in the first hour of operation is SF. A The data from the first hour of operation is used as the total airflow measurement data. The formula for calculating the second quantization value based on the correlation coefficient method is as follows:

[0094]

[0095] In formula (12), Cov(F) A ,SF A ) is F A With SF A The covariance, Var[F A ] is F A The variance, Var[SF A ] is SF A The variance.

[0096] The measured total oxygen flow rate F in the pipeline O For example, the standard state data for the total oxygen flow rate in the first hour of operation is SF6. O The data from the first hour of operation is used as the total oxygen flow measurement data. The formula for calculating the second quantification value based on the correlation coefficient method is as follows:

[0097]

[0098] In formula (15), Cov(F) O ,SF O ) is F O With SF O The covariance, Var[F O ] is F O The variance, Var[SF O ] is SF O The variance.

[0099] To measure the pressure P in 5 different furnace zones i Taking (i = 1, 2, ..., 5) as an example, its initial 1-hour pressure standard state data is SP. i(i = 1, 2, ..., 5), the data from the first hour of operation is used as the pressure measurement data. The formula for calculating the second quantization value based on the correlation coefficient method is as follows:

[0100]

[0101] In formula (18), Cov(P) i SP i ) is P i With SP i The covariance, Var[P i ] is P i The variance, Var[SP i ] is SP i The variance.

[0102] S306, determine the operational quantization result of each component based on the first quantization value and the second quantization value of each component.

[0103] Specifically, after obtaining the first quantization value and the second quantization value of each component, different weights can be assigned to the first quantization value and the second quantization value, and then they can be summed to obtain the operation quantization result of each component.

[0104] Optionally, for the measured condition data G of the 12 roller sections... i (i = 1, 2, ..., 12), where G is a Boolean value of 0 or 1. Therefore, the comprehensive evaluation index of the roller is the same as its state value, which can be expressed by the formula: Z4 = ∑G i ,i=1,2,…,12, to obtain the quantitative results of the roller operation.

[0105] Further, in one embodiment, determining the operational quantization result of each component based on the first quantization value and the second quantization value of each component includes:

[0106] The first quantization value of each component, the weight corresponding to the first quantization value, the second quantization value of each component, and the weight corresponding to the second quantization value are weighted and summed to obtain the quantization result of each component.

[0107] Specifically, the first result (multiplying the first quantized value of each component by its corresponding weight) can be added to the second result (multiplying the second quantized value of each component by its corresponding weight) to obtain the operational quantization result of each component. The weights corresponding to the first and second quantized values ​​are pre-defined weights.

[0108] For example, take the temperature data T of 3 fans as an example. i Taking (i = 1, 2, 3) as an example, we use formula (3):

[0109] Z1=∑(0.5×Z11 i +0.5×|Z12 i |), i=1,2,3, and the quantitative results of the wind turbine's operation can be calculated.

[0110] To measure the current data I1 of 8 motors i Taking (i = 1, 2, ..., 8) as an example, formula (6) is used:

[0111] Z2=∑(0.5×Z21 i +0.5×|Z22 i |), i=1,2,…,8, and the quantization results of the motor's operation can be calculated.

[0112] To measure the current data I2 of 44 heating rods i Taking (i = 1, 2, ..., 44) as an example, formula (9) is used:

[0113] Z3=∑(0.5×Z31 i +0.5×|Z32 i |), i=1,2,…,44, and the quantitative results of the heating rod's operation can be calculated.

[0114] Based on the measured condition data G of the 12 roller sections i Taking (i = 1, 2, ..., 12) as an example, we use formula (10 - b):

[0115] Z4=∑(0.5×Z41 i +0.5×|Z42 i |), i=1,2,…,12, and the quantification results of the roller operation can be calculated.

[0116] The measured total air flow rate F in the pipeline A For example, using formula (13):

[0117] Z5 = 0.5 × Z51 + 0.5 × |Z52|, which can be used to calculate the quantitative result of pipeline air operation;

[0118] The measured total oxygen flow rate F in the pipeline O For example, using formula (16):

[0119] Z6 = 0.5 × Z61 + 0.5 × |Z62|, which can be used to calculate the quantitative result of pipeline oxygen operation;

[0120] To measure the pressure P in 5 different furnace zones i Taking (i = 1, 2, ..., 5) as an example, formula (19) is used:

[0121] Z7=∑(0.5×Z71 i +0.5×|Z72i |), i=1,2,…,5, can be used to calculate the quantitative results of furnace pressure operation.

