Intelligent industrial production environment optimization method and device, storage medium and program product

Abstract

The embodiment of the invention discloses an intelligent industrial production environment optimization method and device, a storage medium and a program product. The method comprises the following steps: based on a communication network, obtaining historical production environment data and historical raw material data according to a target product, and a historical observation value of a specified production index of the target product; screening out production environment conditions and raw materials related to the specified production index of the target product based on the historical observation value; constructing a target function and constraint conditions based on the expected numerical range of the specified production index of the target product, the production environment conditions related to the specified production index of the target product and the raw materials; and solving the objective function according to the constraint condition, and obtaining and displaying ideal production environment data and ideal raw material data. According to the method, through data collection, analysis and real-time display, the efficiency, accuracy and transparency of the industrial production process can be remarkably improved, and finally continuous improvement of the production environment and reasonable utilization of resources are promoted.

Description

Technical Field

[0001] The present disclosure relates to the field of industrial manufacturing technology, and in particular to an intelligent industrial production environment optimization method, device, storage medium, and program product. Background Art

[0002] In the industrial manufacturing industry, production efficiency and product quality are the core indicators for measuring corporate competitiveness. With the development of intelligent manufacturing technology, in-depth analysis and optimization of production processes have become increasingly important. However, traditional production methods mainly rely on experience judgment and basic automation, fail to make full use of data-driven optimization strategies, and have deficiencies in production environment condition control and raw material use, which limits the improvement of production efficiency and product quality. Summary of the invention

[0003] In view of this, the embodiments of the present disclosure provide an intelligent industrial production environment optimization method, device, storage medium, and program product, which can significantly improve the efficiency, accuracy, and transparency of the industrial production process through data collection, analysis, and real-time display, and ultimately promote the continuous improvement of the production environment and the rational use of resources.

[0004] In a first aspect, the embodiments of the present disclosure provide a method for optimizing an intelligent industrial production environment, which adopts the following technical solutions:

[0005] Build a communication network;

[0006] Obtain the target product, and based on the communication network, obtain historical production environment data and historical raw material data of the target product from the industrial equipment module, and transmit them to the processing center;

[0007] Obtain historical observations of specified production indicators of target products;

[0008] The processing center performs correlation analysis on various production environment conditions and various raw materials based on historical observation values, and screens out production environment conditions and raw materials that are related to the specified production indicators of the target product;

[0009] Constructing an objective function and constraint conditions based on an expected numerical range of a specified production indicator of the target product, production environment conditions and raw materials related to the specified production indicator of the target product;

[0010] Solving the objective function according to the constraint conditions to obtain ideal production environment data and ideal raw material data;

[0011] Transmitting the ideal production environment data and the ideal raw material data to a display center for data display based on the communication network;

[0012] Among them, the historical production environment data refers to the values ​​of various production environment conditions of the target product recorded within the past preset time; the historical raw material data refers to the proportion of each raw material used in the production of the target product within the past preset time; the ideal production environment data is the ideal value of the production environment conditions related to the specified production indicators of the target product; the ideal raw material data is the ideal proportion of raw materials related to the specified production indicators of the target product.

[0013] Optionally, it includes: performing a correlation analysis on the production environment conditions, specifically:

[0014] Obtaining a first correlation coefficient between the production environment condition and a specified production index of a target product;

[0015] Obtaining a first absolute value of the first correlation coefficient;

[0016] Determining whether the first absolute value is greater than a first preset threshold;

[0017] If so, determining that the production environment condition is related to the specified production index of the target product;

[0018] If not, it is determined that the production environment condition is not relevant to the specified production index of the target product, and the production environment condition is screened out.

[0019] Optionally, the correlation coefficient between the i-th production environment condition and the specified production index of the target product is The calculation formula is as follows:

[0020]

[0021] Among them, E i is the i-th production environment condition when producing the target product; Z is the specified production index of the target product; E it is the value of the i-th production environment condition when producing the target product recorded at the observation time point t; is the average value of the i-th production environment condition when producing the target product at all observation time points T; Z t is the actual observed value of the specified production indicator of the target product recorded at the observation time point t; It is the average value of the specified production index of the target product recorded at all observation time points T; T is the total number of observation time points.

