Artificial intelligence-based sand mixing device dynamic simulation control system

Abstract

The present application belongs to the field of sand mixing equipment control, and relates to data analysis technology, and is used for solving the problem that the prior art cannot set the operation parameters of the sand mixing equipment according to the user demand, and in particular relates to a sand mixing equipment dynamic simulation control system based on artificial intelligence, which comprises simulation test modules, test analysis modules, demand analysis modules and control execution modules connected in sequence; the simulation test module is used for simulation test analysis of the sand mixing equipment; a plurality of sets of test values are generated for the operation parameters of the sand mixing equipment, the operation parameters of the sand mixing equipment are set according to the test values, and an effect coefficient is obtained at the end of the sand mixing process; the test data of the sand mixing equipment can be processed and analyzed, the test values are differentially marked through the effect coefficient, and then the necessity of optimizing the sand mixing formula is determined according to the marking result, and the sand mixing formula is optimized when necessary, so that the sand mixing effect of the sand mixing equipment is further ensured.

Description

Technical Field

[0001] This invention belongs to the field of sand mixing equipment control and involves data analysis technology, specifically a dynamic simulation control system for sand mixing equipment based on artificial intelligence. Background Technology

[0002] Sand mixing equipment is a key piece of equipment used in industries such as casting, chemical, and building materials for mixing sand, binders, and other additives. The sand mixer uses the relative motion of the grinding wheel and the grinding disc to crush the material placed between them through a grinding and crushing action. The sand mixer also mixes the material while crushing it. The stability and accuracy of its control system directly affect the mixing quality and production efficiency.

[0003] The invention patent with announcement number CN111830849B discloses a dynamic simulation system and device for sand mixing trucks. This simulation system solves the problem that in the prior art, the operation interface of sand mixing trucks is all data display, without a dynamic simulation system, making it impossible to intuitively understand the equipment operation status, and the equipment's operability and fault identification are poor. However, the system cannot set the operating parameters of the sand mixing equipment according to user needs, and cannot take into account both the stability of equipment operation and the sand mixing effect.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic simulation control system for sand mixing equipment based on artificial intelligence, which solves the problem that existing technologies cannot set the operating parameters of sand mixing equipment according to user needs;

[0006] The technical problem to be solved by this invention is: how to provide an artificial intelligence-based dynamic simulation control system for sand mixing equipment that allows users to set operating parameters according to their needs.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] The dynamic simulation control system for sand mixing equipment based on artificial intelligence includes a simulation test module, a test analysis module, a requirements analysis module, and a control execution module connected in sequence.

[0009] The simulation test module is used to perform simulation test analysis on the sand mixing equipment: retrieve a sand mixing formula, generate several sets of test values ​​for the operating parameters of the sand mixing equipment, and assign a sand mixing equipment to each set of test values; set the operating parameters of the sand mixing equipment according to the test values, and obtain the effect coefficient at the end of the sand mixing process; send the effect coefficients of all test values ​​corresponding to the same sand mixing formula to the test analysis module.

[0010] The test analysis module is used to process and analyze the test data of the sand mixing equipment: the test values ​​are marked as valid or invalid values ​​by the effect coefficient; if all test values ​​corresponding to the same sand mixing formula are marked as invalid values, a formula optimization signal is generated and sent to the mobile terminal of the management personnel; if there are valid values ​​among all test values ​​corresponding to the same sand mixing formula, a demand analysis signal is generated and sent to the demand analysis module.

[0011] The demand analysis module is used to perform demand analysis on the sand mixing equipment: obtain the motor surface temperature value and bearing temperature value of the sand mixing equipment during the sand mixing process, mark the sum of the maximum values ​​of the motor surface temperature value and bearing temperature value during the sand mixing process as the fault risk value, mark the stable demand value and the effect demand value through the fault risk value and the effect coefficient; and send the stable demand value and the effect demand value of all sand mixing formulas to the control execution module.

