Lithium iron phosphate high-flux preparation system and method based on artificial intelligence

Abstract

The invention provides a lithium iron phosphate high-flux preparation system based on artificial intelligence, which is characterized by comprising a synthesis module, a mobile robot, a process optimization module and a central control module, wherein the synthesis module comprises a raw material work station, a ball milling work station, a sintering work station and a detection work station; the mobile robot is used for completing material transfer among the workstations in the synthesis module; the process optimization module comprises a waiting time optimization unit; and the central control module is used for data storage and analysis, receiving feedback information of the detection work station and the process optimization module, and controlling the movement of the mobile robot.

Description

Technical Field

[0001] The present invention relates to the field of lithium iron phosphate positive electrode materials, and in particular to an artificial intelligence-based high-throughput lithium iron phosphate preparation system, and a method for preparing lithium iron phosphate using the system. Background Art

[0002] The research and development of new materials has long been in an inefficient mode of low throughput, trial and error, and labor-intensive, which seriously limits the research and development progress of new materials. In recent years, the rapid development of Internet big data, artificial intelligence (AI) and robots has provided an important historical opportunity for intelligent, high-throughput, automated and efficient research and development of new materials. Relying on different experimental platforms around the world, rapid research and development progress has been made in the fields of catalysts and chemical synthesis. In the field of energy storage materials, especially battery materials, machine learning technology has been widely used to predict and discover the properties of materials. In the past, researchers constantly tried different materials and processes, which was inefficient and difficult to meet the needs of the rapidly developing high-tech industry. Artificial intelligence has a strong high-speed and massive data processing capability. It is the most potential technology to break through the above research bottlenecks and has greatly promoted the development of battery material research, battery device design and manufacturing, and material and device characterization. The research and development and application of batteries generate a large amount of data every day. Artificial intelligence and machine learning can assist researchers in solving the parameter and data challenges of lithium-ion batteries and greatly promote the industrialization of large-scale and high-performance electrochemical energy devices.

[0003] Lithium iron phosphate is considered to be the most promising cathode material for power lithium-ion batteries due to its non-toxicity, environmental friendliness, abundant raw material sources, high specific capacity, and good cycle performance. The high-temperature solid-phase method is currently the most commonly used and mature method in the preparation process of lithium iron phosphate. The mixing of raw materials and high-temperature calcination in the preparation process are one of the main factors affecting the performance of the product. These two processes are also the steps that researchers have to wait for a long time. At present, the emergence of AI chemical robots has brought new hope to the research of battery materials, and its automation level has been continuously improved. However, chemical robots that do not have management and planning scheduling systems can only complete the tasks of each workstation step by step according to the sequential workstation process during chemical experiments, which leads to the phenomenon that the chemical robot waits for a long time in the experiment, increases the total duration of the chemical experiment, and makes the chemical robot slow and inefficient in the automated chemical experiment operation process. Summary of the invention

[0004] The purpose of the present invention is to solve at least one of the following technical problems:

[0005] (1) The traditional high-temperature solid-phase method for preparing lithium iron phosphate currently mainly uses manual intensive and high-intensity experiments, which have low throughput, poor targeting, and low efficiency. Most experiments are improved based on the experience of the experimenters, and the innovation process is slow;

[0006] (2) The prepared lithium iron phosphate products need to be manually sent for sample testing to test their compaction density, specific surface area, electrical properties, etc. A large amount of test result data still needs to be manually analyzed one by one, which requires a lot of effort;

[0007] (3) The sanding and sintering processes in the high-temperature solid-phase method take a long time, and conventional chemical robots have to wait for a long time in place, which reduces experimental efficiency.

[0008] To this end, the present invention provides a high-throughput preparation system for lithium iron phosphate based on artificial intelligence, comprising: a synthesis module, a mobile robot, a process optimization module, and a central control module, wherein:

[0009] The synthesis module includes: a raw material workstation, a ball milling workstation, a sintering workstation, and a testing workstation;

[0010] The mobile robot is used to complete the material transfer between the workstations in the synthesis module;

[0011] The process optimization module includes a waiting time optimization unit;

[0012] The central control module is used for data storage and analysis, receiving feedback information from the detection workstation and the process optimization module, and controlling the movement of the mobile robot.