[0022] Optionally, it includes: performing a correlation analysis on the raw materials, specifically:

[0023] Obtaining a second correlation coefficient between the raw material and a specified production index of the target product;

[0024] Obtaining a second absolute value of the second correlation coefficient;

[0025] Determining whether the second absolute value is greater than a second preset threshold;

[0026] If so, it is determined that the raw material is related to the specified production index of the target product;

[0027] If not, it is determined that the raw material is not relevant to the specified production index of the target product, and the raw material is screened out.

[0028] Optionally, the correlation coefficient between the jth raw material and the specified production index of the target product is The calculation formula is as follows:

[0029]

[0030] Among them, E j The jth raw material used to produce the target product; E jt is the proportion of the jth raw material in the production of the target product recorded at the observation time point t; is the average value of the proportion of the jth raw material in the production of the target product recorded at all observation time points T.

[0031] Optionally, the objective function is:

[0032]

[0033] The constraints are:

[0034] L m ≤E m ≤U m , for all m=1,...,M;

[0035]

[0036] L n ≤E n ≤U n , for all n=1,...,N;

[0037] Y min ≤Y≤Y max ;

[0038] Among them, Y is the expected value of the specified production index of the target product; β0 is the intercept term; E m is the value of the mth production environment condition related to the specified production index of the target product; M is the total number of types of production environment conditions related to the specified production index of the target product; is the regression coefficient of the mth production environment condition related to the specified production index of the target product; E n is the proportion of the nth raw material related to the specified production index of the target product; N is the total number of raw material types related to the specified production index of the target product; is the regression coefficient of the nth raw material related to the specified production index of the target product; ε is the error term; L m is the lower limit of the mth production environment condition related to the specified production index of the target product; U m L is the upper limit of the mth production environment condition related to the specified production index of the target product; n is the lower limit of the proportion of the nth raw material related to the specified production index of the target product; U n Y is the upper limit of the proportion of the nth raw material related to the specified production index of the target product; min Y is the expected lower limit of the specified production index of the target product; max Y is the expected upper limit of the specified production index of the target product; min and Y max It constitutes the expected numerical range of the specified production indicators of the target product.

[0039] Optionally, in the communication network, the historical production environment data, the historical raw material data, the ideal production environment data, and the ideal raw material data are transmitted in the form of signals, and the transmitted signals are adjusted to optimize signal transmission, specifically including:

[0040] Constructing an optimization problem, and obtaining an estimated value of a channel matrix based on the optimization problem;

[0041] Acquire a precoding matrix based on an estimated value of the channel matrix;

[0042] YY+242225P

[0043] Based on the precoding matrix, a transmitted signal is adjusted.

[0044] In a second aspect, the embodiments of the present disclosure also provide an intelligent industrial production environment optimization system, which adopts the following technical solutions:

[0045] Network building module, used to build communication network;

[0046] A data module module is used to obtain the target product, obtain the historical production environment data and historical raw material data of the target product from the industrial equipment module based on the communication network, and transmit it to the processing center;

[0047] An observation value acquisition module is used to obtain historical observation values ​​of specified production indicators of target products;

[0048] A correlation analysis module, used for the processing center to perform correlation analysis on a variety of production environment conditions and a variety of raw materials based on historical observations, and screen out production environment conditions and raw materials that are related to the specified production indicators of the target product;

[0049] A function construction module, used to construct an objective function and constraint conditions based on an expected numerical range of a specified production indicator of a target product, production environment conditions related to the specified production indicator of the target product, and raw materials;

[0050] A function solving module, used to solve the objective function according to the constraint conditions to obtain ideal production environment data and ideal raw material data;

[0051] A data display module, used for transmitting the ideal production environment data and the ideal raw material data to a display center for data display based on the communication network;

[0052] Among them, the historical production environment data refers to the values ​​of various production environment conditions of the target product recorded within the past preset time; the historical raw material data refers to the proportion of each raw material used in the production of the target product within the past preset time; the ideal production environment data is the ideal value of the production environment conditions related to the specified production indicators of the target product; the ideal raw material data is the ideal proportion of raw materials related to the specified production indicators of the target product.

[0053] In a third aspect, the embodiments of the present disclosure further provide a computer device, which adopts the following technical solution:

[0054] The computer device comprises:

[0055] at least one processor; and,

[0056] a memory communicatively connected to the at least one processor; wherein,

[0057] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above-mentioned intelligent industrial production environment optimization methods.

