Dynamic simulation control system of sand mixing equipment based on artificial intelligence

Abstract

The invention belongs to the field of sand mixing equipment control, relates to a data analysis technology, and aims to solve the problem that operation parameters of sand mixing equipment cannot be set according to user requirements in the prior art, in particular to a sand mixing equipment dynamic simulation control system based on artificial intelligence. Comprising a simulation test module, a test analysis module, a demand analysis module and a control execution module which are connected in sequence, the simulation test module is used for performing simulation test analysis on the sand mulling equipment: generating a plurality of groups of test numerical values for operation parameters of the sand mulling equipment, setting the operation parameters of the sand mulling equipment according to the test numerical values, and obtaining an effect coefficient when the sand mulling process is finished; according to the method, the test data of the sand mixing equipment can be processed and analyzed, the test numerical values are differentially marked through the effect coefficient, then the optimization necessity of the sand mixing formula is judged according to the marking result, the sand mixing formula is optimized when necessary, and the sand mixing effect of the sand mixing equipment is further guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of sand mixing equipment control and relates to data analysis technology, in particular to a dynamic simulation control system for sand mixing equipment based on artificial intelligence. Background Art

[0002] Sand mixing equipment is a key equipment used to mix sand, binders and other additives in the foundry, chemical, building materials and other industries. The sand mixer uses the relative movement of the grinding wheel and the grinding disc to crush the material placed between the two by grinding and grinding. The sand mixer not only crushes the material but also mixes it. The stability and accuracy of its control system directly affect the mixing quality and production efficiency.

[0003] Patent publication CN111830849B discloses a dynamic simulation system and device for a sand blender truck. This simulation system addresses the existing problem of a sand blender truck operating in which the operating interface is solely data display with no dynamic simulation system, resulting in an inability to intuitively understand the equipment's operating status, and poor controllability and fault identifiability. However, this system cannot set the operating parameters of the sand blender truck according to user needs, and cannot balance equipment operating stability with sand blending performance.

[0004] In response to the above technical problems, this application proposes a solution. Summary of the Invention

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

[0006] The technical problem to be solved by the present invention is: how to provide an artificial intelligence-based dynamic simulation control system for sand mixing equipment that can set the operating parameters of the sand mixing equipment according to user needs.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] The artificial intelligence-based dynamic simulation control system for sand mixing equipment includes a simulation test module, a test analysis module, a demand 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: calling a sand mixing formula, generating several groups of test values ​​for the operating parameters of the sand mixing equipment, and assigning a sand mixing equipment to each group of test values; setting the operating parameters of the sand mixing equipment according to the test values, and obtaining the effect coefficient at the end of the sand mixing process; and sending 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 values ​​or invalid values ​​by the effect coefficient; if all the 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 phone terminal of the administrator; 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;

[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 value of the motor surface temperature value and the bearing temperature value during the sand mixing process as the fault risk value, mark the stability demand value and the effect demand value according to the fault risk value and the effect coefficient; and send the stability 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, adhesive and additives are weighed and proportioned according to the sand mixing formula, and the proportioned raw materials are put into the sand mixing equipment. The operating parameters include the motor output speed, mixing time and heating temperature. The test value generation process is to randomly select a value within the value range of the corresponding operating parameter as the test value.

[0014] Furthermore, 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 value, retrieving the moisture content range of the sand, marking the average of the maximum value and the minimum value of the moisture content range of the sand as the moisture standard value, marking the absolute value of the difference between the moisture value and the moisture standard value as the moisture deviation value, and marking the ratio of the moisture deviation value to the moisture standard value as the moisture deviation coefficient; at the end of the sand mixing process, the sand surface is imaged to obtain a detection image, and the sand grain surface texture interference of the detection image is eliminated by Gaussian filtering, and then the energy coefficient of the detection image is extracted by grayscale co-occurrence matrix, and the difference between the energy coefficient and the moisture deviation coefficient is marked as the effect coefficient.

