Manufacturing Factory Logistics Control Method and System Based on Digital Twin Platform

Abstract

The present invention relates to the technical field of data processing, and particularly to a manufacturing factory logistics control method and system based on a digital twin platform. The method includes the steps of: collecting each temperature data sequence, obtaining the noise performance degree of each temperature data in each temperature data sequence, correcting the noise performance degree of each temperature data in each temperature data sequence according to the correlation between the temperature data sequences, obtaining the corrected noise performance degree of each temperature data in each temperature data sequence, filtering the temperature data in each data set of each temperature data sequence according to the differential order of each data set of each temperature data sequence to obtain each filtered temperature data sequence, and identifying abnormal conditions of the transportation container based on each filtered temperature data sequence. The present invention improves the filtering effect.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for controlling the logistics of a manufacturing factory based on a digital twin platform. Background Art

[0002] Digital twin is to build a model in the digital world that is exactly the same as the physical object in the real world, and through digital means, dynamically simulate, monitor, analyze, and control the physical object. The logistics control of a manufacturing factory based on a digital twin platform is a visualization management method for the factory logistics after building a model for the manufacturing factory. Among them, the digital twin platform of the manufacturing factory needs to provide the visibility of data such as the temperature of the transportation container to ensure the safety of the transported items. Then, it is necessary to collect temperature data and other data of the transportation container in real time for real-time monitoring.

[0003] At present, the patent document with the authorized announcement number of CN110188315B proposes a data processing method for clock taming, including: obtaining the best straight-line equation model of the observed data according to the random sample consensus algorithm; calculating the straight-line distance from each observed data to the best straight-line equation model; assigning corresponding weight coefficients to each observed data according to the straight-line distance corresponding to each observed data; using the weight coefficients corresponding to each observed data as the weights of the observed data in the Vondrak filtering method, and using the Vondrak filtering method to filter the observed data to obtain a filtering curve about the observed data.

[0004] Since the road conditions of the transportation container during transportation are different, different road conditions may have different impacts on the temperature sensor. Then, the degree of noise influence on the temperature data under different road conditions may be different. In the Vondrak filtering algorithm, the difference order is mainly used to describe the smoothness of the data. If a fixed difference order is used, it may not be possible to adjust according to the actual situation of the temperature data and cannot meet the smoothness requirements of the temperature data under different road conditions, resulting in problems of over-filtering or under-filtering of the temperature data. Summary of the Invention

[0005] In order to solve the technical problem that when the Vondrak filtering algorithm uses a fixed difference order to filter the temperature data, it cannot meet the smoothness requirements of the temperature data under different road conditions, resulting in over-filtering or under-filtering of the temperature data, the present invention provides a method and system for controlling the logistics of a manufacturing factory based on a digital twin platform.

[0006] In the first aspect, the present invention provides a method for controlling the logistics of a manufacturing factory based on a digital twin platform, adopting the following technical solution:

[0007] The method for controlling the logistics of a manufacturing factory based on a digital twin platform includes the steps:

[0008] Collect each temperature data sequence; Denote any temperature data in any temperature data sequence as the current temperature data, and denote the temperature sensor that collects the current temperature data as the current temperature sensor;

[0009] Obtain the noise performance degree of the current temperature data after correction ; represents the noise performance degree of the current temperature data; J represents the number of temperature sensors other than the current temperature sensor; represents the distance between the current temperature sensor and the j-th temperature sensor other than the current temperature sensor; represents the Pearson correlation coefficient between the temperature data sequence collected by the current temperature sensor and the temperature data sequence collected by the j-th temperature sensor other than the current temperature sensor; norm() represents the normalization function; exp() represents the exponential function with the natural constant as the base;

[0010] Divide each temperature data sequence into several data sets; Obtain the difference order of each data set of each temperature data sequence, and the difference order is positively correlated with the mean value of the noise performance degree of all temperature data after correction in each data set of each temperature data sequence; Based on the difference order, perform filtering processing on each temperature data sequence, and identify abnormal conditions of the transportation container.