[0013] In the specific implementation of the present application, the central control module is the central hub of the preparation system provided by the present invention, controls the operation of all modules, and is connected to other modules by communication. Specifically, the central control module is connected to the synthesis module, the mobile robot and the process optimization module respectively, the central control module sends the initial experimental plan to the process optimization module, the process optimization module then feeds back the process optimization plan to the central control module, the mobile robot completes the experimental operation according to the instructions of the central control module, and the central control module then obtains the optimal experimental plan based on the test data of the analysis and detection workstation.

[0014] In a specific implementation of the present application, the central control module can be connected to the Internet, and search for relevant documents based on Internet big data, extract and clean the document content based on natural language learning methods, and form a lithium iron phosphate material database.

[0015] Preferably, the central control module extracts key experimental preparation information from the lithium iron phosphate material database, and obtains an initial experimental plan based on an artificial intelligence algorithm.

[0016] In the present application, the central control module provides multiple initial experimental plans. The mobile robot must complete all the initial experimental plans. The central control module then selects the optimal experimental plan based on the test data. The waiting time optimization unit is used to optimize the waiting time of the mobile robot during the experimental operation, try to eliminate the waiting time, and speed up the experimental progress.

[0017] In the present application, the optimization method of the waiting time optimization unit needs to decompose the initial experimental plan into multiple sequential workstation processes, namely the sampling process of the raw material workstation, the ball milling process of the ball milling workstation, the low-temperature pre-sintering process and the high-temperature calcining process of the sintering workstation, and the detection process of the detection workstation. Except for the sampling process, the rest of the processes require a long waiting time. For example, the general sampling (weighing) time is about 0.5h, and ball milling, sintering and detection only require sample delivery and sampling, and the time for each sample delivery and sampling is about 10min.

[0018] In a specific implementation of the present application, the specific optimization steps of the waiting time optimization unit are:

[0019] Step 1: Number multiple initial experimental plans. The optimization plan with a smaller number has a higher priority.

[0020] Step 2: Decompose each initial experimental plan into the following five sequential workstation processes: sampling, ball milling, low-temperature pre-sintering, high-temperature calcining, and testing;

[0021] Step 3: Determine whether there is a next workstation process to be executed in the current initial experimental plan. If so, execute step 4. If not, it means that all workstation processes of the current initial experimental plan have been completed, and execute the next priority initial process plan;

[0022] Step 4: Execute the next workstation process to be executed in the current initial experimental plan, and determine whether the execution task is a loading task. If so, execute step 5; otherwise, it indicates that the execution task is a taking-out task, and execute step 3;

[0023] Step 5: Determine the waiting time of the workstation process to be executed in the current initial experimental plan, and the execution task time of the workstation process to be executed in the next priority initial experimental plan. If the waiting time is long, execute the workstation process to be executed in the next priority initial experimental plan, otherwise, wait in place;

[0024] Step 6: After each workstation process operation is completed, repeat steps 3 to 5;

[0025] Step 7: When all workstation processes to be executed in the initial experimental plan have been completed, stop the optimization process.

[0026] In step 1, the numbering rule is random, as long as all the initial process plans are completed.

[0027] During the experimental operation of the mobile robot, there will be a lot of waiting time, especially for the ball milling workstation and the sintering workstation. The traditional mobile robot has to wait until the process of the previous workstation is completed before conducting the experiment of the next workstation. The waiting time optimization module in this application is set up to solve this problem. After this optimization process, the waiting time of the experiment can be greatly shortened and the experimental efficiency can be improved.

[0028] In the present application, the process optimization module further includes a preliminary optimization unit, which optimizes the initial experimental plan before the waiting time optimization unit to obtain a preliminary process optimization plan. The waiting time optimization unit then optimizes the waiting time of the preliminary process optimization plan to obtain a process optimization plan.

[0029] Preferably, the preliminary optimization unit uses a Bayesian optimization algorithm to optimize the design of the initial experimental plan.