[0058] In a fourth aspect, the embodiments of the present disclosure further provide a computer-readable storage medium storing computer instructions for causing a computer to execute any of the above-mentioned intelligent YY+242225P

[0059] Industrial production environment optimization methods.

[0060] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, including a computer program / instruction, which implements the steps of any of the above methods when executed by a processor.

[0061] The intelligent industrial production environment optimization method provided by the embodiment of the present disclosure realizes the optimization of the production environment based on actual data by building a communication network and obtaining historical production environment data and raw material data from the industrial equipment module. This data-driven method can improve the accuracy and practicality of the optimization. By obtaining the historical observation values ​​of the target product, the processing center performs correlation analysis on various production environment conditions and raw materials, and can screen out factors that have a significant impact on the specified production indicators, so as to avoid invalid optimization attempts and concentrate resources on truly important factors, thereby optimizing the production process. According to the expected numerical range of the specified production indicators of the target product and the relevant production environment conditions and raw materials, the objective function and constraint conditions are constructed, which can ensure the scientificity and rationality of the optimization scheme, so that the production process is more in line with actual needs. By solving the objective function and obtaining the ideal production environment data and the ideal raw material data, a clear optimization direction can be provided for the production process, thereby improving production efficiency and product quality. The ideal production environment data and the ideal raw material data are transmitted to the display center for data display, so that relevant personnel can understand the optimization results in real time and make necessary adjustments, which helps to improve the transparency of production management and the timeliness of decision-making. .

[0062] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0064] Figure 1 A schematic diagram of a process flow of an intelligent industrial production environment optimization method provided by an embodiment of the present disclosure;

[0065] Figure 2 A schematic diagram of a massive MIMO antenna array provided in an embodiment of the present disclosure;

[0066] Figure 3 A schematic diagram of a process for performing correlation analysis on production environment conditions provided in an embodiment of the present disclosure;

[0067] Figure 4 A schematic diagram of a process for performing correlation analysis on raw materials provided in an embodiment of the present disclosure;

[0068] Figure 5 A flow chart of a communication network anti-interference method provided in an embodiment of the present disclosure;

[0069] Figure 6 A principle block diagram of an intelligent industrial production environment optimization system provided by an embodiment of the present disclosure;

[0070] Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0071] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0072] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0073] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0074] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0075] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0076] In the traditional industrial production environment, various industrial equipment, processing centers, and display centers transmit data based on the communication network. Specifically, each industrial equipment constitutes an industrial equipment module, which transmits the collected data to the processing center, which analyzes the data and then transmits the analysis results to the display center for display. However, there are some problems with this approach. For example, the communication network is easily interfered by magnetic fields, which may cause data transmission failure or delay. In addition, traditional data analysis methods often cannot meet the needs of optimized production.

[0077] For this purpose, refer to Figure 1 The present disclosure provides an intelligent industrial production environment optimization method for improving the above problems, comprising the following steps:

[0078] S1: Build a communication network.

[0079] S2: Obtain the target product. Based on the communication network, obtain the historical production environment data and historical raw material data of the target product from the industrial equipment module and transmit them to the processing center.

[0080] Among them, historical production environment data refers to the values ​​of various production environment conditions of the target products recorded within the past preset time; historical raw material data refers to the proportion of each raw material used in the production of the target products within the past preset time.

[0081] S3: Obtain the historical observation values ​​of the specified production indicators of the target product.

[0082] The designated production index is any one of quality, performance, output, production efficiency, and scrap rate.

[0083] S4: The processing center performs correlation analysis on various production environment conditions and various raw materials based on historical observations, and selects production environment conditions and raw materials that are related to the specified production indicators of the target product.

[0084] S5: Construct objective functions and constraints based on the expected numerical range of the specified production indicators of the target product, the production environment conditions related to the specified production indicators of the target product, and the raw materials.

[0085] S6: Solve the objective function according to the constraints to obtain ideal production environment data and ideal raw material data.

[0086] S7: Based on the communication network, the ideal production environment data and the ideal raw material data are transmitted to the display center for data display.

[0087] Among them, the ideal production environment data is the ideal value of the production environment conditions related to the specified production indicators of the target product; the ideal raw material data is the ideal proportion of raw materials related to the specified production indicators of the target product.