[0012] The control execution module is used to perform sand mixing control analysis on the sand mixing equipment.

[0013] Furthermore, the sand, binder and additives are weighed and proportioned according to the sand mixing formula. The proportioned raw materials are then put into the sand mixing equipment. The operating parameters include motor output speed, mixing time and heating temperature. The test value is generated by randomly selecting a value from the range of the corresponding operating parameters.

[0014] Furthermore, the process of obtaining the effect coefficient corresponding to the test values ​​includes: obtaining the moisture content of the sand at the end of the sand mixing process and marking it as the moisture content value; retrieving the moisture content range of the sand; marking the average of the maximum and minimum values ​​of the moisture content range as the moisture content standard value; marking the absolute value of the difference between the moisture content value and the moisture content standard value as the moisture content deviation value; and marking the ratio of the moisture content deviation value to the moisture content standard value as the moisture content deviation coefficient. At the end of the sand mixing process, an image of the sand surface is captured to obtain a detection image. Gaussian filtering is used to eliminate the interference of sand grain surface texture in the detection image. Then, the energy coefficient of the detection image is extracted through the gray-level co-occurrence matrix, and the difference between the energy coefficient and the moisture content deviation coefficient is marked as the effect coefficient.

[0015] Furthermore, the specific process of marking test values ​​as valid or invalid values ​​includes: obtaining the effect threshold from the database, comparing the effect coefficient with the effect threshold; if the effect coefficient is less than the effect threshold, it is determined that the sand mixing effect of the corresponding sand mixing process does not meet the requirements, and the corresponding test value is marked as invalid; if the effect coefficient is greater than or equal to the effect threshold, it is determined that the sand mixing effect of the corresponding sand mixing process meets the requirements, and the corresponding test value is marked as valid.

[0016] Furthermore, the process of marking stable demand values ​​includes: marking the effective value corresponding to the sand mixing process with the lowest failure risk value as the stable demand value of the sand mixing formula;

[0017] The process of marking the effect requirement value includes: obtaining the failure risk threshold from the database, comparing the failure risk value of each sand mixing process with the failure risk threshold one by one; if the failure risk value is less than the failure risk threshold, the corresponding sand mixing process is marked as a safe process; if the failure risk value is greater than or equal to the failure risk threshold, the corresponding sand mixing process is marked as a risky process; and marking the effective value corresponding to the safe process with the largest effect coefficient as the effect requirement value of the sand mixing formula.

[0018] Furthermore, the specific process of the control execution module performing sand mixing control analysis on the sand mixing equipment includes: generating a control cycle; when a sand mixing task is received within the control cycle, retrieving the stable demand value and effect demand value corresponding to the sand mixing formula; allowing the user to choose either the stable demand value or the effect demand value to set the operating parameters of the sand mixing equipment; if the user does not make a selection, the stable demand value is used first for setting the operating parameters; after the sand mixing is completed, calculating the effect coefficient of this sand mixing process; and marking the difference between the expected effect coefficient and the effect coefficient of this sand mixing process as the expected deviation value.

[0019] Furthermore, the specific process of the control execution module performing sand mixing control analysis on the sand mixing equipment also includes: at the end of the control cycle, summing and averaging the expected deviation values ​​of all sand mixing processes to obtain the expected deviation coefficient, obtaining the expected deviation threshold from the database, and comparing the expected deviation coefficient with the expected deviation threshold: if the expected deviation coefficient is less than the expected deviation threshold, it is determined that the overall sand mixing effect of the control cycle meets the requirements; if the expected deviation coefficient is greater than or equal to the expected deviation threshold, it is determined that the overall sand mixing effect of the control cycle does not meet the requirements, generating a retest signal and sending the retest signal to the simulation test module.

