Manufacturing industry factory logistics management and control method and system based on digital twin platform

Abstract

The invention relates to the technical field of data processing, in particular to a manufacturing industry factory logistics management and control method and system based on a digital twin platform. The method comprises the steps that each temperature data sequence is collected, the noise expression degree of each piece of temperature data in each temperature data sequence is obtained, the noise expression degree of each piece of temperature data in each temperature data sequence is corrected according to the correlation between the temperature data sequences, and the noise expression degree of each piece of temperature data in each temperature data sequence is obtained. The method comprises the following steps: obtaining a noise expression degree of each temperature data after correction in each temperature data sequence, filtering the temperature data in each data set of each temperature data sequence according to a difference order of each data set of each temperature data sequence to obtain each filtered temperature data sequence, and obtaining a corrected noise expression degree of each temperature data in each temperature data sequence based on each filtered temperature data sequence. According to the invention, the filtering effect is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a manufacturing factory logistics management and control method and system based on a digital twin platform. Background Art

[0002] Digital twins are models built in the digital world that are completely consistent with physical objects in the real world. Physical objects are dynamically simulated, monitored, analyzed, and controlled through digital means. Logistics control of manufacturing factories based on digital twin platforms is a visual management method for factory logistics after building a model for the manufacturing factory. The digital twin platform of the manufacturing factory needs to provide visibility of data such as the temperature of transport containers to ensure the safety of the transported goods. Therefore, it is necessary to collect temperature data and other data of the transport containers in real time for real-time monitoring.

[0003] The patent document with the current authorization announcement number 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 sampling consistency algorithm; calculating the straight-line distance of each observed data to the best straight-line equation model; assigning a corresponding weight coefficient to each observed data according to the straight-line distance corresponding to each observed data; using the weight coefficient corresponding to each observed data as the weight of the observed data in the Vondrak filtering method, and filtering the observed data using the Vondrak filtering method to obtain a filtering curve about the observed data.

[0004] Due to the different road conditions during the transportation of transport containers, different road conditions may have different impacts on the temperature sensor. Therefore, the degree of noise impact on the temperature data under different road conditions may be different. In the Vondrak filter algorithm, the differential order is mainly used to describe the degree of smoothing of the data. If a fixed differential order is used, it may not be adjusted according to the actual situation of the temperature data, and it may not be able to adapt to the smoothing requirements of the temperature data under different road conditions, resulting in filtering or under-filtering problems 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 temperature data, it cannot adapt to the smoothing requirements of temperature data under different road conditions, resulting in under-filtering or under-filtering of temperature data, the present invention provides a manufacturing factory logistics management and control method and system based on a digital twin platform.

[0006] In the first aspect, the present invention provides a manufacturing factory logistics management and control method based on a digital twin platform, which adopts the following technical solutions: The manufacturing factory logistics control method based on the digital twin platform includes the following steps: Collect each temperature data sequence; record any temperature data in any temperature data sequence as current temperature data, and record 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 ; represents the noise performance 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 jth 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; Each temperature data sequence is divided into several data sets; the differential order of each data set of each temperature data sequence is obtained, and the differential order is positively correlated with the mean value of the corrected noise performance degree of all temperature data in each data set of each temperature data sequence; each temperature data sequence is filtered based on the differential order, and abnormal conditions of the transport container are identified.

[0007] The innovation of the present invention lies in analyzing the changing characteristics of temperature data collected during the operation of the transport 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 the temperature data sequences to obtain the corrected noise performance degree of the temperature data, thereby adapting the differential order in the Vondrak filter according to the corrected noise performance degree, and being able to adaptively adjust the differential order according to the actual situation of the temperature data, thereby providing the best smoothing effect, helping to adapt to the smoothing requirements of temperature data in different situations, improving the generalization ability of the algorithm, and effectively avoiding the problems of being unable to adapt to temperature data changes, filtering and under-filtering caused by fixed differential orders, thereby improving the efficiency of logistics control in manufacturing factories.