[0011] The innovation of the present invention lies in analyzing the change characteristics of the temperature data collected during the operation of the transportation container, obtaining the noise performance degree of the temperature data, and correcting the noise performance degree of the temperature data according to the correlation between temperature data sequences to obtain the noise performance degree of the temperature data after correction. Thus, the difference order in the Vondrak filter is adapted according to the corrected noise performance degree, and the difference order can be adaptively adjusted according to the actual situation of the temperature data, so as to provide the best smoothing effect, help to adapt to the smoothing requirements of temperature data in different situations, improve the generalization ability of the algorithm, effectively avoid the problems of inability to adapt to temperature data changes, over-filtering and under-filtering caused by a fixed difference order, and further improve the efficiency of logistics control in manufacturing factories.

[0012] Preferably, the obtaining of the noise performance degree of the current temperature data includes:

[0013] Obtain the slope and fitting value of each surrounding data of the current temperature data;

[0014] ;

[0015] In the formula, represents the noise performance degree of the current temperature data; The slope variance of all surrounding data representing the current temperature data; The number of surrounding data of the current temperature data; The value of the i-th surrounding data of the current temperature data; The fitted value of the i-th surrounding data of the current temperature data; || represents the absolute value symbol; tanh() represents the normalization function.

[0016] It is convenient to adaptively obtain the difference order according to the noise performance degree subsequently, improving the filtering effect.

[0017] Preferably, the obtaining of the slope and the fitted value of each surrounding data of the current temperature data includes:

[0018] Denote the previous m temperature data and the subsequent m temperature data of the current temperature data in its belonging temperature data sequence as the surrounding data of the current temperature data; Use the least squares method to fit the surrounding data of the current temperature data to obtain the fitted curve of the current temperature data and the fitted value of each surrounding data of the current temperature data;

[0019] Obtain the slope of each surrounding data of the current temperature data in the fitted curve of the current temperature data, denoted as the slope of each surrounding data of the current temperature data.

[0020] Preferably, the dividing of each temperature data sequence into several data sets includes:

[0021] Preset the number M of data in the data set, and divide each temperature data sequence into several data sets in sequence according to the number of data in the data set.

[0022] It is convenient to adaptively obtain the difference order of each data set of each temperature data sequence subsequently.

[0023] Preferably, the obtaining of the difference order of each data set of each temperature data sequence includes:

[0024] ;

[0025] In the formula, Represents the difference order of the b-th data set of the a-th temperature data sequence; Represents the reference difference order, Represents the mean value of the corrected noise performance degree of all temperature data in the b-th data set of the a-th temperature data sequence.

[0026] The obtained difference order of each data set of each temperature data sequence is more accurate.

[0027] Preferably, filtering each temperature data sequence based on the difference order and identifying abnormal conditions of the transportation container, including:

[0028] Using the Vondrak filtering algorithm, and filtering the temperature data in each data set of each temperature data sequence according to the difference order of each data set of each temperature data sequence to obtain each filtered temperature data sequence; denoting the multiple temperature data corresponding to any sampling moment in all the filtered temperature data sequences as the temperature data group at this sampling moment, if the data mean value in the temperature data group at this sampling moment exceeds the preset temperature range, the transportation container has an abnormality at this sampling moment.

[0029] Adapting the difference order in the Vondrak filtering according to the noise performance degree after the temperature data is corrected, so as to provide the best smoothing effect.

[0030] Preferably, collecting several temperature data sequences, including:

[0031] The preset sampling moment is 2 seconds / time, and a total of one hour is collected. Install multiple temperature sensors at equal intervals in the transportation container. During the operation of the transportation container, collect the temperature data of each temperature sensor, and sort the temperature data of each temperature sensor in turn to obtain several temperature data sequences.

[0032] In a second aspect, the present invention provides a manufacturing factory logistics control system based on a digital twin platform, adopting the following technical solution:

[0033] A manufacturing factory logistics control system based on a digital twin platform includes: a processor and a memory, and the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned manufacturing factory logistics control method based on the digital twin platform is realized.