[0030] More preferably, the Bayesian optimization algorithm is specifically:

[0031] The test result data of the sample is taken as y, and the corresponding experimental parameters are taken as X. Different samples correspond to different y and X, thus forming a sample data set. Then, through the machine learning model, the objective function y=f(X) is established;

[0032] Randomly select sample points (X, y), use Gaussian process regression to calculate the posterior distribution of the sample points, and if the termination condition is met, output the optimal X value, that is, the optimal experimental parameters;

[0033] If the termination condition is not met, the acquisition function is used to select the next iteration point (X i ,y i ), the acquisition function can be the UCB function, EI function or PI function, and Gaussian process regression is performed again until the optimal X is output.

[0034] In the present application, the test result data y includes compaction density, specific surface area and particle size D50.

[0035] In the present application, the experimental parameters X include the type and amount of raw materials, ball milling time and rotation speed, and sintering temperature and time.

[0036] In the present application, the data of the sample data set is derived from a lithium iron phosphate material database.

[0037] In the above Bayesian optimization algorithm, the termination condition is that the compaction density is ≥ 2.0 g / cm 3Specific surface area is 10-20m 2 / g, and the particle size D50 is between 0.8-2.0μm.

[0038] In the traditional experimental process, the optimization and adjustment of the experimental plan mainly depends on the technical level of the experimenter, and the level of the experimenter's technical level seriously affects the progress of the experiment. To address this problem, the central control module proposed in this application has the key information of the experimental preparation in this field, and obtains the preliminary process optimization plan through the preliminary optimization unit, thereby avoiding the shortcoming of completely relying on the technical level of the experimenter to optimize the experimental plan. At the same time, the traditional experimental process relies entirely on manual operation, and the innovation process is slow. To address this problem, this application proposes a waiting time optimization unit, which can not only realize unmanned laboratories and reduce labor costs, but also the chemical robot can meet the needs of 24h operation and achieve the purpose of high-throughput preparation.

[0039] In a specific embodiment of the present application, the workflow of the lithium iron phosphate high-throughput preparation system is as follows:

[0040] S1. The central control module extracts key experimental preparation information from the lithium iron phosphate material database and obtains the initial experimental plan based on the artificial intelligence algorithm;

[0041] S2. The central control module sends the initial experimental plan to the process optimization module, which optimizes the waiting time and obtains the process optimization plan;

[0042] S3, the central control module sends instructions to the mobile robot to carry out high-throughput experiments according to the process optimization plan;

[0043] S4. After the experiment is completed, the central control module selects the optimal experimental plan based on the test data of the detection workstation.

[0044] Preferably, the workflow of the lithium iron phosphate high-throughput preparation system is:

[0045] S1. The central control module extracts key experimental preparation information from the lithium iron phosphate material database and obtains the initial experimental plan based on the artificial intelligence algorithm;

[0046] S2-1, the central control module sends the initial experimental plan to the process optimization module, and the process optimization module uses the Bayesian optimization algorithm to optimize the initial experimental plan and obtain a preliminary process optimization plan;

[0047] S2-2, the waiting time optimization unit optimizes the waiting time of the preliminary process optimization plan to obtain a process optimization plan;

[0048] S3, the central control module sends instructions to the mobile robot to carry out high-throughput experiments according to the process optimization plan;

[0049] S4. After the experiment is completed, the central control module selects the optimal experimental plan based on the test data of the detection workstation.

[0050] In a specific embodiment of the present application, the raw materials of the raw material workstation include: an iron source, a phosphorus source and a lithium source, wherein:

[0051] The iron source is selected from at least one of ferrous oxalate FeC2O4·2H2O, ferrous acetate, and ferric phosphate;

[0052] The phosphorus source is selected from at least one of diammonium hydrogen phosphate (NH4)2HPO4, ammonium dihydrogen phosphate, and iron phosphate;

[0053] The lithium source is selected from at least one of lithium carbonate Li2CO3, lithium hydroxide and lithium acetate.

[0054] In a specific embodiment of the present application, the amount of raw materials is controlled by controlling the atomic ratio of lithium (Li), iron (Fe), and phosphorus (P) in the iron source, phosphorus source, and lithium source, for example, Li:Fe:P=(0.95-1.05):1:1.