[0015] Furthermore, the specific process of marking the test value as a valid value or an invalid value includes: obtaining the effect threshold through the database, and 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 an invalid value; 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 a valid value.

[0016] Furthermore, the marking process of the stable required value includes: marking the valid value corresponding to the sand mixing process with the minimum fault risk value as the stable required value of the sand mixing formula;

[0017] The process of marking the effect requirement value includes: obtaining the fault risk threshold through the database, and comparing the fault risk values ​​of all sand mixing processes with the fault risk threshold one by one: if the fault risk value is less than the fault risk threshold, the corresponding sand mixing process is marked as a safe process; if the fault risk value is greater than or equal to the fault risk threshold, the corresponding sand mixing process is marked as a risky process; the effective value corresponding to the safe process with the largest effect coefficient is marked 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, and when a sand mixing task is received within the control cycle, retrieving the stability requirement value and the effect requirement value corresponding to the sand mixing formula, and the user selects the stability requirement value or the effect requirement value to set the operating parameters of the sand mixing equipment. When the user does not make a selection, the stability requirement value is preferentially used to set the operating parameters. After the 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.

[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, the expected deviation values ​​of all sand mixing processes are summed and averaged to obtain the expected deviation coefficient, the expected deviation threshold is obtained through the database, and the expected deviation coefficient is compared 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, a retest signal is generated and the retest signal is sent to the simulation test module.

[0020] Furthermore, the working method of the dynamic simulation control system of the sand mixing equipment based on artificial intelligence includes the following steps:

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

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

[0023] Step 3: Analyze the demand 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 be used to perform simulation test analysis on the sand mixing equipment, generate multiple sets of test values ​​for each sand mixing formula, set the operating parameters according to the test values, and calculate the effect coefficient of the sand mixing process, so as to independently match the 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, differentiate the test values ​​through 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 be used to analyze the demand of sand mixing equipment, generate stability 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 stability demand values ​​and effect demand values;

[0029] 4. The control execution module can be used to analyze the sand mixing effect, and the expected deviation coefficient can be statistically analyzed at the end of the control cycle. The overall sand mixing effect can be evaluated based on the expected deviation coefficient. When the overall sand mixing effect is abnormal, it can be retested in time, and the control basis can be dynamically adjusted according to the operation stage of the sand mixer. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

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

[0032] Figure 2 This is a flow chart of the method of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0033] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

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

[0035] The simulation test module is used to perform simulation test analysis on the sand mixing equipment: call a sand mixing formula, weigh and proportion the sand, adhesive and additive according to the sand mixing formula, put the proportioned raw materials into the sand mixing equipment, and generate several groups of test values ​​for the operating parameters of the sand mixing equipment. The operating parameters include motor output speed, mixing time and heating temperature. The test value generation process is to randomly select a value as the test value within the value range of the corresponding operating parameter, and assign a sand mixing equipment to each group of test values; set the operating parameters of the sand mixing equipment according to the test value, obtain the moisture content of the sand at the end of the sand mixing process and mark it as the moisture value, call the moisture content range of the sand, mark the average of the maximum and minimum values ​​of the sand moisture content range as the moisture standard value, mark the absolute value of the difference between the moisture value and the moisture standard value as the moisture deviation value, and mark the ratio of the moisture deviation value to the moisture standard value as the moisture deviation coefficient; take an image of the sand surface at the end of the sand mixing process A detection image is obtained, and the surface texture interference of the sand grains in the detection image is eliminated by Gaussian filtering. Gaussian filtering is a linear smoothing filter that is suitable for eliminating Gaussian noise and is widely used in the noise reduction process of image processing. Then, the energy coefficient (energy parameter of the image) of the detection image is extracted through the grayscale co-occurrence matrix. The grayscale co-occurrence matrix refers to a common method of describing texture by studying the spatial correlation characteristics of grayscale. The energy coefficient is used to reflect the uniformity of sand grain distribution. The higher the value of the energy coefficient (closer to 1), the more uniform the sand grain distribution; the difference between the energy coefficient and the water deviation coefficient is marked as the effect coefficient; after all test values ​​corresponding to the same sand mixing formula are tested and the effect coefficients are obtained, all the effect coefficients are sent to the test analysis module; multiple groups of test values ​​are generated for each sand mixing formula, and after the operating parameters are set according to the test values, the effect coefficient of the sand mixing process is calculated, so that the test values ​​are independently matched for different sand mixing formulas to ensure the sand mixing effects of different sand mixing formulas.

