Intelligent control method and system for raw material bin cleaning

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent control method and system for raw material bin cleaning. The method comprises the steps that flow data and pressure data of a cleaning agent in the current time period during raw material bin cleaning are obtained in real time; calculating the anomalous degree of the flow data in the previous time period; calculating the intensity of the traffic data in the previous time period; determining an adaptive cooling coefficient of the flow data in the current time period; and according to the self-adaptive cooling coefficient, the ideal flow of the flow data in the next time period is obtained through a simulated annealing algorithm, so that intelligent control over raw material bin cleaning is achieved. According to the method, the cooling coefficient is dynamically adjusted according to the anomalous degree and the intensity degree of the flow data, the robustness and the adaptability of the simulated annealing algorithm are improved, and the limitation caused by the fixed cooling coefficient is overcome; the flow is monitored and adjusted in real time, it is ensured that the cleaning agent flow is stable, the raw material bin cleaning quality is improved, and the influence of errors or fluctuation on the cleaning effect is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent control method and system for cleaning a raw material warehouse. Background Art

[0002] With the increasing requirements of modern industrial enterprises for the management and cleaning of raw material warehouses, the cleaning of raw material warehouses is not only related to production quality, but also involves whether the product safety and hygiene standards are qualified. The traditional manual cleaning method is inefficient and prone to human omissions, which does not meet the requirements of modern production enterprises for high efficiency, safety and error-free. With the development of technology, intelligent cleaning systems can reduce manual intervention, improve cleaning efficiency, and shorten cleaning cycles through automated control, thereby effectively improving the overall efficiency of the production line. In the intelligent cleaning system, the intelligent control of the cleaning agent flow directly affects the cleaning effect; if the cleaning agent flow is insufficient, it may not be able to effectively remove the residue or dirt in the raw material warehouse, resulting in incomplete cleaning; if the cleaning agent flow is too large, it will lead to waste, which not only increases the cleaning cost, but also may cause unnecessary corrosion or damage to the raw material warehouse. An existing method for intelligent control of cleaning agent flow is the simulated annealing algorithm, which is flexible and adaptable in the intelligent control of cleaning agent flow and has good global optimization capabilities.

[0003] The patent document with announcement number CN112232545B discloses an AGV task scheduling method based on simulated annealing algorithm, which belongs to the field of automated warehousing system task scheduling. It includes: 1. Determine the location coordinates and cruising range of the AGV, the location coordinates of the shelves to be transported and the picking workstation; 2. Set the parameters of the simulated annealing algorithm; 3. Generate an initial task allocation plan; 4. Start iterative search and generate candidate solutions; 5. Check the feasibility of the candidate solution. If it is not feasible, go to step 4 to regenerate the candidate solution; 6. Determine whether to accept the candidate solution. If accepted, replace the current solution with the candidate solution; 7. If the preset number of iterations is not reached, jump to step 4 for the next iterative search; 8. If the algorithm termination condition is met, output the current solution, otherwise reduce the annealing temperature and jump to step 4 to start a new round of iterative search. This method can quickly solve an AGV task scheduling problem in an intelligent warehousing system environment and obtain a task allocation plan with the shortest or approximately the shortest completion time.

[0004] However, the above patent document is not aimed at warehouse cleaning, and when using the simulated annealing algorithm for intelligent control of cleaning agent flow data, a fixed cooling coefficient (i.e., temperature drop rate) is usually used, which affects the exploration and convergence process of the algorithm; a cooling coefficient that is too small will lead to slow convergence, thereby wasting a lot of computing resources, and a cooling coefficient that is too large will cause the temperature to drop too quickly, and the algorithm will soon stop exploring new solutions and converge to the local optimal solution prematurely. In this way, although the algorithm runs faster, the final result may not be the global optimal solution; at the same time, when collecting cleaning agent flow data, since other electrical equipment (such as motors, inverters, etc.) in the cleaning system may generate electromagnetic interference, noise data is generated in the cleaning agent flow data, and the presence of noise data will affect the quantification of the changing characteristics of the cleaning agent flow data and the accuracy of the subsequent calculation of the cooling coefficient. Summary of the invention

[0005] In order to solve the problem that the use of a fixed cooling coefficient in the simulated annealing algorithm affects the exploration and convergence process of the algorithm, and at the same time, the accuracy of the calculated cooling coefficient is reduced due to the noise data generated by electromagnetic interference, the present invention provides an intelligent control method and system for raw material warehouse cleaning.