[0088] The intelligent industrial production environment optimization method disclosed in the present invention realizes the optimization of the production environment based on actual data by building a communication network and obtaining historical production environment data and raw material data from the industrial equipment module. This data-driven method can improve the accuracy and practicality of the optimization. By obtaining the historical observation values ​​of the target product, the processing center performs correlation analysis on various production environment conditions and raw materials, and can screen out factors that have a significant impact on the specified production indicators, so that invalid optimization attempts can be avoided, and resources can be concentrated on truly important factors, thereby optimizing the production process. According to the expected numerical range of the specified production indicators of the target product and the relevant production environment conditions and raw materials, the objective function and constraints are constructed, which can ensure the scientificity and rationality of the optimization scheme, so that the production process is more in line with actual needs. By solving the objective function and obtaining the ideal production environment data and the ideal raw material data, a clear optimization direction can be provided for the production process, thereby improving production efficiency and product quality. The ideal production environment data and the ideal raw material data are transmitted to the display center for data display, so that relevant personnel can understand the optimization results in real time and make necessary adjustments, which helps to improve the transparency of production management and the timeliness of decision-making.

[0089] In summary, this optimization method can significantly improve the efficiency, accuracy and transparency of industrial production processes through data collection, analysis and real-time display, and ultimately promote the continuous improvement of the production environment and the rational use of resources.

[0090] In S1, the communication network consists of LiFi network and WiFi network. LiFi (Light Fidelity, optical wireless communication) network and WiFi (Wireless Fidelity, wireless fidelity) network are deployed in industrial manufacturing plants. LiFi network is implemented by LiFi transmitter (LiFi transmitter can be LED light) and LiFi receiver (LiFi receiver can be photoelectric detector), and WiFi network is implemented by WiFi access point (AP). In the actual environment of industrial manufacturing plants, the deployment locations of LiFi transmitter, LiFi receiver and WiFi access point are planned to ensure coverage of all key areas. Among them, data is transmitted by modulating the intensity of LED lights, and the human eye cannot detect this change, which can achieve a very high data transmission rate (up to 224Gbps). LiFi network is not susceptible to electromagnetic interference and is suitable for use in electromagnetically sensitive industrial environments. WiFi is a wireless local area network technology based on the IEEE 802.11 standard. It uses radio frequency bands such as 2.4GHz and 5GHz and is widely used in various electronic devices such as smartphones, laptops, etc. The coverage range is usually tens to hundreds of meters. The latest standards (such as WiFi6) can provide data transmission rates of up to 9.6Gbps.

[0091] In the process of deploying LiFi network and WiFi network, MIMO (Massive Multiple-Input Multiple-Output) technology is integrated at the same time, by installing large-scale MIMO antenna arrays on LiFi transmitters, LiFi receivers, and WiFi access points (refer to Figure 2The schematic diagram of the massive MIMO antenna array is shown. The figure shows a 6x4 antenna element array with a total of 24 antenna elements. This is just an example. The actual massive MIMO system will have more antenna elements, which can reach dozens to hundreds. Multiple antenna elements are arranged in a compact array. In practical applications, this design allows the system to process multiple data streams simultaneously, significantly improving network capacity and spectrum efficiency), which can significantly improve signal transmission quality and network capacity. MIMO technology uses its multiple antenna elements to serve multiple users simultaneously, thereby achieving higher data transmission rates and improving signal quality in LiFi networks and WiFi networks, and ensuring communication reliability and efficiency even in complex and challenging environments such as industrial manufacturing plants. Specifically, according to the specific layout of the industrial manufacturing plant and the expected coverage range, the physical structure of the antenna array is designed, and the appropriate array shape (linear, planar or three-dimensional) and size are selected. High-gain, high-directivity antenna elements are selected to optimize signal coverage and reduce interference. Consider using antennas with different polarization modes to improve signal robustness. Design modular antenna array units to facilitate future expansion and maintenance. Each module should contain a certain number of antenna units and the necessary radio frequency (RF) interfaces.

[0092] Cell-free massive MIMO technology is an advanced wireless communication technology that significantly improves network capacity and spectrum efficiency by deploying large-scale antenna arrays. This technology does not rely on the traditional cellular network structure, but is deployed in a more flexible and distributed manner to adapt to complex and changing environments.