[0122] In this embodiment, the operating parameters of each component are processed using the 3σ principle to obtain a first quantized value for each component. Then, the operating parameters of each component are processed using the correlation coefficient method to obtain a second quantized value. Based on the first and second quantized values ​​of each component, the quantification result of each component's operation is determined. This allows for the use of two different algorithms to quantify the operating status of different components within the kiln equipment, obtaining quantified results for each component. This provides a basis for evaluating the overall operating status of the kiln equipment, resulting in a comprehensive and accurate evaluation of the kiln equipment's operating status, and ultimately determining whether any abnormalities exist in the kiln equipment's operating status.

[0123] The above embodiments illustrate how to determine the quantitative results of the operation of each component within the kiln equipment. Now, one embodiment will be used to illustrate how to determine whether there are any abnormalities in the operating status of the kiln equipment based on the quantitative results of the operation of each component. In one embodiment, such as... Figure 4 As shown, based on the quantitative results of the operation of each component, it is determined whether there are any abnormalities in the operating status of the kiln equipment, including:

[0124] S402, the quantitative results of the operation of each component and the weight of each component are weighted and summed to obtain the target quantitative result of the kiln equipment.

[0125] Specifically, after obtaining the quantitative results of each component's operation, each component's quantitative results are assigned a weight, and then summed to obtain the target quantitative result of the kiln equipment. The weights of each component can be set according to the importance of different components. For example, if the measured temperature data T of three fans is used... i (i = 1, 2, 3), 8 motor current data I1 i (i = 1, 2, ..., 8), Current data I2 of 44 heating rods i (i = 1, 2, ..., 44), 12 sections of roller state data G i (i = 1, 2, ..., 12), Total air flow rate F in the pipeline A Total oxygen flow rate F in the pipeline O 5 different furnace zone pressures P iTaking (i=1,2,…,5) as an example, the quantitative results of the operation of the fan Z1, the quantitative results of the operation of the motor Z2, the quantitative results of the operation of the heating rod Z3, the quantitative results of the operation of the roller Z4, the quantitative results of the operation of the air in the pipeline Z5, the quantitative results of the operation of the oxygen in the pipeline Z6, and the quantitative results of the operation of the pressure in the furnace Z7 are obtained. Different weights are assigned to the quantitative results of the operation of each component. For example, the quantitative result Z1 of the operation of the fan is assigned a weight of 0.2, the quantitative result Z2 of the operation of the motor is assigned a weight of 0.2, the quantitative result Z3 of the operation of the heating rod is assigned a weight of 0.3, the quantitative result Z4 of the operation of the roller is assigned a weight of 0.1, the quantitative result Z5 of the operation of the pipeline air is assigned a weight of 0.05, the quantitative result Z6 of the operation of the pipeline oxygen is assigned a weight of 0.05, and the quantitative result Z7 of the operation of the furnace pressure is assigned a weight of 0.1. Then the formula can be adopted: T = 0.2*Z1 + 0.2*Z2 + 0.3*Z3 + 0.1*Z4 + 0.05*Z5 + 0.05*Z6 + 0.1*Z7; where T is the target quantitative result of the kiln equipment.

[0126] S404. Based on the target quantification results and the preset evaluation threshold range, determine whether there are any abnormalities in the operating status of the kiln equipment.

[0127] The preset evaluation threshold range is a threshold range set manually to determine whether the kiln equipment is operating normally.

[0128] Specifically, the target quantification result can be compared with a preset evaluation threshold range. If the target quantification result is greater than the maximum value of the preset evaluation threshold range, the kiln equipment is considered to be operating normally. If the target quantification result is less than the minimum value of the preset evaluation threshold range, the kiln equipment is considered to be operating abnormally.

[0129] Optionally, if the target quantification result is within the range of a preset evaluation threshold, an alarm message is output, which is used to indicate that the kiln equipment should be monitored.

[0130] For example, if the target quantification result T is less than or equal to 85, an alarm command is sent to prompt that the kiln equipment needs to be monitored and spare parts need to be prepared. If T is less than or equal to 70, a maintenance command is sent to prompt that the kiln equipment needs to be repaired or replaced.

[0131] In this embodiment, the target quantitative result of the kiln equipment is obtained by weighted summation of the operation quantitative results of each component and the weight of each component. Based on the target quantitative result and the preset evaluation threshold range, it is determined whether there is any abnormality in the operation status of the kiln equipment. By comprehensively considering the operation quantitative results of each component, it is possible to accurately determine whether there is any abnormality or normality in the operation status of the kiln equipment and issue a warning.