[0008] Preferably, obtaining the noise performance level of the current temperature data includes: Get the slope and fitting value of each surrounding data of the current temperature data; ; 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 fitted value of the i-th surrounding data of the current temperature data; || represents the absolute value symbol; tanh() represents the normalization function.

[0009] This facilitates the subsequent adaptive acquisition of the differential order according to the degree of noise performance to improve the filtering effect.

[0010] Preferably, 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.

[0011] Preferably, the step of dividing each temperature data sequence into a plurality of data sets includes: The number of data in the data set is preset 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.

[0012] This facilitates the subsequent adaptive acquisition of the difference order of each data set of each temperature data sequence.

[0013] Preferably, obtaining the differential order of each data set of each temperature data sequence includes: ; In the formula, Represents the difference order of the bth data set of the ath temperature data series; represents the base difference order, Represents the mean value of the noise performance of all temperature data in the bth data set of the ath temperature data sequence after correction.

[0014] The resulting difference order for each data set of each temperature data series is more accurate.

[0015] Preferably, filtering each temperature data sequence based on the differential order and identifying abnormal conditions of the transport container includes: 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.

[0016] The difference order in the Vondrak filter is adapted according to the noise performance after the temperature data is corrected, thereby providing the best smoothing effect.

[0017] Preferably, the collecting of a plurality of 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.

[0018] In the second aspect, the present invention provides a manufacturing factory logistics management and control system based on a digital twin platform, which adopts the following technical solutions: A manufacturing factory logistics management and control system based on a digital twin platform includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the manufacturing factory logistics management and control method based on the digital twin platform is implemented.

[0019] By adopting the above-mentioned technical solution, the above-mentioned manufacturing factory logistics control method based on the digital twin platform is generated into a computer program and stored in the memory to be loaded and executed by the processor, so that a terminal device is made according to the memory and the processor for easy use.

[0020] The present invention has the following technical effects: the purpose of the present invention is to analyze the change characteristics of temperature data collected during the operation of the transport 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 to obtain the corrected noise performance degree of the temperature data, so as to adapt the differential order in the Vondrak filter according to the corrected noise performance degree, and be able to adaptively adjust the differential order 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, and effectively avoid the problems of being unable to adapt to temperature data changes, filtering and under-filtering caused by fixed differential orders, thereby improving the efficiency of logistics control in manufacturing factories. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0022] Figure 1 It is a method flow chart of the manufacturing factory logistics management and control method based on the digital twin platform in an embodiment of the present invention. DETAILED DESCRIPTION

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

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

[0025] The embodiment of the present invention discloses a manufacturing factory logistics management and control method based on a digital twin platform, referring to Figure 1 , comprising steps S1 to S3: S1: Collect each temperature data sequence.

[0026] It should be noted that in order to ensure the safety and reliability of the transported items (battery cells, battery packs) in the transport container, the present invention needs to collect temperature data in the transport container. By collecting the temperature data on the battery cells and battery packs, it can be determined whether a sudden temperature change occurs in the battery cells or battery packs to ensure safety during transportation.

[0027] In the implementation of the present invention, the sampling time is preset to 2 seconds / time, and the total sampling is one hour. A temperature sensor is installed on each transport 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.

[0028] S2: Obtain the noise performance level of each temperature data in each temperature data sequence.

[0029] It should be noted that due to the different road conditions during the transportation of transport containers, different road conditions may have different impacts on the temperature sensor. Therefore, the degree of noise impact on the temperature data under different road conditions may be different. In the Vondrak filtering algorithm, the differential order is mainly used to describe the degree of smoothing of the data. If a fixed differential order is used, it may not be adjusted according to the actual situation of the temperature data, and it may not be able to adapt to the smoothing requirements of the temperature data under different road conditions, resulting in 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 changing characteristics of the temperature data of the transport container during the transportation process, the noise performance degree of the temperature data is obtained, and the differential order in the Vondrak filter is adapted according to the noise performance degree, so as to avoid filtering or under-filtering problems caused by using a fixed differential order, and improve the filtering effect of the temperature data, thereby improving the monitoring efficiency of the temperature data of the transport container and improving the efficiency of the logistics control of the manufacturing factory.