[0020] Furthermore, the working method of this AI-based dynamic simulation control system for sand mixing equipment includes the following steps:

[0021] Step 1: Conduct simulation testing and analysis on the sand mixing equipment;

[0022] Step 2: Process and analyze the test data from the sand mixing equipment;

[0023] Step 3: Conduct a requirements analysis for sand mixing equipment;

[0024] Step 4: Conduct sand mixing control analysis on the sand mixing equipment.

[0025] The present invention has the following beneficial effects:

[0026] 1. The simulation test module can perform simulation test analysis on sand mixing equipment, generate multiple sets of test values ​​for each sand mixing formula, set the operating parameters according to the test values, calculate the effect coefficient of the sand mixing process, and thus independently match test values ​​for different sand mixing formulas to ensure the sand mixing effect of different sand mixing formulas.

[0027] 2. The test analysis module can process and analyze the test data of the sand mixing equipment, mark the test values ​​with the effect coefficient, and then determine the necessity of optimizing the sand mixing formula based on the marking results. If necessary, the sand mixing formula can be optimized to further ensure the sand mixing effect of the sand mixing equipment.

[0028] 3. The demand analysis module can perform demand analysis on sand mixing equipment, generate stable demand values ​​and effect demand values ​​from the perspective of user needs, and then control the equipment operation stability and sand mixing effect based on the stable demand values ​​and effect demand values.

[0029] 4. The control execution module can perform sand mixing control analysis on the sand mixing effect, and statistically analyze the expected deviation coefficient at the end of the control cycle. The overall sand mixing effect is evaluated based on the expected deviation coefficient. When the overall sand mixing effect is abnormal, retesting is carried out in a timely manner. The control basis is dynamically adjusted according to the operation stage of the sand mixer. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0032] Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0033] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1: As Figure 1As shown, the dynamic simulation control system for sand mixing equipment based on artificial intelligence includes a simulation test module, a test analysis module, a requirements analysis module, and a control execution module connected in sequence. The test analysis module, the requirements analysis module, and the control execution module are all connected to the database, and the control execution module is also connected to the simulation test module.

[0035] The simulation testing module is used to perform simulation testing and analysis on sand mixing equipment. It retrieves a sand mixing formula, weighs and proportions the sand, binder, and additives according to the formula, inputs the proportioned raw materials into the sand mixing equipment, and generates several sets of test values ​​for the equipment's operating parameters, including motor output speed, mixing time, and heating temperature. The test value generation process involves randomly selecting a value from the corresponding operating parameter range and assigning a sand mixing device to each set of test values. The operating parameters of the sand mixing equipment are set according to the test values. At the end of the sand mixing process, the moisture content of the sand is obtained and marked as the moisture content value. The range of sand moisture content is retrieved, and the average of the maximum and minimum values ​​within the range is marked as the standard moisture content value. The absolute value of the difference between the moisture content value and the standard moisture content value is marked as the moisture content deviation value, and the ratio of the moisture content deviation value to the standard moisture content value is marked as the moisture content deviation coefficient. Finally, images of the sand surface are captured at the end of the sand mixing process. The detection image is obtained, and Gaussian filtering is used to eliminate surface texture interference from sand particles. Gaussian filtering is a linear smoothing filter suitable for eliminating Gaussian noise and is widely used in image processing noise reduction. Then, the energy coefficient (image energy parameter) of the detection image is extracted using the gray-level co-occurrence matrix. The gray-level co-occurrence matrix refers to a common method for describing texture by studying the spatial correlation characteristics of gray levels. The energy coefficient is used to reflect the uniformity of sand particle distribution; the higher the energy coefficient (closer to 1), the more uniform the sand particle distribution. The difference between the energy coefficient and the moisture content deviation coefficient is marked as the effect coefficient. After testing all test values ​​corresponding to the same sand mixing formula and obtaining the effect coefficient, all effect coefficients are sent to the test analysis module. Multiple sets of test values ​​are generated for each sand mixing formula. After setting the running parameters according to the test values, the effect coefficient of the sand mixing process is calculated, thereby independently matching test values ​​for different sand mixing formulas to ensure the sand mixing effect of different sand mixing formulas.