[0132] The above embodiments illustrate how to determine whether there are any abnormalities in the operating status of the kiln equipment. After obtaining the quantitative results of the operation of each component, the quantitative results of the operation of each component can be further processed. An embodiment will be used to illustrate this. In one embodiment, such as... Figure 5 As shown, the quantitative results of the operation of each component and the weight of each component are weighted and summed to obtain the target quantitative results of the kiln equipment, including:

[0133] S502 normalizes the operational quantification results of each component to obtain the standardized quantification results of each component.

[0134] Specifically, if we take the temperature data T of three fans as an example... i (i = 1, 2, 3), 8 motor current data I1 i (i = 1, 2, ..., 8), Current data I2 of 44 heating rods i (i = 1, 2, ..., 44), 12 sections of roller state data G i (i = 1, 2, ..., 12), Total air flow rate F in the pipeline A Total oxygen flow rate F in the pipeline O 5 different furnace zone pressures P i Taking (i = 1, 2, ..., 5) as an example, the quantitative results of the fan operation are obtained as follows: Z1, Z2, Z3, Z4, Z5, Z6, Z7, and Z8 respectively. Normalization is then performed on each component.

[0135]

[0136]

[0137]

[0138]

[0139] SZ5=100×Z5 (24)

[0140] SZ6=100×Z6 (25)

[0141]

[0142] The standardized quantization results of each component after normalization can be obtained by formulas (20)-(26).

[0143] S504 calculates the target quantitative result of the kiln equipment by weighting and summing the standardized quantitative results of each component and the weight of each component.

[0144] Specifically, after obtaining the standardized quantification results of each component, the standardized quantification results of each component are assigned weights and then summed to obtain the target quantification result of the kiln equipment. For example, if the measured temperature data T of three fans is used... i (i = 1, 2, 3), 8 motor current data I1 i (i = 1, 2, ..., 8), Current data I2 of 44 heating rods i (i = 1, 2, ..., 44), 12 sections of roller state data G i (i = 1, 2, ..., 12), Total air flow rate F in the pipeline A Total oxygen flow rate F in the pipeline O 5 different furnace zone pressures P i Taking (i=1,2,…,5) as an example, we obtain the standardized quantization result SZ1 for the fan, SZ2 for the motor, SZ3 for the heating rod, SZ4 for the roller, SZ5 for the air in the pipeline, SZ6 for the oxygen in the pipeline, and SZ7 for the pressure inside the furnace. Different weights are assigned to the standardized quantification results of each component. For example, the weight of the standardized quantification result SZ1 of the fan is 0.2, the weight of the standardized quantification result SZ2 of the motor is 0.2, the weight of the standardized quantification result SZ3 of the heating rod is 0.3, the weight of the standardized quantification result SZ4 of the roller is 0.1, the weight of the standardized quantification result SZ5 of the pipeline air is 0.05, the weight of the standardized quantification result SZ6 of the pipeline oxygen is 0.05, and the weight of the standardized quantification result SZ7 of the furnace pressure is 0.1. Then the formula can be adopted: T = 0.2*SZ1 + 0.2*SZ2 + 0.3*SZ3 + 0.1*SZ4 + 0.05*SZ5 + 0.05*SZ6 + 0.1*SZ7; where T is the target quantification result of the kiln equipment.

[0145] In this embodiment, the operational quantification results of each component are normalized to obtain the standardized quantification results of each component. The standardized quantification results of each component and the weight of each component are weighted and summed to obtain the target quantification result of the kiln equipment. This can standardize the operational quantification results of each component so as to better integrate the operational quantification results of each component and obtain the accurate target quantification result of the kiln equipment.

[0146] To facilitate understanding by those skilled in the art, the method for determining the operating status of a kiln is further illustrated by an embodiment. In one embodiment, the method for determining the operating status of a kiln includes:

[0147] S100: Obtain the operating parameters of at least two components of the kiln equipment within a preset time period.

[0148] S200 uses the 3σ principle to process the operating parameters of each component to obtain the first quantization value of each component.

[0149] S300 uses the correlation coefficient method to process the operating parameters of each component to obtain the second quantization value of each component.

[0150] S400 performs a weighted summation of the first quantization value, the weight corresponding to the first quantization value, the second quantization value, and the weight corresponding to the second quantization value for each component to obtain the operational quantization result of each component.

[0151] S500 normalizes the quantitative results of the operation of each component to obtain the standardized quantitative results of each component.