[0030] It should be further explained that due to the road conditions during the transportation of the transport container, the vehicle may be bumpy, which will cause noise in the temperature sensor when collecting temperature data. Such noise is manifested as violent jittering of the temperature data in the surrounding data, and irregular fluctuations occur. Therefore, the regularity of the temperature data in its surrounding data can be used to obtain the noise performance degree of the temperature data; if the changing trend of the surrounding data of the temperature data is more consistent, it means that the regularity of the temperature data in its surrounding data is better, and the noise interference is less, so the surrounding data of the temperature data is obtained, and the noise performance degree of the temperature data is obtained based on the surrounding data of the temperature data.

[0031] 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; 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; 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.

[0032] Get the noise level of the current temperature data: ; 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.

[0033] 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.

[0034] It should be noted that, since there are some normal data changes in the temperature data of the transport container, such as temperature control, this will also cause drastic changes in the temperature data, which in turn makes the change trend of the surrounding data of the temperature data more inconsistent, causing deviations in the calculation of the noise performance degree of the temperature data, such as calculating the noise performance degree of the normally changing temperature data too large, so it is necessary to correct the noise performance degree of the temperature data; The temperature data collected simultaneously by multiple temperature sensors in the transport 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 by 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 means that the temperature data is more likely to be a normal data change. Therefore, the noise performance degree of the temperature data needs to be lowered. 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. The correlation between the temperature data sequence to which the temperature data belongs and other temperature data sequences should also be weighted according to the distance between the temperature data sequence to which the temperature data belongs and other temperature data sequences.

[0035] In the embodiment of the present invention, the temperature sensor that collects current temperature data is recorded as the current temperature sensor; Get the corrected noise performance of the current temperature data: ; In the formula, k represents the noise performance degree after the current temperature data is corrected; Represents the noise performance 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 jth 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 jth temperature sensor other than the current temperature sensor; The value of is between -1 and 1, so use Will The value of is limited to between 0 and 1; norm() represents the normalization function; exp() represents the exponential function with a natural constant as the base; The larger the value is, the more correlation there is between the temperature data sequence collected by the current temperature sensor and the temperature data sequence collected by the jth temperature sensor other than the current temperature sensor, indicating that the data in the temperature data sequences collected by the two temperature sensors are normal data changes. At this time, the noise performance of the current temperature data is less realistic and needs to be reduced. The smaller the value of , the more accurate the correlation between the temperature data sequence collected by the current temperature sensor and the temperature data sequence collected by the jth temperature sensor other than the current temperature sensor.

[0036] Similarly, the noise performance degree of each temperature data in each temperature data sequence after correction is obtained.

[0037] It should be noted that when Vondrak filtering is performed on all temperature data collected in the transport container, a larger differential order is required for a data set with a greater degree of noise performance in order to better smooth the data; conversely, a smaller differential order is used for a data set with a smaller degree of noise performance in order to better retain the detailed information of the temperature data and save computing resources. Therefore, each temperature data sequence is divided into several data sets, and the differential order of each data set of each temperature data sequence is obtained according to the corrected noise performance of all temperature data in each data set of each temperature data sequence.

[0038] In an embodiment of the present invention, the number of data in the preset data set is M. According to the number of data in the data set, each temperature data sequence is divided into several data sets in turn. If the number at the end of the temperature data sequence does not meet M, it is padded with 0. It should be noted that the number of data in the preset data set is M=5. In other embodiments, the implementer can preset the value of M according to the specific implementation situation.