[0036] The test analysis module processes and analyzes the test data of the sand mixing equipment. It obtains the effect threshold from the database and compares the effect coefficient with the effect threshold. If the effect coefficient is less than the effect threshold, the sand mixing effect of the corresponding sand mixing process is deemed unsatisfactory, and the corresponding test value is marked as invalid. If the effect coefficient is greater than or equal to the effect threshold, the sand mixing effect of the corresponding sand mixing process is deemed satisfactory, and the corresponding test value is marked as valid. If all test values ​​corresponding to the same sand mixing formula are marked as invalid, a formula optimization signal is generated and sent to the administrator's mobile terminal. If valid values ​​exist among all test values ​​corresponding to the same sand mixing formula, a demand analysis signal is generated and sent to the demand analysis module. The test values ​​are differentiated using the effect coefficient, and the necessity for optimizing the sand mixing formula is determined based on the marking results. If necessary, the sand mixing formula is optimized to further ensure the sand mixing effect of the equipment.

[0037] The requirements analysis module is used to perform requirements analysis on the sand mixing equipment: It acquires the motor surface temperature and bearing temperature values ​​during the sand mixing process, marks the sum of the maximum values ​​of these two temperatures as the fault risk value, and marks the effective value corresponding to the sand mixing process with the lowest fault risk value as the stable requirement value of the sand mixing formula. It obtains the fault risk threshold from the database and compares the fault risk value of each sand mixing process with the threshold: if the fault risk value is less than the threshold, the corresponding sand mixing process is marked as a safe process; if the fault risk value is greater than or equal to the threshold, the corresponding sand mixing process is marked as a risky process. The effective value corresponding to the safe process with the highest effect coefficient is marked as the effect requirement value of the sand mixing formula. It sends the stable requirement values ​​and effect requirement values ​​of all sand mixing formulas to the control execution module. From the perspective of user needs, it generates stable requirement values ​​and effect requirement values, and then controls the equipment's operational stability and sand mixing effect based on these values.

[0038] The control execution module is used for sand mixing control analysis of the sand mixing equipment: It generates a control cycle; when a sand mixing task is received within the control cycle, it retrieves the stable demand value and effect demand value corresponding to the sand mixing formula. The user can choose either the stable demand value or the effect demand value to set the operating parameters of the sand mixing equipment. If the user does not make a selection, the stable demand value is used first. After sand mixing is completed, the effect coefficient of this sand mixing process is calculated, and the difference between the expected effect coefficient and the effect coefficient of this sand mixing process is marked as the expected deviation value (the expected effect coefficient is the effect coefficient of the stable demand value or the effect demand value during the test). At the end of the control cycle, all sand mixing processes are analyzed. The expected deviation values ​​of the process are summed and averaged to obtain the expected deviation coefficient. The expected deviation threshold is obtained from the database. The expected deviation coefficient is compared with the expected deviation threshold: if the expected deviation coefficient is less than the expected deviation threshold, the overall sand mixing effect of the control cycle is determined to meet the requirements; if the expected deviation coefficient is greater than or equal to the expected deviation threshold, the overall sand mixing effect of the control cycle is determined to not meet the requirements, a retest signal is generated and sent to the simulation test module; at the end of the control cycle, the expected deviation coefficient is statistically analyzed, and the overall sand mixing effect is evaluated based on the expected deviation coefficient. When the overall sand mixing effect is abnormal, a retest is performed in a timely manner, and the control is dynamically adjusted according to the operating stage of the sand mixer.

[0039] Example 2: Figure 2 As shown, the dynamic simulation control method for sand mixing equipment based on artificial intelligence includes the following steps:

[0040] Step 1: Simulation test analysis of sand mixing equipment: Select a sand mixing formula, put the proportioned raw materials into the sand mixing equipment, generate several sets of test values ​​for the operating parameters of the sand mixing equipment, set the operating parameters of the sand mixing equipment according to the test values, and record the effect coefficient of the sand mixing process.