[0034] By adopting the above technical solution, generating a computer program for the above-mentioned manufacturing factory logistics control method based on the digital twin platform and storing it in the memory, so as to be loaded and executed by the processor, and thus manufacturing a terminal device according to the memory and the processor, which is convenient to use.

[0035] The present invention has the following technical effects: The object of the present invention is to analyze the change characteristics of the temperature data collected during the operation of the transportation container, obtain the noise performance degree of the temperature data, and correct the noise performance degree of the temperature data according to the correlation between the temperature data sequences, so as to obtain the noise performance degree of the temperature data after correction. Then, according to the corrected noise performance degree, the differential order in the Vondrak filtering is adapted, and the differential order can be adaptively adjusted according to the actual situation of the temperature data, so as to provide the best smoothing effect, which helps to adapt to the smoothing requirements of temperature data in different situations, improve the generalization ability of the algorithm, effectively avoid the problems of inability to adapt to the change of temperature data, over-filtering and under-filtering caused by the fixed differential order, and further improve the efficiency of the logistics control of the manufacturing factory. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown by way of example and not limitation, and the same or corresponding reference numerals represent the same or corresponding parts.

[0037] Figure 1 It is a flowchart of the method in the method for logistics control of a manufacturing factory based on a digital twin platform according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] It should be understood that when the claims, specifications and drawings of the present invention use terms such as "first" and "second", they are only used to distinguish different objects and not to describe a specific order. The terms "including" and "comprising" used in the specifications and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0040] An embodiment of the present invention discloses a method for logistics control of a manufacturing factory based on a digital twin platform. Referring to Figure 1 , it includes steps S1 - S3:

[0041] S1: Collect each temperature data sequence.

[0042] It should be noted that, in order to ensure the safety and reliability of the transported items (electric cores, battery packs) in the transport container, the present invention needs to collect the temperature data in the transport container. The temperature data on the electric cores and battery packs can be collected to determine whether there is a sudden temperature change in the electric core or battery pack, so as to ensure the safety during the transportation process.

[0043] In the implementation of the present invention, the preset sampling time is 2 seconds per time, and the sampling is carried out for one hour in total. Temperature sensors are installed on each transported item in the transport container. During the operation of the transport container, the temperature data of each temperature sensor is collected, and the temperature data of each temperature sensor is sorted in turn to obtain each temperature data sequence.

[0044] S2: Obtain the noise performance degree of each temperature data in each temperature data sequence.

[0045] It should be noted that due to the different road conditions during the transportation of the transport container, different road conditions may have different impacts on the temperature sensor. Then, the degree of noise influence on the temperature data under different road conditions may be different. In the Vondrak filtering algorithm, the difference order is mainly used to describe the smoothness of the data. If a fixed difference order is used, it may not be possible to adjust according to the actual situation of the temperature data and cannot meet the smoothness requirements of the temperature data under different road conditions, resulting in problems of over-filtering or under-filtering of the temperature data. Therefore, the present invention proposes a manufacturing factory logistics control method and system based on a digital twin platform. By analyzing the change characteristics of the temperature data during the transportation of the transport container, the noise performance degree of the temperature data is obtained, so as to adaptively adjust the difference order in the Vondrak filtering according to the noise performance degree, avoid the problems of over-filtering or under-filtering caused by using a fixed difference order, improve the filtering effect of the temperature data, and further improve the monitoring efficiency of the temperature data of the transport container and the logistics control efficiency of the manufacturing factory.

[0046] It should be further noted that due to the road conditions during the transportation of the transport container, the vehicle may be jolted, which will cause noise when the temperature sensor collects temperature data. Such noise is manifested as the violent jitter of the temperature data among the surrounding data, showing irregular fluctuations. Therefore, the noise performance degree of the temperature data can be obtained by using the regularity of the temperature data among its surrounding data; if the change trends of the surrounding data of the temperature data are more consistent, it indicates that the regularity of the temperature data among its surrounding data is better and the influence of noise interference is lower. Therefore, the surrounding data of the temperature data is obtained, and the noise performance degree of the temperature data is obtained according to the surrounding data of the temperature data.