[0093] To ensure that network servers and terminal devices run operating systems that support MPTCP (Multipath Transmission Control Protocol), we use Linux kernel version 3.10 or higher for the server side so that the MPTCP kernel module can be installed through the package manager. For example, in Linux systems, you can use the command sudo apt-get install linux-mptcp to install the required module. After installation, edit system configuration files such as / etc / sysctl.conf to enable MPTCP functions, set checksums, select key parameters such as path managers and schedulers. After completing the configuration, restart the network service to apply these new settings.

[0094] For end devices, the MPTCP configuration process will vary depending on the device type and operating system. Android devices can be enabled by flashing a custom ROM that supports MPTCP, while iOS devices have supported MPTCP by default since iOS 7, although mainly for specific system services. For dedicated equipment such as steel manufacturing and transportation, configure the firmware or driver that supports MPTCP. For laptops and workstations running Windows, you need to rely on a third-party MPTCP implementation; for Linux, follow the steps on the server side.

[0095] In addition to the deployment of the MPTCP protocol stack described above, MPTCP policies can be configured to optimize the allocation of data flows on LiFi and WiFi paths for more efficient data transmission and network management. When configuring the MPTCP policy, LiFi connections are prioritized and WiFi is automatically switched to as a backup link when LiFi is unavailable. The implementation of this policy involves defining interface priorities and adjusting the configuration of the MPTCP path manager. First, create an MPTCP interface configuration file to explicitly set the high priority of the LiFi interface and the low priority of the WiFi interface. Then, by configuring the MPTCP path manager, the "netlink" path manager and the "redundant" scheduler are used to achieve fine-grained control of data flows and enhance network reliability. To achieve automatic switching when LiFi is disconnected, a link monitoring script is developed. The script periodically detects the LiFi link status and automatically switches to the WiFi interface once a link interruption is detected. This script will be configured with appropriate permissions and set to run in the background to ensure seamless network connectivity. In addition, in order for applications to fully utilize the benefits of MPTCP, update the applications to integrate network libraries that support MPTCP. For older applications, consider deploying MPTCP proxies, such as MPTCP transparent proxies, to ensure that they can also run on multipath networks.

[0096] After completing the network configuration, comprehensive testing and optimization are key steps to ensure the performance of the hybrid LiFi and WiFi network, including using network performance testing tools such as iperf3 to evaluate key indicators such as bandwidth, latency, and throughput of MPTCP connections. At the same time, by simulating LiFi link failures, verify whether the network can smoothly and seamlessly switch to WiFi to ensure the continuity and stability of the connection. In addition, continuous monitoring of network performance is essential for real-time detection and resolution of potential problems. Based on the monitoring results and actual usage, the MPTCP configuration parameters are adjusted to optimize the network's load balancing, path selection, and resource allocation strategies to adapt to changing network conditions. Through these meticulous testing, verification, and adjustments, an efficient and reliable hybrid LiFi and WiFi network is built. Such a network configuration can not only give full play to LiFi's advantages in high-speed data transmission, but also ensure seamless switching and continuous operation of network communications when LiFi connections are unstable or unavailable, meeting the strict requirements of mission-critical network stability.

[0097] In terms of performance evaluation, we monitor network performance indicators such as throughput, latency, and switching times to ensure that the network operates in the best state. By collecting user feedback and analyzing data transmission logs, we further optimize MPTCP strategies and network deployment to improve the performance of the entire network system and user experience.

[0098] Through these technical measures, an intelligent and adaptive network environment can be built to provide continuous and stable data transmission capabilities, and the continuity and stability of network connections can be guaranteed even when some network technologies are unavailable.

[0099] Hybrid LiFi and WiFi network is a new network architecture that combines the advantages of LiFi and WiFi technologies. In this network, LiFi and WiFi work together to provide users with a seamless connection experience, taking advantage of LiFi's high speed and anti-interference characteristics while retaining WiFi's wide coverage advantage. It can dynamically switch between LiFi and WiFi according to environmental conditions and user needs, improving overall network capacity and reliability. Hybrid LiFi and WiFi networks that support MPTCP combine MPTCP with hybrid LiFi and WiFi networks, and can simultaneously use LiFi and WiFi links to transmit data, maximizing throughput, and seamlessly switching to WiFi networks when LiFi signals are blocked to ensure connection continuity. It dynamically adjusts traffic distribution according to network conditions, optimizes transmission performance, and improves the robustness and user experience of the overall network.