[0152] S600 calculates the target quantification result of the kiln equipment by weighting and summing the standardized quantification results of each component and the weight of each component.

[0153] S700 determines whether there are any abnormalities in the operating status of the kiln equipment based on the target quantification results and the preset evaluation threshold range.

[0154] S800, if the target quantification result is greater than the maximum value of the preset evaluation threshold range, the kiln equipment is in normal operating status.

[0155] S900, if the target quantification result is less than the minimum value of the preset evaluation threshold range, the operating status of the kiln equipment is abnormal.

[0156] S1000 If the target quantification result is within the preset evaluation threshold range, an alarm message will be output. The alarm message is used to indicate that the kiln equipment should be monitored.

[0157] In this embodiment, by acquiring the operating parameters of at least two components of the kiln equipment within a preset time period, and processing the operating parameters of each component using a preset algorithm including the 3σ principle method and / or correlation coefficient method, the operating quantification results of each component are obtained. Based on the operating quantification results of each component, it is determined whether there are any abnormalities in the operating status of the kiln equipment. This allows for a comprehensive and accurate assessment of the operating status of the kiln equipment based on the operating parameters of different components, thus determining the health status of the kiln equipment. This improves the reliability of prior art in assessing a single component.

[0158] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0159] Based on the same inventive concept, this application also provides a kiln operating state determination device for implementing the kiln operating state determination method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the kiln operating state determination device provided below can be found in the limitations of the kiln operating state determination method described above, and will not be repeated here.

[0160] In one embodiment, such as Figure 6 As shown, a device for determining the operating status of a kiln is provided, comprising:

[0161] The acquisition module 601 is used to acquire the operating parameters of at least two components of the kiln equipment within a preset time period;

[0162] The quantization result determination module 602 is used to process the operating parameters of each component using a preset algorithm to obtain the quantization results of each component's operation; wherein the preset algorithm includes the 3σ principle method and / or the correlation coefficient method;

[0163] The operating status determination module 603 is used to determine whether there are any abnormalities in the operating status of the kiln equipment based on the quantitative results of the operation of each component.

[0164] Optionally, the operating parameters of at least two components include at least two of the following: temperature data of each fan, current data of each motor, operating status data of each section of roller, current data of each heating rod, air flow rate in the pipeline, total oxygen flow rate in the pipeline, and pressure data inside the furnace.

[0165] The target object determination device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0166] In one embodiment, the preset algorithm includes the 3σ principle method and the correlation coefficient method, and the quantification result determination module includes:

[0167] The first processing unit is used to process the operating parameters of each component using the 3σ principle method to obtain the first quantization value of each component.

[0168] The second processing unit is used to process the operating parameters of each component using the correlation coefficient method to obtain the second quantization value of each component.

[0169] The first determining unit is used to determine the operational quantization result of each component based on the first quantization value and the second quantization value of each component.

[0170] In one embodiment, the first processing unit is specifically used to perform a weighted summation of the first quantization value of each component, the weight corresponding to the first quantization value, the second quantization value of each component, and the weight corresponding to the second quantization value, to obtain the running quantization result of each component.

[0171] In one embodiment, the running status determination module includes:

[0172] The quantization unit is used to perform a weighted summation of the quantization results of each component's operation and the weight of each component to obtain the target quantization result of the kiln equipment.

[0173] The second determining unit is used to determine whether there is any abnormality in the operating status of the kiln equipment based on the target quantification results and the preset evaluation threshold range.

[0174] In one embodiment, the quantization unit is specifically used to normalize the quantization results of each component to obtain the standardized quantization results of each component; and to perform a weighted summation of the standardized quantization results of each component and the weight of each component to obtain the target quantization result of the kiln equipment.

[0175] In one embodiment, the second determining unit is specifically configured to determine the kiln equipment's operating status as normal if the target quantification result is greater than the maximum value of the preset evaluation threshold range, and the kiln equipment's operating status as abnormal if the target quantification result is less than the minimum value of the preset evaluation threshold range.

[0176] In one embodiment, the kiln operating status determination device further includes:

[0177] The early warning module is used to output alarm information if the target quantification result is within the preset evaluation threshold range. The alarm information is used to indicate that the kiln equipment should be monitored.

[0178] The target object determination device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0179] Each module in the aforementioned kiln operation status determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0180] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 The computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for determining the operating status of a kiln. The display screen can be an LCD screen or an e-ink display screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0181] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0182] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any of the above embodiments.