[0047] In the embodiment of the present invention, the number of neighborhood data m is preset, any temperature data in any temperature data sequence is recorded as current temperature data, and the m temperature data before and after the current temperature data in the temperature data sequence to which it belongs are recorded as surrounding data of the current temperature data; the surrounding data of the current temperature data are fitted using the least squares method to obtain a fitting curve of the current temperature data and a fitting value of each surrounding data of the current temperature data;

[0048] Obtaining the slope of each surrounding data of the current temperature data in the fitting curve of the current temperature data, and recording it as the slope of each surrounding data of the current temperature data;

[0049] In the embodiment of the present invention, the preset number of neighborhood data m=5. In other embodiments, the implementer may preset the value of the number of neighborhood data according to specific implementation conditions.

[0050] Get the noise level of the current temperature data:

[0051] ;

[0052] In the formula, Represents the noise level of the current temperature data; Represents the slope variance of all surrounding data of the current temperature data; Represents the number of surrounding data of the current temperature data; Represents the value of the i-th surrounding data of the current temperature data; Represents the fitting value of the i-th surrounding data of the current temperature data; || represents the absolute value symbol; tanh() represents the normalization function; Represents the mean difference between the actual value and the fitted value of all surrounding data of the current temperature data. The larger the value, the greater the noise performance of the current temperature data; The larger the value is, the more inconsistent the change trend of the surrounding data of the current temperature data is, and the greater the noise performance of the current temperature data is.

[0053] S3: According to the correlation between the temperature data sequences, the noise performance degree of each temperature data in each temperature data sequence is corrected to obtain the corrected noise performance degree of each temperature data in each temperature data sequence.

[0054] It should be noted that since there are some normal data changes in the temperature data of the transportation container, such as temperature control, this will also cause the temperature data to change violently, and then the change trends of the surrounding data of the temperature data will become more inconsistent, resulting in deviations in the calculation of the noise performance degree of the temperature data. For example, the noise performance degree of the temperature data with normal changes is calculated too large. Therefore, it is necessary to correct the noise performance degree of the temperature data;

[0055] The temperature data collected simultaneously under multiple temperature sensors in the transportation container will show the same data changes. Therefore, the noise performance degree of the temperature data can be corrected according to the correlation between the temperature data sequences collected under multiple temperature sensors. If the correlation between the temperature data sequence to which any temperature data belongs and other temperature data sequences is greater, it indicates that the temperature data is more likely to be a normal data change. Therefore, it is necessary to lower the noise performance degree of the temperature data. And the smaller the distance between the temperature data sequence to which the temperature data belongs and other temperature data sequences, the more credible the correlation between the temperature data sequence to which the temperature data belongs and other temperature data sequences. It is also necessary to weight the correlation between the temperature data sequence to which the temperature data belongs and other temperature data sequences according to the distance between the temperature data sequence to which the temperature data belongs and other temperature data sequences.

[0056] In the embodiment of the present invention, the temperature sensor that collects the current temperature data is denoted as the current temperature sensor;

[0057] Obtain the corrected noise performance degree of the current temperature data:

[0058] ;

[0059] In the formula, k represents the corrected noise performance degree of the current temperature data; represents the noise performance degree of the current temperature data; J represents the number of temperature sensors other than the current temperature sensor; represents the distance between the current temperature sensor and the j-th temperature sensor other than the current temperature sensor; represents the Pearson correlation coefficient between the temperature data sequence collected under the current temperature sensor and the temperature data sequence collected by the j-th temperature sensor other than the current temperature sensor; Since ranges from -1 to 1, so is used to limit the value of The larger the value, the more correlated the temperature data sequence collected by the current temperature sensor is with the temperature data sequence collected by the j-th temperature sensor other than the current temperature sensor, indicating that the data in the temperature data sequences collected by these two temperature sensors are normal data changes. At this time, the noise manifestation degree of the current temperature data is less realistic and needs to be adjusted smaller. The smaller the value, the more accurate the correlation between the temperature data sequence collected by the current temperature sensor and the temperature data sequence collected by the j-th temperature sensor other than the current temperature sensor.