[0006] In the first aspect, the present invention provides an intelligent control method for cleaning a raw material bin, which adopts the following technical solution: A method for intelligent control of cleaning of a raw material warehouse comprises: obtaining in real time the flow data and pressure data of a cleaning agent in a current time period when cleaning the raw material warehouse; calculating the abnormality of the flow data of the previous time period based on the extreme difference of the flow data points in the flow data of the previous time period and the number of flow data points whose second-order derivative is 0 in the flow data of the previous time period; calculating the severity of the flow data of the previous time period based on the abnormality, the correlation coefficient of the flow data and pressure data of the previous time period, and the difference between the percentiles of the flow data points and pressure data points corresponding to all moments in the previous time period in their respective data segments; weighting a preset initial cooling coefficient according to the severity to obtain an adaptive cooling coefficient of the flow data of the current time period; and obtaining the ideal flow of the flow data of the next time period using a simulated annealing algorithm according to the adaptive cooling coefficient, so as to realize intelligent control of cleaning of the raw material warehouse.

[0007] By dynamically adjusting the cooling coefficient according to the degree of abnormality and severity of the flow data, the robustness and adaptability of the simulated annealing algorithm can be improved, and the limitations brought by the fixed cooling coefficient can be overcome; the flow rate can be monitored and adjusted in real time to ensure the stability of the cleaning agent flow, improve the quality of raw material warehouse cleaning, and avoid the impact of errors or fluctuations on the cleaning effect; by calculating the outliers of the flow and pressure data, the impact of electromagnetic interference and noise on the data can be reduced to ensure the accuracy of algorithm optimization; the flow rate can be accurately controlled to reduce resource waste, optimize the cleaning process, and improve production efficiency.

[0008] Furthermore, the abnormal degree satisfies the following relationship: ; In the formula, is the abnormality of the traffic data in the previous time period, and are the maximum and minimum values ​​of the traffic data points in the traffic data of the previous time period, is the number of flow data points whose second-order derivative is 0 in the flow data of the previous time period, is a hyperparameter, is the normalization function, is the absolute value symbol.

[0009] By combining the range of flow data and the number of flow data points with a second-order derivative of 0, it is possible to accurately identify abnormal fluctuations in flow data, quantify the degree of anomaly, and improve sensitivity to data changes. The number of flow data points with a second-order derivative of 0 is used to reflect the stationarity of the data, effectively reducing the impact of noise on flow data and improving the accuracy of data analysis. Through normalization processing and the introduction of hyperparameters, the calculation of the degree of anomaly is more stable for data of different scales, ensuring that the adjustment of the cooling coefficient has good adaptability.

[0010] Furthermore, the severity satisfies the following relationship: ; In the formula, is the intensity of the traffic data in the previous time period, is the abnormality of the traffic data in the previous time period, is the correlation coefficient between the flow data and pressure data in the previous time period, is the number of moments in the previous time period, For the previous period The percentile of the traffic data point corresponding to the moment in the data segment to which it belongs, For the previous period The percentile of the pressure data point corresponding to the moment in the data segment to which it belongs, is a hyperparameter, is the normalization function, is the absolute value symbol.

[0011] By combining the degree of abnormality of flow data and the correlation coefficient of flow and pressure data, it is possible to accurately measure the drastic changes in flow data and reflect the severity of fluctuations in the system; introducing the percentile differences of flow and pressure data points provides a more comprehensive assessment, which can consider the deviation between flow and pressure at the same time, making the calculation more accurate.

[0012] Furthermore, the correlation coefficient is a Pearson correlation coefficient.

[0013] Furthermore, the adaptive cooling coefficient satisfies the following relationship: ; In the formula, is the adaptive cooling coefficient of the flow data in the current time period, is the intensity of the traffic data in the previous time period, is the preset initial cooling coefficient.

[0014] By combining the intensity of flow data in the previous time period with the preset initial cooling coefficient, the cooling coefficient of the current time period can be dynamically adjusted according to the strength of flow fluctuations, ensuring that the cooling system makes optimized adjustments based on real-time needs and improves cooling efficiency; the adaptive cooling coefficient can be adjusted according to drastic changes in system flow to avoid excessive or insufficient cooling, thereby effectively maintaining stable operation of the system.