[0100] In order to adapt to the high temperature and harsh environmental conditions of industrial manufacturing plants, the network hardware used in the above method is made of high temperature and corrosion resistant materials, and adopts a sealed and waterproof design. Before the network hardware is deployed, an environmental impact assessment is conducted to determine the best installation location for the network hardware to avoid high temperature areas and potential physical damage.

[0101] Since industrial manufacturing plants involve sensitive production data, it is necessary to strengthen encryption measures for data transmission, implement end-to-end encryption, use the Advanced Encryption Standard (AES) to ensure the security of data during transmission, configure firewalls and intrusion detection systems to prevent unauthorized access, and prevent the leakage of sensitive data.

[0102] In S2, based on actual production needs, the administrator formulates the target product on the user side, for example, steel. Among them, the historical production environment data refers to the values ​​of various production environment conditions of the target product recorded in the past preset time, such as temperature, humidity, equipment status, etc. The historical raw material data refers to the proportion of each raw material used to produce the target product in the past preset time. The industrial equipment module is a collection of various industrial equipment that records historical production environment data and historical raw material data. Using the established communication network and user side, the administrator transmits the target product to the industrial equipment module, and the industrial equipment module uses the communication network to transmit the historical production environment data and historical raw material data of the target product to the processing center.

[0103] In S3, the administrator transmits the historical observation values ​​of the designated production indicators of the target product to the processing center through the user terminal, or sets up a storage in the processing center, and the storage is used to store and record the historical observation values ​​of the designated production indicators of the target product.

[0104] In S4, refer to Figure 3 The flowchart of the correlation analysis method for production environment conditions is shown, and "screening out production environment conditions related to the specified production indicators of the target product" includes the following steps:

[0105] S411: Obtaining a correlation coefficient between the production environment conditions and the specified production index of the target product, recorded as a first correlation coefficient;

[0106] S412: Obtaining an absolute value of a first correlation coefficient, recorded as a first absolute value;

[0107] S413: Determine whether the first absolute value is greater than a first preset threshold; if so, execute S414; if not, execute S415;

[0108] S414: Determine whether the production environment condition is related to the specified production index of the target product;

[0109] S415: Determine that the production environment condition is not related to the specified production index of the target product, and screen out the production environment condition.

[0110] Among them, the correlation coefficient between the i-th production environment condition and the specified production index of the target product is The calculation formula is as follows:

[0111]

[0112] In formula 1, E i is the i-th production environment condition when producing the target product; Z is the specified production index of the target product; E it is the value of the i-th production environment condition when producing the target product recorded at the observation time point t; is the average value of the i-th production environment condition when producing the target product at all observation time points T; Z t is the actual observed value of the specified production indicator of the target product recorded at the observation time point t (e.g. the actual output of steel at the observation time point t); It is the average value of the specified production index of the target product recorded at all observation time points T (for example, the average output of steel at all observation time points T); T is the total number of observation time points, indicating the time span of the historical data set.

[0113] Among them, refer to Figure 4 The flow chart of the correlation analysis method for raw materials is shown, "Screening out raw materials related to the specified production indicators of the target product" includes the following steps:

[0114] S421: Obtain the correlation coefficient between the raw material and the specified production index of the target product, recorded as the second correlation coefficient;

[0115] S422: Obtaining an absolute value of a second correlation coefficient, recorded as a second absolute value;

[0116] S423: Determine whether the second absolute value is greater than a second preset threshold; if so, execute S424; if not, execute S425;

[0117] S424: Determine whether the raw material is related to the specified production index of the target product;

[0118] S425: Determine that the raw material is not relevant to the specified production index of the target product, and screen out the raw material.

[0119] Among them, the correlation coefficient between the jth raw material and the specified production index of the target product is The calculation formula is as follows:

[0120]

[0121] In formula 2, E j The jth raw material used to produce the target product; E jt is the proportion of the jth raw material in the production of the target product recorded at the observation time point t; is the average value of the proportion of the jth raw material in the production of the target product recorded at all observation time points T.