[0060] Similarly, obtain the noise manifestation degree after correction for each temperature data in each temperature data sequence.

[0061] It should be noted that when performing Vondrak filtering on all temperature data collected in the transportation container, a larger difference order is required for a data set with a larger noise manifestation degree to better smooth the data; conversely, a smaller difference order is used for a data set with a smaller noise manifestation degree to better retain the detailed information of the temperature data and also save computing resources. Therefore, each temperature data sequence is divided into several data sets, and according to the noise manifestation degree after correction of all temperature data in each data set of each temperature data sequence, obtain the difference order of each data set of each temperature data sequence.

[0062] In the embodiment of the present invention, preset the number M of data in the data set. According to the number of data in the data set, each temperature data sequence is successively divided into several data sets. If the number at the end of the temperature data sequence does not meet M, perform a padding operation with 0; it should be noted that the preset number M of data in the data set is 5. In other embodiments, the implementer can preset the value of M according to the specific implementation situation.

[0063] Obtain the difference order of each data set of each temperature data sequence:

[0064] ;

[0065] In the formula, represents the difference order of the b-th data set of the a-th temperature data sequence; represents the reference difference order; in the embodiment of the present invention, preset the reference difference order =3, represents the mean value of the noise manifestation degree after correction of all temperature data in the b-th data set of the a-th temperature data sequence; The larger the value, the larger the difference order required for the b-th data set of the a-th temperature data sequence.

[0066] S4: Filter the temperature data in each dataset of each temperature data sequence according to the difference order of each dataset of each temperature data sequence to obtain each filtered temperature data sequence, and identify abnormal conditions of the transportation container based on each filtered temperature data sequence.

[0067] It should be noted that the temperature data in each dataset of each temperature data sequence is filtered according to the difference order of each dataset of each temperature data sequence.

[0068] In the embodiment of the present invention, the Vondrak filtering algorithm is used, and the temperature data in each dataset of each temperature data sequence is filtered according to the difference order of each dataset of each temperature data sequence to obtain each filtered temperature data sequence;

[0069] The multiple temperature data corresponding to any sampling moment in all the filtered temperature data sequences are recorded as the temperature data group at this sampling moment. If the data mean value in the temperature data group at this sampling moment exceeds the preset temperature range, at this time, the transportation container has an abnormality at this sampling moment, and the system issues an alarm to remind the staff to process it in time, completing the control of the temperature data of the transportation container in the logistics process of the manufacturing factory.

[0070] In the embodiment of the present invention, the preset temperature range is 2°C - 10°C. In other embodiments, the implementer can preset the temperature range according to the specific implementation situation.

[0071] It should be noted that in order to ensure the safety and reliability of the transported items (electric cores, battery packs) in the transportation container, the pressure data on the electric cores and battery packs in the transportation container can also affect the safety during transportation. If the pressure data on the electric core or battery pack changes suddenly, it indicates that the electric core or battery pack has been squeezed or collided, affecting transportation safety; therefore, the present invention also needs to collect pressure data for analysis, install pressure sensors on each transported item in the transportation container, and collect the pressure data of each pressure sensor during the operation of the transportation container. If any pressure data exceeds the normal pressure range, it indicates that the transportation container has an abnormality at the sampling moment corresponding to this pressure data.

[0072] The embodiment of the present invention also discloses a logistics control system for a manufacturing factory based on a digital twin platform, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the logistics control method for a manufacturing factory based on a digital twin platform according to the present invention is implemented.

[0073] The above system also includes other components well known to those skilled in the art such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be described in detail here.