[0015] Furthermore, the use of the simulated annealing algorithm to obtain the ideal flow rate of the flow data for the next time period includes: acquiring the dirt concentration data of the raw material warehouse in the current time period in real time, constructing the objective function of the simulated annealing algorithm based on the flow data of the current time period, the values ​​of the dirt concentration data points of the raw material warehouse corresponding to the start and end times of the current time period, and the cleaning cost of the raw material warehouse in the current time period, and taking the optimal solution among the solutions of the objective function as the ideal flow rate of the flow data for the next time period.

[0016] By comprehensively considering the current flow rate, dirt concentration and cleaning cost, the simulated annealing algorithm can accurately optimize the ideal flow rate for the next time period and improve the system operation efficiency; by obtaining dirt concentration data in real time, the system can adjust the flow rate according to the actual dirt situation to avoid excessive accumulation and affect production, and maintain stable operation; optimizing flow data can reduce unnecessary cleaning frequency, reduce cleaning costs, and extend the service life of equipment; by reasonably adjusting the flow rate and balancing various factors, production efficiency can be improved and resource waste can be reduced, bringing long-term energy saving and cost savings.

[0017] Furthermore, the objective function satisfies the following relationship: ; In the formula, is the solution of the objective function, is the preset dirt concentration weight value, and are the values ​​of the dirt concentration data points corresponding to the start and end times of the current time period, is the preset cost weight value, It is the cleaning cost of the raw material warehouse in the current time period.

[0018] In a second aspect, the present invention provides an intelligent control system for cleaning a raw material warehouse, which adopts the following technical solution: A raw material warehouse cleaning intelligent control system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned raw material warehouse cleaning intelligent control method is implemented.

[0019] By adopting the above technical solution, the above-mentioned intelligent control method for cleaning a raw material warehouse is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0020] The present invention has the following technical effects: Since noise data may cause the simulated annealing algorithm to misjudge the change of flow data, thereby affecting the adaptive adjustment process of the cooling coefficient, by analyzing the intensity of the change of flow data in the previous time period, a smoother and more accurate dynamic adjustment mechanism can be provided for the current time period, effectively reducing the interference of noise, avoiding too fast or too slow temperature drop rate, and making the adjustment of the cooling coefficient more accurate, thereby optimizing the exploration process and convergence process of the simulated annealing algorithm, avoiding falling into the local optimal solution too early, and accelerating the search speed of the global optimal solution; therefore, by analyzing the change characteristics of the cleaning agent flow data and pressure data, and combining with the adaptive adjustment of the cooling coefficient, the interference of noise data on the intelligent control process can be effectively reduced, and the global optimization ability and convergence speed of the simulated annealing algorithm can be improved; in addition, the adaptive adjustment mechanism can flexibly adjust the cooling rate according to the actual changes in the flow data, so that the control process is more in line with the actual working conditions, and the intelligent control accuracy of the cleaning agent flow is improved, and finally the operation efficiency of the cleaning system is optimized, the waste of computing resources is reduced, and the stability and accuracy of the intelligent control results are ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding numbers are the same or corresponding parts.

[0022] Figure 1 It is a method flow chart of an intelligent control method for cleaning a raw material warehouse according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0025] The present invention combines the analysis of the change characteristics of the flow data and pressure data of the cleaning agent in the same time period to obtain the severity of the change in the flow data of the previous time period of the current time period, and then adapts the cooling coefficient corresponding to the flow data of the cleaning agent in the current time period according to the severity of the change in the cleaning agent flow data in the previous time period, and obtains the ideal flow in the next time period through the simulated annealing algorithm, thereby realizing intelligent control of raw material warehouse cleaning and improving control accuracy.

[0026] The embodiment of the present invention discloses an intelligent control method for cleaning a raw material warehouse, referring to Figure 1 , comprising steps S1 to S5: S1: Real-time acquisition of the flow data and pressure data of the cleaning agent in the current time period when the raw material warehouse is cleaned.