[0041] Step 2: Process and analyze the test data of the sand mixing equipment: mark the test values ​​as invalid or valid values ​​based on the effect coefficient;

[0042] Step 3: Conduct a demand analysis for the sand mixing equipment: Obtain the failure risk value of the sand mixing process, and mark the stable demand value and the effect demand value in the effective values ​​by using the failure risk value and the effect coefficient;

[0043] Step 4: Perform sand mixing control analysis on the sand mixing equipment: Generate a control cycle. When a sand mixing task is received within the control cycle, retrieve the stable demand value and effect demand value corresponding to the sand mixing formula. The user selects the stable demand value or the effect demand value to set the operating parameters. After the settings are completed, automatic sand mixing is performed.

[0044] The AI-based dynamic simulation control system for sand mixing equipment operates by retrieving a sand mixing formula, feeding the proportioned raw materials into the mixing equipment, generating several sets of test values ​​for the equipment's operating parameters, setting the parameters according to these values, and recording the effect coefficient of the mixing process. The system then marks the test values ​​as invalid or valid based on the effect coefficient. It acquires the fault risk value of the mixing process and marks the stable demand value and effect demand value among the valid values ​​using the fault risk value and the effect coefficient. Finally, it generates a control cycle. When a sand mixing task is received within the control cycle, it retrieves the stable demand value and effect demand value corresponding to the sand mixing formula. The user selects either the stable demand value or the effect demand value to set the operating parameters, and then the system automatically mixes the sand.

[0045] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0046] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0047] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A dynamic simulation control system for sand mixing equipment based on artificial intelligence, characterized in that, It includes a simulation testing module, a test analysis module, a requirements analysis module, and a control execution module connected in sequence; The simulation test module is used to perform simulation test analysis on the sand mixing equipment: retrieve a sand mixing formula, generate several sets of test values ​​for the operating parameters of the sand mixing equipment, and assign a sand mixing equipment to each set of test values; set the operating parameters of the sand mixing equipment according to the test values, and obtain the effect coefficient at the end of the sand mixing process; send the effect coefficients of all test values ​​corresponding to the same sand mixing formula to the test analysis module. The test analysis module is used to process and analyze the test data of the sand mixing equipment: the test values ​​are marked as valid or invalid values ​​by the effect coefficient; if all test values ​​corresponding to the same sand mixing formula are marked as invalid values, a formula optimization signal is generated and sent to the mobile terminal of the management personnel. If there is a valid value among all the test values ​​corresponding to the same sand mixing formula, a demand analysis signal is generated and sent to the demand analysis module. The demand analysis module is used to perform demand analysis on the sand mixing equipment: obtain the motor surface temperature value and bearing temperature value of the sand mixing equipment during the sand mixing process, mark the sum of the maximum values ​​of the motor surface temperature value and bearing temperature value during the sand mixing process as the fault risk value, mark the stable demand value and the effect demand value through the fault risk value and the effect coefficient; and send the stable demand value and the effect demand value of all sand mixing formulas to the control execution module. The control execution module is used to perform sand mixing control analysis on the sand mixing equipment; The process of obtaining the effect coefficient corresponding to the test value includes: obtaining the moisture content of the sand at the end of the sand mixing process and marking it as the moisture content value; retrieving the moisture content range of the sand; marking the average of the maximum and minimum values ​​of the moisture content range as the moisture content standard value; marking the absolute value of the difference between the moisture content value and the moisture content standard value as the moisture content deviation value; and marking the ratio of the moisture content deviation value to the moisture content standard value as the moisture content deviation coefficient. At the end of the sand mixing process, the surface of the sand is photographed to obtain the test image. Gaussian filtering is used to eliminate the interference of sand grain surface texture in the test image. Then, the energy coefficient of the test image is extracted through the gray-level co-occurrence matrix. The difference between the energy coefficient and the moisture content deviation coefficient is marked as the effect coefficient.