[0074] In the present invention, the foregoing memory may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as, for example, a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high bandwidth memory, a hybrid memory cube, etc., or any other medium that can be used to store the required information and can be accessed by an application program, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0075] Although this specification has shown and described several embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many variations, changes, and alternative ways will occur to those skilled in the art without departing from the spirit and scope of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

[0076] The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A method for controlling the logistics of a manufacturing factory based on a digital twin platform, characterized in that, Includes steps: Collecting a number of temperature data sequences; recording any temperature data in any temperature data sequence as current temperature data, and recording the temperature sensor that collects the current temperature data as the current temperature sensor; Get the noise performance after the current temperature data is corrected, specifically: ； Wherein, represents the noise performance degree of the current temperature data; J represents the number of temperature sensors other than the current temperature sensor; represents the distance between the current temperature sensor and the j-th temperature sensor other than the current temperature sensor; represents the Pearson correlation coefficient between the temperature data sequence collected under the current temperature sensor and the temperature data sequence collected by the j-th temperature sensor other than the current temperature sensor; norm() represents the normalization function; exp() represents the exponential function with the natural constant as the base; The noise performance level of the current temperature data is obtained by: obtaining the slope and the fitted value of each surrounding data of the current temperature data, and calculating the noise performance level of the current temperature data as follows: ； In the formula, represents the slope variance of all surrounding data of the current temperature data; represents the number of surrounding data of the current temperature data; represents the value of the i-th surrounding data of the current temperature data; represents the fitted value of the i-th surrounding data of the current temperature data; || represents the absolute value symbol; tanh() represents the normalization function; Divide each temperature data series into several data sets, including: The number of data in the preset data set is M, and each temperature data sequence is divided into a number of data sets in turn according to the number of data in the data set; Get the difference order of each data set for each temperature data series, including: ； In the formula, represents the difference order of the b-th data set of the a-th temperature data sequence; represents the reference difference order, represents the mean value of the noise performance degree after correction of all temperature data in the b-th data set of the a-th temperature data sequence; Each temperature data sequence is filtered based on the difference order, and abnormal conditions of the transport container are identified.

2. The method for manufacturing plant logistics control based on a digital twin platform according to claim 1, characterized in that, The step of obtaining the slope and fitting value of each surrounding data of the current temperature data includes: Record the m temperature data before and the m temperature data after the current temperature data in the temperature data sequence to which it belongs as the surrounding data of the current temperature data; use the least square method to fit the surrounding data of the current temperature data to obtain the fitting curve of the current temperature data and the fitting value of each surrounding data of the current temperature data; The slope of each surrounding data of the current temperature data in the fitting curve of the current temperature data is obtained, and recorded as the slope of each surrounding data of the current temperature data.

3. The method for controlling the logistics of a manufacturing factory based on a digital twin platform according to claim 1, wherein The filtering process is performed on each temperature data sequence based on the differential order, and identifying abnormal conditions of the transport container, including: The Vondrak filtering algorithm is used, and the temperature data in each data set of each temperature data sequence is filtered according to the differential order of each data set of each temperature data sequence to obtain each filtered temperature data sequence; the multiple temperature data corresponding to any sampling time in all filtered temperature data sequences are recorded as the temperature data group at the sampling time. If the data mean value in the temperature data group at the sampling time exceeds the preset temperature range, the transport container at the sampling time is abnormal.

4. The method for controlling the logistics of a manufacturing factory based on a digital twin platform according to claim 1, wherein The collecting of several temperature data sequences includes: The preset sampling time is 2 seconds / time, and the data is collected for a total of one hour. Multiple temperature sensors are installed at equal intervals in the transport container. During the operation of the transport container, the temperature data of each temperature sensor is collected, and the temperature data of each temperature sensor is sorted in turn to obtain several temperature data sequences.

5. A manufacturing plant logistics control system based on a digital twin platform, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a manufacturing factory logistics management and control method based on a digital twin platform according to any one of claims 1 to 4 is implemented.

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