[0027] In the present invention, a high-frequency turbine flowmeter and a pressure sensor are used to collect the flow data and pressure data of the cleaning agent of the high-pressure spray gun in real time during the cleaning process. It is necessary to ensure that the two types of data are collected in the same time period. The specific collection time is the entire process of cleaning the raw material warehouse, and the collection frequency is once every two seconds; then an analog-to-digital conversion device is used to perform analog-to-digital conversion on the above two types of data.

[0028] S2: Calculate the degree of abnormality of the traffic data in the previous time period.

[0029] It should be noted that by analyzing the data change characteristics of the flow data of the cleaning agent, the degree of abnormality in each time period is obtained. When analyzing this indicator, the more discrete the numerical distribution in the flow data of a time period and the more frequent the changes, the greater the degree of abnormality of the flow data of a time period.

[0030] Based on the range of the flow data points in the flow data of the previous time period and the number of flow data points whose second-order derivative is 0 in the flow data of the previous time period, the abnormality of the flow data of the previous time period is calculated.

[0031] Specifically, the abnormal degree satisfies the following relationship: ; In the formula, is the abnormality of the traffic data in the previous time period, and are the maximum and minimum values ​​of the traffic data points in the traffic data of the previous time period, is the number of flow data points whose second-order derivative is 0 in the flow data of the previous time period, is a hyperparameter, is the normalization function, is the absolute value symbol.

[0032] Implementers can set hyperparameters according to specific implementation conditions, for example, 0.1. The existence of hyperparameters is to prevent , making the formula meaningless.

[0033] in, Indicates the range of the flow data points in the flow data of the previous time period. The larger the value, the greater the abnormality of the flow data in the previous time period. The larger it is, the more frequent the numerical changes in the traffic data of the previous time period are. This can confirm that the greater the range in the traffic data of the previous time period, the greater the credibility, and the greater the abnormality of the traffic data of the previous time period.

[0034] S3: Calculate the severity of the traffic data in the previous time period.

[0035] It should be noted that since there may be noise data in the data, the normal flow data will show the same abnormality as the abnormal flow data, so the possibility of noise data needs to be excluded; according to the scene survey, the pressure energy of the fluid at the nozzle of the high-pressure spray gun is converted into kinetic energy, resulting in an increase in flow rate, so the flow of the spray gun is often positively correlated with the pressure of the system; and if there is noise data in the flow data, the correlation between the two data will decrease; then, in this step, the correlation between the flow data of the cleaning agent and the change characteristics between the pressure data will be analyzed, and the severity of the flow data will be obtained in combination with the abnormality of the flow data. Among them, the greater the correlation between the change characteristics between the flow data and the pressure data of the cleaning agent, the less likely it is that there is noise in the flow data of this time period. If the abnormality of the flow data of this time period is large, the less likely it is noise and the greater the possibility of the real change of the high-pressure spray gun itself, then the severity of the flow data of this time period will also be greater.

[0036] Based on the degree of abnormality, the correlation coefficient of the flow data and pressure data of the previous time period, and the difference between the percentiles of the flow data points and pressure data points corresponding to all moments in the previous time period in their respective data segments, the severity of the flow data of the previous time period is calculated.

[0037] Specifically, the severity satisfies the following relationship: ; In the formula, is the intensity of the traffic data in the previous time period, is the abnormality of the traffic data in the previous time period, is the correlation coefficient between the flow data and pressure data in the previous time period, is the number of moments in the previous time period, The first The percentile of the traffic data point corresponding to the moment in the data segment to which it belongs, The first The percentile of the pressure data point corresponding to the moment in the data segment to which it belongs, is a hyperparameter, is the normalization function, is the absolute value symbol.

[0038] Implementers can set hyperparameters according to specific implementation conditions, for example, 0.1. The existence of hyperparameters is to prevent , making the formula meaningless.

[0039] Specifically, the correlation coefficient is the Pearson correlation coefficient.