[0122] In S5, the objective function is:

[0123]

[0124] The constraints are:

[0125] L m ≤E m ≤U m , for all m=1,...,M;(Formula 4)

[0126]

[0127] L n ≤E n ≤U n , for all n=1,...,N; (Formula 6)

[0128] Y min ≤Y≤Y max ;(Formula 7)

[0129] In formula 3-7, Y is the expected value of the specified production index of the target product; β0 is the intercept term; E m is the value of the mth production environment condition related to the specified production index of the target product; M is the total number of types of production environment conditions related to the specified production index of the target product; is the regression coefficient of the mth production environment condition related to the specified production index of the target product (obtained according to regression analysis); E n is the proportion of the nth raw material related to the specified production index of the target product; N is the total number of raw material types related to the specified production index of the target product; is the regression coefficient of the nth raw material related to the specified production index of the target product (obtained according to regression analysis); ε is the error term; L m is the lower limit of the mth production environment condition related to the specified production index of the target product; U m L is the upper limit of the mth production environment condition related to the specified production index of the target product; n is the lower limit of the proportion of the nth raw material related to the specified production index of the target product; U nY is the upper limit of the proportion of the nth raw material related to the specified production index of the target product; min Y is the expected lower limit of the specified production index of the target product; max Y is the expected upper limit of the specified production index of the target product; min and Y max It constitutes the expected numerical range of the specified production indicators of the target product, for example, the daily output of steel reaches 100 to 150 tons.

[0130] In S6, an optimization algorithm (such as linear programming, Lagrange multiplier method, etc.) is used to solve the objective function while satisfying the constraints to obtain ideal production environment data and ideal raw material data. The ideal production environment data is the ideal value of the production environment conditions related to the specified production indicators of the target product, and the ideal raw material data is the ideal proportion of raw materials related to the specified production indicators of the target product.

[0131] Furthermore, when producing the target product, by-products are usually generated. After calculating the ideal production environment data and the ideal raw material data, the values ​​of various production indicators of the by-products can be predicted based on these data, such as the expected daily output, expected efficiency, scrap rate, etc. of the by-products. If the user is not satisfied with the values ​​of the various production indicators of the by-products, the ideal production environment data and the ideal raw material data can be changed by adjusting the expected numerical range of the specified production indicators of the target product, and then the values ​​of the various production indicators of the by-products can be re-predicted. These steps are repeated until the values ​​of the specified production indicators of the target product and the values ​​of the various production indicators of the by-products meet the user's needs.

[0132] In S7, during the intelligent manufacturing upgrade process, an efficient visualization rendering engine is designed to display the ideal production environment data and raw material data in an intuitive way in the display center. The engine adopts a responsive layout to ensure the best visual effect on display devices of different sizes. By integrating multiple chart types such as line charts, scatter charts, heat maps, and interactive data exploration tools, users can easily drill, filter, and aggregate data. The development of these tools, based on the stable transmission capacity of the communication network, ensures unimpeded information flow between the data processing center and the display center, thereby realizing real-time visualization and in-depth analysis of production data.

[0133] Furthermore, through LiFi transmitters and / or WiFi access points, as well as LiFi receivers and / or WiFi client devices (WiFi client devices include smartphones, tablets, laptops, etc., which have built-in WiFi receivers and act as receiving ends, capable of receiving and decoding signals from WiFi access points), it is possible to transmit historical production environment data and historical raw material data from the industrial equipment module to the processing center, transmit ideal production environment data and ideal raw material data from the processing center to the display center, and transmit other data.

[0134] Special equipment or materials in industrial manufacturing plants may generate electromagnetic interference, affecting the use of communication networks, thereby affecting the transmission of various data such as historical production environment data, historical raw material data, ideal production environment data, and ideal raw material data. In order to cope with the complex electromagnetic environment of industrial manufacturing plants, refer to Figure 5 The flow chart of the communication network anti-interference method is shown, and the method disclosed in the present invention also includes: in the communication network, historical production environment data, historical raw material data, ideal production environment data, and ideal raw material data are transmitted in the form of signals, and the transmitted signals are adjusted to optimize signal transmission, specifically including the following steps:

[0135] S81: construct an optimization problem, and obtain an estimated value of a channel matrix based on the optimization problem;

[0136] S82: Acquire a precoding matrix based on an estimated value of the channel matrix;

[0137] S83: Adjust the transmitted signal based on the precoding matrix.