[0055] In a specific embodiment of the present application, the specific process of the raw material workstation includes: the mobile robot receives instructions from the central control module, weighs the iron source, phosphorus source, and lithium source respectively according to the proportion, and then transfers the weighed raw materials to the ball mill workstation.

[0056] In the specific implementation of the present application, the specific process of the ball milling workstation includes: the mobile robot receives the instruction of the central control module, loads the weighed raw materials into the ball mill, the central control module sets the ball milling time and speed, and after the ball milling is completed, the mobile robot takes out the sample and transports it to the sintering workstation. For example, the ball milling time is set to 6-12h, and the speed is set to 400-600r / min, so that the raw materials are mixed evenly and refined.

[0057] The ball milling workstation contains multiple ball mills, which can realize the simultaneous ball milling of multiple samples, thereby saving waiting time and speeding up the experiment. The ball milling time and speed are controlled by the central control module, and the mobile robot only needs to load and remove samples from the ball mill.

[0058] In the specific implementation of the present application, the specific process of the sintering workstation includes: the mobile robot receives the instructions of the central control module, loads the ball-milled samples into the sintering furnace, the central control module sets the sintering program, and after the sintering is completed, the mobile robot takes out the samples and transports them to the detection workstation.

[0059] Preferably, the mobile robot takes the sample out of the sintering furnace, grinds the sample, and then transports the sample to the testing workstation.

[0060] The sintering workstation contains multiple box furnaces, which can realize the simultaneous sintering of multiple samples, thereby saving waiting time and speeding up the experiment. The atmosphere of the box furnace is nitrogen atmosphere. According to the instructions issued by the central control module, the mobile robot loads the ball-milled samples into the sintering furnace and takes out the samples from the sintering furnace.

[0061] The sintering procedure may be: pre-sintering at 300-350°C for 5-10h under an inert atmosphere, cooling to room temperature, and then sintering at 600-800°C for 10-20h.

[0062] In a specific implementation of the present application, the specific process of the detection workstation includes: the mobile robot receives instructions from the central control module, performs performance testing on the sintered samples, and feeds back the test results to the central control module.

[0063] The testing workstation includes a powder compaction density meter, a specific surface area tester, and a particle size analyzer. The mobile robot transfers the sintered, cooled, and ground samples to the testing workstation according to the instructions issued by the central control module. The testing workstation tests the compaction density, specific surface area, and particle size D50 of the samples, and feeds back the test data to the central control module.

[0064] In a specific embodiment of the present application, the chemical operation of the mobile robot is performed by a six-degree-of-freedom robotic arm equipped with a gripper with gripping feedback control, a depth camera and a laser sensor, and a visually guided intelligent detection and positioning system. In order to meet the high positioning accuracy requirements of the gripper in the experimental operation, each experimental workstation is affixed with an independent QR code label. The robot can identify the current workstation by visually identifying the QR code label in the field of view, and accurately measure the posture of the workstation based on the relative posture between the QR code label and the target to be operated on the experimental workstation.

[0065] To ensure precise and interactive chemical operations, the mobile robot is equipped with an integrated mapping and positioning system based on dual lidars to obtain its positioning information and laboratory map, thereby realizing navigation and autonomous obstacle avoidance functions.

[0066] In a second aspect, the present invention provides a high-throughput preparation method of lithium iron phosphate based on artificial intelligence, which is prepared using the above-mentioned preparation system, and the preparation method comprises:

[0067] (1) The iron source, phosphorus source and lithium source are placed in the raw material workstation, and the central control module sends the initial experimental plan to the process optimization module for waiting time optimization, and then feeds back the process optimization plan to the central control module;

[0068] (2) The mobile robot receives instructions from the central control module and conducts high-throughput experiments;

[0069] (3) The mobile robot weighs the raw materials at the raw material workstation in the synthesis module;

[0070] (4) After the raw materials are weighed, the mobile robot loads the raw materials into the ball mill, and the central control module controls the speed and time of the ball mill;

[0071] (5) After the ball milling is completed, the mobile robot takes the sample out of the ball mill and puts it into the sintering furnace. The central control module controls the sintering temperature and time of the sintering furnace;

[0072] (6) After sintering, the mobile robot takes the sample out of the sintering furnace and finally sends it to the testing workstation;

[0073] (7) When the mobile robot completes all the initial experimental plans, the central control module selects the optimal experimental plan based on the test data of the detection workstation;

[0074] Steps (3) to (6) are repeated according to the number of initial experimental schemes, and the order of steps (3) to (6) is optimized by the process optimization module.