2. The dynamic simulation control system for sand mixing equipment based on artificial intelligence according to claim 1, characterized in that, According to the sand mixing formula, the sand, binder and additives are weighed and proportioned. The proportioned raw materials are put into the sand mixing equipment. The operating parameters include motor output speed, mixing time and heating temperature. The test value is generated by randomly selecting a value from the corresponding operating parameter range as the test value.

3. The dynamic simulation control system for sand mixing equipment based on artificial intelligence according to claim 2, characterized in that, The specific process of marking test values ​​as valid or invalid values ​​includes: obtaining the effect threshold from the database, comparing the effect coefficient with the effect threshold; if the effect coefficient is less than the effect threshold, it is determined that the sand mixing effect of the corresponding sand mixing process does not meet the requirements, and the corresponding test value is marked as invalid; if the effect coefficient is greater than or equal to the effect threshold, it is determined that the sand mixing effect of the corresponding sand mixing process meets the requirements, and the corresponding test value is marked as valid.

4. The dynamic simulation control system for sand mixing equipment based on artificial intelligence according to claim 3, characterized in that, The process of marking stable demand values ​​includes: marking the effective value corresponding to the sand mixing process with the lowest failure risk value as the stable demand value of the sand mixing formula; The process of marking the effect requirement value includes: obtaining the failure risk threshold from the database, comparing the failure risk value of each sand mixing process with the failure risk threshold one by one; if the failure risk value is less than the failure risk threshold, the corresponding sand mixing process is marked as a safe process; if the failure risk value is greater than or equal to the failure risk threshold, the corresponding sand mixing process is marked as a risky process; and marking the effective value corresponding to the safe process with the largest effect coefficient as the effect requirement value of the sand mixing formula.

5. The dynamic simulation control system for sand mixing equipment based on artificial intelligence according to claim 4, characterized in that, The specific process of the control execution module performing sand mixing control analysis on the sand mixing equipment includes: generating a control cycle; when a sand mixing task is received within the control cycle, retrieving the stable demand value and effect demand value corresponding to the sand mixing formula; allowing the user to choose either the stable demand value or the effect demand value to set the operating parameters of the sand mixing equipment; if the user does not make a selection, the stable demand value is used first for setting the operating parameters; after the sand mixing is completed, calculating the effect coefficient of this sand mixing process; and marking the difference between the expected effect coefficient and the effect coefficient of this sand mixing process as the expected deviation value.

6. The dynamic simulation control system for sand mixing equipment based on artificial intelligence according to claim 5, characterized in that, The specific process of the control execution module performing sand mixing control analysis on the sand mixing equipment also includes: at the end of the control cycle, summing and averaging the expected deviation values ​​of all sand mixing processes to obtain the expected deviation coefficient, obtaining the expected deviation threshold from the database, and comparing the expected deviation coefficient with the expected deviation threshold: if the expected deviation coefficient is less than the expected deviation threshold, it is determined that the overall sand mixing effect of the control cycle meets the requirements; if the expected deviation coefficient is greater than or equal to the expected deviation threshold, it is determined that the overall sand mixing effect of the control cycle does not meet the requirements, generating a retest signal and sending the retest signal to the simulation test module.

7. The dynamic simulation control system for sand mixing equipment based on artificial intelligence according to any one of claims 1-6, characterized in that, The working method of this AI-based dynamic simulation control system for sand mixing equipment includes the following steps: Step 1: Conduct simulation testing and analysis on the sand mixing equipment; Step 2: Process and analyze the test data from the sand mixing equipment; Step 3: Conduct a requirements analysis for sand mixing equipment; Step 4: Conduct sand mixing control analysis on the sand mixing equipment.

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