[0040] in, The larger the value, the more drastic and frequent the changes in the traffic data in the previous time period, and the greater the drastic degree of the traffic data in the previous time period; The larger it is, the greater the correlation between the flow data and pressure data in the previous time period. If the abnormality of the flow data in the previous time period is large, the possibility that this situation is caused by noise is smaller, and the possibility that it is a real change of the high-pressure spray gun itself is greater, and the drastic degree of the flow data in the previous time period will also be greater; It represents the sum of the differences between the percentiles of the flow data points and pressure data points corresponding to all moments in the previous time period in their respective data segments. The smaller the value, the greater the correlation between the flow data and the pressure data in the previous time period. It can also confirm that the credibility of the Pearson correlation coefficient between the flow data and the pressure data in the previous time period is greater. If the abnormality of the flow data in the current time period is large, the possibility that this situation is caused by noise is smaller, and the possibility that it is a real change of the high-pressure spray gun itself is greater, and the intensity of the flow data in the previous time period will also be greater.

[0041] S4: Determine an adaptive cooling coefficient for the flow data of the current time period.

[0042] It should be noted that when the cooling coefficient of the flow data is adaptively adjusted, it is first necessary to clarify that the frequency of the regulation of the flow data in the present invention is that one flow data regulation value corresponds to one time period (including multiple sampling moments), rather than one flow data regulation value corresponding to one sampling moment. This is because the regulation of the flow data based on the time period can effectively solve the problems of equipment instability, high control complexity and resource waste caused by instantaneous flow fluctuations; then, a time period (for example, a time length of one minute) is determined as a benchmark to regulate the flow data; finally, when calculating the control value of the flow data corresponding to a time period, it is necessary to adapt its cooling coefficient, and the reference basis is the intensity of the flow data in the previous time period immediately adjacent to this time period. (Although the flow data in the previous time period may also correspond to a control value of the flow data, the flow data will not completely remain at a constant control value in a time period, but will fluctuate within a reasonable range, so it will have different degrees of intensity for reference.) Therefore, taking the current time period as an example, according to the intensity of the flow data in the previous time period of the current time period, the corresponding adaptive cooling coefficient when the flow data of the current time period is intelligently controlled when the simulated annealing algorithm is used later is calculated.

[0043] The preset initial cooling coefficient is weighted according to the severity of the flow data in the previous time period to obtain the adaptive cooling coefficient of the flow data in the current time period.

[0044] Specifically, the adaptive cooling coefficient satisfies the following relationship: ; In the formula, is the adaptive cooling coefficient of the flow data in the current time period, is the intensity of the traffic data in the previous time period, is the preset initial cooling coefficient.

[0045] The implementer can set the initial cooling coefficient according to the specific implementation situation, for example, 0.97. If the current time period is the first time period for the start of raw material warehouse cleaning, the adaptive cooling coefficient of the current time period is the same as the initial cooling coefficient.

[0046] in, The larger it is, the larger the adaptive cooling coefficient of the flow data in the current time period should be, so that the temperature drops slower and the number of iterations increases, to ensure that the iteration for the optimal solution can be more accurate.

[0047] S5: According to the adaptive cooling coefficient, the ideal flow rate of the flow data in the next time period is obtained by using a simulated annealing algorithm to achieve intelligent control of the raw material warehouse cleaning.

[0048] Specifically, the method of obtaining the ideal flow rate of the flow data of the next time period by using the simulated annealing algorithm includes: The initial parameters in the simulated annealing algorithm are preset. For example, the initial temperature value is set to the empirical value of 100°C, the temperature threshold is set to the empirical value of 60°C, the initial solution of the flow data of the cleaning agent is set to the empirical value of 20L / min, the random perturbation range in each iteration is set to the range of 5L / min below the value of the flow setting value in the previous annealing process, the cooling coefficient is 0.97, and the subsequent parameters are adaptively adjusted according to the adaptive cooling coefficient; The dirt concentration data of the raw material warehouse in the current time period is obtained in real time. According to the flow data of the current time period, the values ​​of the dirt concentration data points corresponding to the start and end times of the current time period, and the cleaning cost of the raw material warehouse in the current time period, the objective function of the simulated annealing algorithm is constructed, and then the optimal solution of the flow corresponding to the flow data of the current time period is calculated and output. The specific calculation method of the optimal solution is the existing public technology of the simulated annealing algorithm, which will not be repeated here. The optimal solution in the solution of the objective function is used as the ideal flow of the flow data of the next time period.

[0049] Specifically, the objective function satisfies the following relationship: ; In the formula, is the solution of the objective function, is the preset dirt concentration weight value, and are the values ​​of the dirt concentration data points corresponding to the start and end times of the current time period, is the preset cost weight value, It is the cleaning cost of the raw material warehouse in the current time period.