[0075] In a specific embodiment of the present application, the parameters of the initial experimental scheme in step (1) include: the type and amount of raw materials, the ball milling speed and time, and the sintering time and temperature.

[0076] Beneficial effects:

[0077] The system of the present invention can realize an unmanned laboratory, reduce labor costs, and the chemical robot can meet the needs of 24-hour operation to achieve the purpose of high-throughput preparation;

[0078] The system of the present invention improves the design of the traditional optimization module, and adopts a waiting time optimization module in view of the problem of long waiting time in the preparation process of lithium iron phosphate, which greatly reduces the waiting time of the chemical robot in place and further improves the experimental efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A schematic diagram of the structure of a high-throughput lithium iron phosphate preparation system provided by the present invention;

[0080] Figure 2 A schematic diagram of the composition of the synthesis module provided by the present invention;

[0081] Figure 3 The flowchart of the Bayesian optimization algorithm used in the present invention;

[0082] Figure 4Schematic diagram of the waiting time of the experimental process of the traditional mobile robot;

[0083] Figure 5 A schematic diagram of the waiting time of the experimental process of the mobile robot provided in this application;

[0084] Figure 6 A schematic diagram of the workflow of a high-throughput lithium iron phosphate preparation system provided in this application;

[0085] Figure 7 A schematic diagram of the workflow of another high-throughput lithium iron phosphate preparation system provided in this application;

[0086] Figure 8 A schematic diagram of the process flow of the waiting time optimization unit provided in this application. DETAILED DESCRIPTION

[0087] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0088] The endpoints and any values ​​of the ranges disclosed in this article are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoint values ​​of each range, the endpoint values ​​of each range and the individual point values, and the individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed in this article.

[0089] In the description of the present invention, it should be noted that the terms "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0090] The present invention is further described in detail below through specific embodiments.

[0091] Example 1

[0092] This embodiment provides a high-throughput preparation system for lithium iron phosphate based on artificial intelligence, including: a synthesis module, a mobile robot, a process optimization module, and a central control module, wherein:

[0093] The synthesis module includes: a raw material workstation, a ball milling workstation, a sintering workstation, and a testing workstation;

[0094] The mobile robot is used to complete the material transfer between the workstations in the synthesis module;

[0095] The process optimization module includes a waiting time optimization unit;

[0096] The central control module is used for data storage and analysis, receiving feedback information from the detection workstation and the process optimization module, and controlling the movement of the mobile robot; the central control module is connected to the synthesis module, the mobile robot and the process optimization module respectively, the central control module sends the initial experimental plan to the process optimization module, the process optimization module then feeds back the process optimization plan to the central control module, the mobile robot completes the experimental operation according to the instructions of the central control module, and the central control module then derives the optimal experimental plan based on the test data of the analysis detection workstation.

[0097] This embodiment provides a method for preparing lithium iron phosphate using the above preparation system, comprising:

[0098] (1) Place the iron source, phosphorus source and lithium source into the raw material workstation, and the central control module provides two initial experimental plans:

[0099] Initial Experimental Plan 1 (Sample No. 1):

[0100] The raw materials are 73.89g lithium carbonate and 271.8g iron phosphate; the ball mill speed is 500r / min, and the ball milling time is 8h; the sintering procedure is to preheat at a low temperature of 350℃ for 6h, and then calcine at a high temperature of 650℃ for 12h after cooling.

[0101] Initial Experimental Plan 2 (Sample No. 2):

[0102] The raw materials are 73.89g lithium carbonate and 271.8g iron phosphate; the ball mill speed is 600r / min, and the ball milling time is 8h; the sintering procedure is to preheat at a low temperature of 350℃ for 6h, and then calcine at a high temperature of 700℃ for 15h after cooling.