[0039] Get the differencing order for each data set for each temperature data series: ; In the formula, Represents the difference order of the bth data set of the ath temperature data series; represents the reference differential order; in the embodiment of the present invention, the preset reference differential order =3, Represents the mean value of the noise performance of all temperature data in the bth data set of the ath temperature data sequence after correction; The larger the value of , the larger the difference order of the bth data set of the ath temperature data sequence is required.

[0040] S4: According to the differential order of each data set of each temperature data sequence, the temperature data in each data set of each temperature data sequence is filtered to obtain each filtered temperature data sequence, and based on each filtered temperature data sequence, the abnormal situation of the transport container is identified.

[0041] It should be noted that, according to the difference order of each data set of each temperature data sequence, the temperature data in each data set of each temperature data sequence is filtered.

[0042] In the embodiment of the present invention, 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 moment in all filtered temperature data sequences are recorded as the temperature data group at the sampling moment. If the data mean in the temperature data group at the sampling moment exceeds the preset temperature range, there is an abnormality in the transport container at the sampling moment. The system issues an alarm to remind the staff to deal with it in time, thus completing the control of the temperature data of the transport containers in the logistics process of the manufacturing factory.

[0043] In the embodiment of the present invention, the preset temperature range is 2° C.-10° C. In other embodiments, implementers can preset the temperature range according to specific implementation conditions.

[0044] It should be noted that in order to ensure the safety and reliability of the transported items (battery cells, battery packs) in the transport container, the pressure data on the battery cells and battery packs in the transport container may also affect the safety during transportation. If the pressure data on the battery cells or battery packs changes suddenly, it means that the battery cells or battery packs have been squeezed or collided, affecting transportation safety. Therefore, the present invention also needs to collect pressure data for analysis, and install pressure sensors on each transported item in the transport container. During the operation of the transport container, the pressure data of each pressure sensor is collected. If any pressure data exceeds the normal pressure range, it means that the transport container at the sampling time corresponding to the pressure data is abnormal. An embodiment of the present invention also discloses a manufacturing factory logistics management and control system based on a digital twin platform, including a processor and a memory, wherein the memory stores computer program instructions. 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 the present invention is implemented.

[0045] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0046] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as 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 storage cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0047] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0048] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A manufacturing factory logistics management and control method 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 ; Represents the noise level 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 jth 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; Divide each temperature data sequence into a plurality of data sets; obtain the differential order of each data set of each temperature data sequence, wherein the differential order is positively correlated with the mean value of the corrected noise performance of all temperature data in each data set of each 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 manufacturing factory logistics management and control method based on the digital twin platform according to claim 1 is characterized in that: The acquisition of the noise performance degree of the current temperature data includes: Get the slope and fitting value of each surrounding data of the current temperature data; ； 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 fitted value of the i-th surrounding data of the current temperature data; || represents the absolute value symbol; tanh() represents the normalization function.

3. The manufacturing factory logistics management and control method based on the digital twin platform according to claim 2 is 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.

4. The manufacturing factory logistics management and control method based on the digital twin platform according to claim 1 is characterized in that: Each temperature data sequence is divided into several data sets, including: The number of data in the data set is preset, 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.

5. The manufacturing factory logistics management and control method based on the digital twin platform according to claim 4 is characterized in that: The step of obtaining the difference order of each data set of each temperature data sequence includes: ； In the formula, Represents the difference order of the bth data set of the ath temperature data series; represents the base difference order, Represents the mean value of the noise performance of all temperature data in the bth data set of the ath temperature data sequence after correction.

6. The manufacturing factory logistics management and control method based on the digital twin platform according to claim 1 is characterized in that: 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.

7. The manufacturing factory logistics management and control method based on the digital twin platform according to claim 1 is characterized in that: 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.

8. The manufacturing factory logistics management and control system based on the digital twin platform is characterized by: 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 7 is implemented.

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