[0036] The test analysis module is used to process and analyze the test data of the sand mixing equipment: the effect threshold is obtained through the database, and the effect coefficient is compared 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 an invalid value; 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 a valid value; if all test values ​​corresponding to the same sand mixing formula are marked as invalid values, a formula optimization signal is generated and the formula optimization signal is sent to the mobile phone terminal of the administrator; 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 the demand analysis signal is sent to the demand analysis module; the test values ​​are differentiated by 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 to further ensure the sand mixing effect of the sand mixing equipment.

[0037] 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 the bearing temperature value during the sand mixing process as the fault risk value, and mark the valid value corresponding to the sand mixing process with the smallest fault risk value as the stable demand value of the sand mixing formula; obtain the fault risk threshold through the database, and compare the fault risk values ​​of all sand mixing processes with the fault risk threshold one by one: if the fault risk value is less than the fault risk threshold, the corresponding sand mixing process is marked as a safe process; if the fault risk value is greater than or equal to the fault risk threshold, the corresponding sand mixing process is marked as a risky process; mark the valid value corresponding to the safe process with the largest effect coefficient as the effect demand value of the sand mixing formula; send the stable demand value and effect demand value of all sand mixing formulas to the control execution module; generate the stable demand value and effect demand value from the perspective of user needs, and then control the equipment operation stability and sand mixing effect according to the stable demand value and effect demand value.

[0038] The control execution module is used to perform sand mixing control analysis on the sand mixing equipment: generate a control cycle, and when a sand mixing task is received within the control cycle, retrieve the stability requirement value and effect requirement value corresponding to the sand mixing formula, and the user selects the stability requirement value or the effect requirement value to set the operating parameters of the sand mixing equipment. If the user does not make a selection, the stability requirement value is given priority to set the operating parameters. After the sand mixing is completed, calculate the effect coefficient of this sand mixing process, and mark the difference between the expected effect coefficient and the effect coefficient of this sand mixing process as the expected deviation value (the expected effect coefficient is the effect coefficient of the stability requirement value or the effect requirement value during the test process). At the end of the control cycle, all sand mixing processes are evaluated. The expected deviation values ​​of the process are summed and averaged to obtain the expected deviation coefficient, the expected deviation threshold is obtained through the database, and the expected deviation coefficient is compared 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 period 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 period does not meet the requirements, and a retest signal is generated and sent to the simulation test module; at the end of the control period, the expected deviation coefficient is counted, and the overall sand mixing effect is evaluated according to the expected deviation coefficient. When the overall sand mixing effect is abnormal, a retest is carried out in time, and the control basis is dynamically adjusted according to the operation stage of the sand mixer.

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

[0040] Step 1: Conduct simulation test analysis on the sand mixing equipment: retrieve a sand mixing recipe, feed the prepared raw materials into the sand mixing equipment, and 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 ​​through the effect coefficient;

[0042] Step 3: Analyze the demand for sand mixing equipment: Obtain the failure risk value of the sand mixing process, and mark the stable demand value and effect demand value in the valid values ​​using the failure risk value and 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 stability requirement value and effect requirement value corresponding to the sand mixing formula. The user selects the stability requirement value or effect requirement value to set the operating parameters. After the setting is completed, automatic sand mixing is performed.