[0103] (2) The mobile robot receives the instruction from the central control module, completes the process optimization plan given by the process optimization module, and starts weighing the raw materials of sample No. 1 at the raw material workstation in the synthesis module;

[0104] (3) After the raw materials of sample No. 1 are weighed, the mobile robot loads the raw materials into the ball mill, and the central control module controls the speed and time of the ball mill;

[0105] (4) After the ball mill starts milling, the mobile robot receives instructions from the central control module and returns to the raw material workstation to weigh the raw materials of sample No. 2;

[0106] (5) After the raw materials of sample No. 2 are weighed, the mobile robot loads the raw materials into the ball mill, and the central control module controls the speed and time of the ball mill;

[0107] (6) After the ball milling of sample No. 1 is completed, the mobile robot takes sample No. 1 out of the ball mill and puts it into the sintering furnace. The central control module controls the sintering temperature and time of the sintering furnace;

[0108] (7) After the ball milling of sample No. 2 is completed, the mobile robot takes sample No. 2 out of the ball mill and puts it into the sintering furnace. The central control module controls the sintering temperature and time of the sintering furnace;

[0109] (8) After the sintering of sample No. 1 is completed, the mobile robot takes the sample out of the sintering furnace and finally sends it to the testing workstation;

[0110] (9) After the sintering of sample No. 2 is completed, the mobile robot takes the sample out of the sintering furnace and finally sends it to the testing workstation;

[0111] (10) The central control module obtains the optimal experimental plan based on the test data of the two samples, which is the initial experimental plan 2.

[0112] like Figure 4 As shown in the figure, the traditional mobile robot, which has not undergone process optimization design, can only prepare the next sample after the previous sample is prepared. When the sample is ball milled and sintered, the mobile robot can only wait in place. Generally, the sampling (weighing) time is about 0.5h, the ball milling time is about 6h, the low-temperature pre-burning time is about 6h, the high-temperature calcination time is about 12h, and the detection time is about 3h. In this way, if two samples are prepared, the waiting time in place is about 54h.

[0113] like Figure 5 As shown, after the process optimization module is optimized, when sample No. 1 is being ball-milled, the mobile robot will weigh sample No. 2 and transfer it to another ball mill for ball milling. After the ball milling of sample No. 1 is completed, it will be taken out and transferred to low-temperature pre-burning. During the waiting stage of low-temperature pre-burning, sample No. 2 will be taken out from the ball mill. In this way, if two samples are prepared, the waiting time required in situ is about 25 hours. Due to the long waiting time in this project, if the initial experimental plan is large and the number of samples increases, the advantages of the technical solution provided by this application will be more obvious.

[0114] The specific embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0115] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0116] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A high-throughput preparation system for lithium iron phosphate based on artificial intelligence, characterized in that: include: synthesis module, mobile robot, process optimization module and central control module; Wherein, the synthesis module includes: a raw material workstation, a ball milling workstation, a sintering workstation, and a testing workstation; The mobile robot is used to complete the material transfer between the workstations in the synthesis module; The process optimization module includes a waiting time optimization unit; The central control module is used for data storage and analysis, receiving feedback information from the detection workstation and the process optimization module, and controlling the movement of the mobile robot.

2. The preparation system according to claim 1, characterized in that: The central control module is connected to the synthesis module, the mobile robot and the process optimization module respectively. The central control module sends the initial experimental plan to the process optimization module, and the process optimization module then feeds back the process optimization plan to the central control module. The mobile robot completes the experimental operation according to the instructions of the central control module, and the central control module then obtains the optimal experimental plan based on the test data of the analysis and detection workstation.

3. The preparation system according to claim 1, characterized in that: The central control module searches for relevant documents based on Internet big data, extracts and cleans the document content based on a natural language learning method, and forms a lithium iron phosphate material database.

4. The preparation system according to claim 3, characterized in that: The central control module extracts key experimental preparation information from the lithium iron phosphate material database and obtains an initial experimental plan based on an artificial intelligence algorithm.