[0183] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0184] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0185] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0186] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0187] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0188] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the operating status of a kiln, characterized in that, The method includes: Obtain operating parameters of at least two components of the kiln equipment within a preset time period; the operating parameters of the at least two components include at least two of the following: temperature data of each fan, current data of each motor, operating status data of each section of roller, current data of each heating rod, air flow rate in the pipeline, total oxygen flow rate in the pipeline, and pressure data inside the furnace. The preset time period is divided into a first preset time period and a second preset time period; the operating parameters of each component collected during the first preset time period are used as standard state data; and the operating parameters of each component collected during the second preset time period are used as measurement data. Using the 3σ principle method, based on the mean and standard deviation of the standard state data, the measurement data is calculated to obtain the first quantization value of each component; using the correlation coefficient method, based on the covariance and variance between the standard state data and the measurement data, the measurement data is calculated to obtain the second quantization value of each component; based on the first quantization value and the second quantization value of each component, the operational quantization result of each component is determined. The target quantification result of the kiln equipment is obtained by weighted summation of the quantification results of the operation of each component and the weight of each component. Based on the target quantification result and the preset evaluation threshold range, it is determined whether the operating status of the kiln equipment is abnormal. If the target quantification result is greater than the maximum value of the preset evaluation threshold range, the operating status of the kiln equipment is normal; if the target quantification result is less than the minimum value of the preset evaluation threshold range, the operating status of the kiln equipment is abnormal.

2. The method according to claim 1, characterized in that, The step of determining the operational quantization result of each component based on the first quantization value and the second quantization value of each component includes: The first quantization value of each component, the weight corresponding to the first quantization value, the second quantization value of each component, and the weight corresponding to the second quantization value are weighted and summed to obtain the operation quantization result of each component.

3. The method according to claim 1, characterized in that, The step of weighted summing of the operational quantification results of each component and the weights of each component to obtain the target quantification result of the kiln equipment includes: The operational quantization results of each component are normalized to obtain the standardized quantization results of each component. The standardized quantification results of each component and the weight of each component are weighted and summed to obtain the target quantification result of the kiln equipment.

4. The method according to claim 1, characterized in that, The method further includes: If the target quantification result is within the preset evaluation threshold range, an alarm message is output, which is used to indicate that the kiln equipment should be monitored.

5. A device for determining the operating status of a kiln, characterized in that, The device includes: The acquisition module is used to acquire the operating parameters of at least two components of the kiln equipment within a preset time period; the operating parameters of the at least two components include at least two of the following: temperature data of each fan, current data of each motor, operating status data of each section of roller, current data of each heating rod, air flow rate in the pipeline, total oxygen flow rate in the pipeline, and pressure data inside the furnace. The quantization result determination module is used to divide the preset time period into a first preset time period and a second preset time period; use the operating parameters of each component collected in the first preset time period as standard state data; use the operating parameters of each component collected in the second preset time period as measurement data; use the 3σ principle method to calculate the measurement data based on the mean and standard deviation of the standard state data to obtain a first quantization value for each component; use the correlation coefficient method to calculate the measurement data based on the covariance and variance between the standard state data and the measurement data to obtain a second quantization value for each component; and determine the operating quantization result of each component based on the first quantization value and the second quantization value of each component. The operating status determination module is used to perform a weighted summation of the operating quantification results of each component and the weight of each component to obtain the target quantification result of the kiln equipment; based on the target quantification result and a preset evaluation threshold range, it determines whether the operating status of the kiln equipment is abnormal. If the target quantification result is greater than the maximum value of the preset evaluation threshold range, the operating status of the kiln equipment is normal; if the target quantification result is less than the minimum value of the preset evaluation threshold range, the operating status of the kiln equipment is abnormal.

6. The apparatus according to claim 5, characterized in that, The quantization result determination module includes: The first processing unit is used to perform a weighted summation of the first quantization value, the weight corresponding to the first quantization value, the second quantization value, and the weight corresponding to the second quantization value of each component to obtain the running quantization result of each component.

7. The apparatus according to claim 5, characterized in that, The operating status determination module includes: The quantization unit is used to normalize the quantization results of the operation of each component to obtain the standardized quantization results of each component; and to perform a weighted summation of the standardized quantization results of each component and the weight of each component to obtain the target quantization result of the kiln equipment.

8. The apparatus according to claim 5, characterized in that, The device also includes an early warning module; The early warning module is used to output an alarm message if the target quantification result is within the range of the preset evaluation threshold. The alarm message is used to indicate that the kiln equipment should be monitored.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

Images