[0044] The dynamic simulation control system of sand mixing equipment based on artificial intelligence, when working, calls a sand mixing formula, puts the proportioned raw materials into the sand mixing equipment, and generates several groups of test values ​​for the operating parameters of the sand mixing equipment, sets the operating parameters of the sand mixing equipment according to the test values ​​and records the effect coefficient of the sand mixing process; marks the test value as invalid value or valid value through the effect coefficient; obtains the fault risk value of the sand mixing process, and marks the stability requirement value and effect requirement value in the valid value through the fault risk value and the effect coefficient; generates a control cycle, and when a sand mixing task is received within the control cycle, calls the stability requirement value and effect requirement value corresponding to the sand mixing formula, and the user selects the stability requirement value or the effect requirement value to set the operating parameters, and automatically performs sand mixing after the setting is completed.

[0045] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

[0046] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these 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 any one or more embodiments or examples.

[0047] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Dynamic simulation control system of sand mixing equipment based on artificial intelligence, characterized by: It includes a simulation test module, a test analysis module, a demand 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: calling a sand mixing formula, generating several groups of test values ​​for the operating parameters of the sand mixing equipment, and assigning a sand mixing equipment to each group of test values; setting the operating parameters of the sand mixing equipment according to the test values, and obtaining the effect coefficient at the end of the sand mixing process; and sending 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 values ​​or invalid values ​​according to the effect coefficient; if all the 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 phone terminal of the administrator; If a valid value exists 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 value of the motor surface temperature value and the bearing temperature value during the sand mixing process as the fault risk value, mark the stability demand value and the effect demand value according to the fault risk value and the effect coefficient; and send the stability 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.

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

3. The artificial intelligence-based dynamic simulation control system for sand mixing equipment according to claim 2 is characterized in that: 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 value, calling the moisture content range of the sand, marking the average of the maximum and minimum values ​​of the sand moisture content range as the moisture standard value, marking the absolute value of the difference between the moisture value and the moisture standard value as the moisture deviation value, and marking the ratio of the moisture deviation value to the moisture standard value as the moisture deviation coefficient; at the end of the sand mixing process, the sand surface is imaged to obtain a detection image, and the sand grain surface texture interference of the detection image is eliminated by Gaussian filtering, and then the energy coefficient of the detection image is extracted by gray level co-occurrence matrix, and the difference between the energy coefficient and the moisture deviation coefficient is marked as the effect coefficient.

4. The artificial intelligence-based dynamic simulation control system for sand mixing equipment according to claim 3 is characterized in that: The specific process of marking the test value as a valid value or an invalid value includes: obtaining the effect threshold through the database, and 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 an invalid value; 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 a valid value.

5. The artificial intelligence-based dynamic simulation control system for sand mixing equipment according to claim 4 is characterized in that: The marking process of the stable required value includes: marking the valid value corresponding to the sand mixing process with the minimum fault risk value as the stable required value of the sand mixing formula; The process of marking the effect requirement value includes: obtaining the fault risk threshold through the database, and comparing the fault risk values ​​of all sand mixing processes with the fault risk threshold one by one: if the fault risk value is less than the fault risk threshold, the corresponding sand mixing process is marked as a safe process; if the fault risk value is greater than or equal to the fault risk threshold, the corresponding sand mixing process is marked as a risky process; the effective value corresponding to the safe process with the largest effect coefficient is marked as the effect requirement value of the sand mixing formula.

6. The artificial intelligence-based dynamic simulation control system for sand mixing equipment according to claim 5 is 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, and when a sand mixing task is received within the control cycle, retrieving the stability requirement value and effect requirement value corresponding to the sand mixing formula, and the user selects the stability requirement value or the effect requirement value to set the operating parameters of the sand mixing equipment. When the user does not make a selection, the stability requirement value is used to set the operating parameters. After the 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.

7. The artificial intelligence-based dynamic simulation control system for sand mixing equipment according to claim 6 is 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, the expected deviation values ​​of all sand mixing processes are summed and averaged to obtain the expected deviation coefficient, the expected deviation threshold is obtained through the database, and the expected deviation coefficient is compared 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, a retest signal is generated and the retest signal is sent to the simulation test module.

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

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