5. The preparation system according to claim 1, characterized in that: The specific optimization steps of the waiting time optimization unit are: Step 1: Number multiple initial experimental plans. The optimization plan with a smaller number has a higher priority. Step 2: Decompose each initial experimental plan into the following five sequential workstation processes: sampling, ball milling, low-temperature pre-sintering, high-temperature calcining, and testing; Step 3: Determine whether there is a next workstation process to be executed in the current initial experimental plan. If so, execute step 4. If not, it means that all workstation processes of the current initial experimental plan have been completed, and execute the next priority initial process plan; Step 4: Execute the next workstation process to be executed in the current initial experimental plan, and determine whether the execution task is a loading task. If so, execute step 5; otherwise, it indicates that the execution task is a taking-out task, and execute step 3; Step 5: Determine the waiting time of the workstation process to be executed in the current initial experimental plan, and the execution task time of the workstation process to be executed in the next priority initial experimental plan. If the waiting time is long, execute the workstation process to be executed in the next priority initial experimental plan, otherwise, wait in place; Step 6: After each workstation process operation is completed, repeat steps 3 to 5; Step 7: When all workstation processes to be executed in the initial experimental plan have been completed, stop the optimization process.

6. The preparation system according to claim 1, characterized in that: The process optimization module further includes a preliminary optimization unit, which optimizes the initial experimental plan before the waiting time optimization unit to obtain a preliminary process optimization plan. The waiting time optimization unit then optimizes the waiting time of the preliminary process optimization plan to obtain a process optimization plan.

7. The preparation system according to claim 1, characterized in that: The raw materials of the raw material workstation include: iron source, phosphorus source and lithium source, wherein: The iron source is selected from at least one of ferrous oxalate FeC2O4·2H2O, ferrous acetate, and ferric phosphate; The phosphorus source is selected from at least one of diammonium hydrogen phosphate (NH4)2HPO4, ammonium dihydrogen phosphate, and iron phosphate; The lithium source is selected from at least one of lithium carbonate Li2CO3, lithium hydroxide and lithium acetate.

8. The preparation system according to claim 7, characterized in that: The specific process of the raw material workstation includes: the mobile robot receives the instruction of the central control module, weighs the iron source, phosphorus source and lithium source respectively according to the proportion, and then transfers the weighed raw materials to the ball mill workstation.

9. The preparation system according to claim 1, characterized in that: The specific process of the ball milling workstation includes: the mobile robot receives the instruction of the central control module, loads the weighed raw materials into the ball mill, the central control module sets the ball milling time and speed, and after the ball milling is completed, the mobile robot takes out the sample and transports it to the sintering workstation.

10. The preparation system according to claim 1, characterized in that: The specific process of the sintering workstation includes: the mobile robot receives the instruction of the central control module, loads the ball-milled sample into the sintering furnace, the central control module sets the sintering program, and after the sintering is completed, the mobile robot takes out the sample and transports it to the detection workstation.

11. The preparation system according to claim 1, characterized in that: The specific process of the testing workstation includes: the mobile robot receives the instruction of the central control module, performs performance testing on the sintered sample, and feeds back the test data to the central control module; Preferably, the performance tests include compacted density, specific surface area and particle size D50.

12. A high-throughput preparation method for lithium iron phosphate based on artificial intelligence, using the preparation system according to any one of claims 1 to 11 for preparation, characterized in that: The preparation method comprises: (1) The iron source, phosphorus source and lithium source are placed in the raw material workstation, and the central control module sends the initial experimental plan to the process optimization module for waiting time optimization, and then feeds back the process optimization plan to the central control module; (2) The mobile robot receives instructions from the central control module and conducts high-throughput experiments; (3) The mobile robot weighs the raw materials at the raw material workstation in the synthesis module; (4) After the raw materials are weighed, the mobile robot loads the raw materials into the ball mill, and the central control module controls the speed and time of the ball mill; (5) After the ball milling is completed, the mobile robot takes the sample out of the ball mill and puts it into the sintering furnace. The central control module controls the sintering temperature and time of the sintering furnace; (6) After sintering, the mobile robot takes the sample out of the sintering furnace and finally sends it to the testing workstation; (7) When the mobile robot completes all experimental operations, the central control module selects the optimal experimental plan based on the test data of the detection workstation; Steps (3) to (6) are repeated according to the number of initial experimental schemes, and the order of steps (3) to (6) is optimized by the process optimization module.

Images