[0050] Implementers can set the dirt concentration weight value and the cost weight value according to the specific implementation situation. For example, the dirt concentration weight value is 0.5 and the cost weight value is 0.5.

[0051] Among them, the objective function Indicates the dirt removal effect in the current time period. The larger the value, the larger the solution of the objective function, and the worse the priority. The larger it is, the larger the solution of the objective function is and the worse the priority will be.

[0052] An embodiment of the present invention further discloses an intelligent control system for cleaning a raw material bin, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent control method for cleaning a raw material bin according to the present invention is implemented.

[0053] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0054] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0055] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0056] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A raw material warehouse cleaning intelligent control method, characterized in that: include: Real-time acquisition of the flow data and pressure data of the cleaning agent in the current time period when the raw material warehouse is cleaned; Based on the range of the flow data points in the flow data of the previous time period and the number of flow data points whose second-order derivative is 0 in the flow data of the previous time period, the abnormality of the flow data of the previous time period is calculated; Calculate the severity of the flow data in the previous time period based on the abnormality, the correlation coefficient of the flow data and the pressure data in the previous time period, and the difference between the percentiles of the flow data points and the pressure data points corresponding to all moments in the previous time period in the corresponding data segments; The preset initial cooling coefficient is weighted according to the severity to obtain an adaptive cooling coefficient of the flow data of the current time period; According to the adaptive cooling coefficient, the ideal flow rate of the flow data in the next time period is obtained by using a simulated annealing algorithm to realize intelligent control of the cleaning of the raw material warehouse.

2. The intelligent control method for cleaning a raw material warehouse according to claim 1, characterized in that: The abnormal degree satisfies the following relationship: ； In the formula, is the abnormality of the traffic data in the previous time period, and are the maximum and minimum values ​​of the traffic data points in the traffic data of the previous time period, is the number of flow data points whose second-order derivative is 0 in the flow data of the previous time period, is a hyperparameter, is the normalization function, is the absolute value symbol.

3. The intelligent control method for cleaning raw material warehouse according to claim 1, characterized in that: The severity satisfies the following relationship: ； In the formula, is the intensity of the traffic data in the previous time period, is the abnormality of the traffic data in the previous time period, is the correlation coefficient between the flow data and pressure data in the previous time period, is the number of moments in the previous time period, For the previous period The percentile of the traffic data point corresponding to the moment in the data segment to which it belongs, For the previous period The percentile of the pressure data point corresponding to the moment in the data segment to which it belongs, is a hyperparameter, is the normalization function, is the absolute value symbol.

4. A raw material warehouse cleaning intelligent control method according to claim 1 or 3, characterized in that: The correlation coefficient is the Pearson correlation coefficient.

5. The intelligent control method for cleaning raw material warehouse according to claim 1, characterized in that: The adaptive cooling coefficient satisfies the following relationship: ； In the formula, is the adaptive cooling coefficient of the flow data in the current time period, is the intensity of the traffic data in the previous time period, is the preset initial cooling coefficient.

6. The intelligent control method for cleaning raw material silo according to claim 1, characterized in that: The method of using the simulated annealing algorithm to obtain the ideal flow rate of the flow data in the next time period includes: The dirt concentration data of the raw material warehouse in the current time period is obtained in real time. According to the flow data of the current time period, the values ​​of the dirt concentration data points of the raw material warehouse corresponding to the start and end times of the current time period, and the cleaning cost of the raw material warehouse in the current time period, the objective function of the simulated annealing algorithm is constructed, and the optimal solution among the solutions of the objective function is used as the ideal flow rate of the flow data for the next time period.

7. The intelligent control method for cleaning a raw material warehouse according to claim 1, characterized in that: The objective function satisfies the following relationship: ； In the formula, is the solution of the objective function, is the preset dirt concentration weight value, and are the values ​​of the dirt concentration data points corresponding to the start and end times of the current time period, is the preset cost weight value, It is the cleaning cost of the raw material warehouse in the current time period.

8. An intelligent control system for cleaning raw material warehouse, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent control method for cleaning a raw material warehouse according to any one of claims 1 to 7 is implemented.

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