[0138] In S81, the optimization problem is expressed as follows:

[0139]

[0140] In Formula 8, h is the sparse representation of the channel; is a dictionary matrix. In the field of compressed sensing and sparse signal processing, the dictionary matrix is ​​used to represent the possible sparse basis or atomic set of the signal; ∈ is the fault tolerance measure; ‖·‖1 is the L1 norm; ‖·‖2 is the L2 norm. y is a discretized received signal vector, which is composed of signal samples received at different time points or frequency points. In a communication network, the received signal vector is a set of discrete signal samples obtained by transmitting a pilot signal through a channel and collecting it at the receiving end. This vector contains information about the channel response and noise, and is used for channel estimation and subsequent signal processing. Among them, the pilot signal is a known signal pattern used for channel estimation and signal synchronization in a communication network. In a communication network, pilot signals are usually periodically inserted into the transmitted data stream because they carry important information about the signal transmission characteristics.

[0141] Use the greedy algorithm (orthogonal matching pursuit (OMP)) or convex optimization algorithm to solve the above optimization problem and obtain the estimated sparse channel vector The sparse channel vector Converted to an estimate of the channel matrix

[0142] Using the above channel estimation algorithm, the output is the estimated value of the channel matrix, which contains information about all signal propagation paths from the transmitter (the transmitter refers to the LiFi transmitter and / or WiFi access point) to the receiver (the receiver refers to the LiFi receiver and / or WiFi client device).

[0143] In S82, if maximum ratio combining (MRC) precoding is adopted, the expression of the precoding matrix P is as follows:

[0144]

[0145] In Formula 9, is the conjugate transpose of the estimate of the channel matrix.

[0146] If zero-forcing (ZF) precoding is used, the expression of the precoding matrix P is as follows:

[0147]

[0148] In formula 10, is the pseudo-inverse of the channel matrix.

[0149] In an industrial manufacturing environment where interference reduction is required, precoding is forced by selecting zero.

[0150] In S83, the transmitted signal refers to a signal generated by a LiFi transmitter and / or a WiFi access point, which contains data to be transmitted to a LiFi receiver and / or a WiFi client device. The transmitted signal is denoted as x, and the transmitted signal x is adjusted using a precoding matrix to obtain a new signal s, s=Px.

[0151] The new signal s is transmitted through the antenna array, and the precoding matrix adjusts the transmission direction and shape of the signal according to the channel matrix to achieve beamforming to optimize the power concentration of the signal in the desired direction. Through precoding processing, interference between users can be reduced and noise can be suppressed to improve the transmission quality of the signal. In other words, the precoding matrix can dynamically adjust the transmitted signal according to channel conditions and user needs to overcome the challenges brought by the complex electromagnetic environment of industrial manufacturing plants.

[0152] Furthermore, the realization of cell-free massive MIMO technology involves designing and deploying massive antenna arrays, developing efficient channel estimation algorithms, implementing advanced precoding techniques, and designing distributed collaboration mechanisms. In industrial manufacturing plants, multiple massive MIMO antenna arrays are installed on the ceiling and walls of the factory, each array contains 128 antenna elements, and a planar array design is used to achieve beamforming and comprehensive coverage in three-dimensional space.

[0153] The deep learning-based channel estimation algorithm implemented in this disclosure uses sensor data in the factory environment as auxiliary information to quickly and accurately estimate the current channel state and adapt to the complex and changeable electromagnetic environment inside the factory. At the same time, a distributed precoding solution is developed, where each antenna array is equipped with an edge computing unit, connected through a high-speed optical fiber network, sharing information to coordinate precoding strategies, and using an algorithm based on a graph neural network to quickly calculate the optimal precoding matrix.

[0154] In addition, a seamless switching mechanism is implemented to support mobility within the factory, predict equipment movement paths, and prepare resource allocation in advance to ensure uninterrupted communication. The intelligent interference management system uses machine learning algorithms to analyze historical data, predict potential sources of interference, and dynamically adjust beam direction and power allocation to minimize the impact of interference. The adaptive multi-user scheduling algorithm considers the service quality requirements of different devices and combines the current network load and channel conditions to optimize resource allocation in real time.

[0155] Through this cell-free massive MIMO system, industrial manufacturing plants are able to support a large number of concurrent wireless connections, achieve high network capacity and extremely low latency, maintain ultra-high reliability, and provide a powerful communication infrastructure for smart manufacturing.

[0156] Optionally, a visual large-screen data management system can be designed. First, focus on building an efficient and reliable data acquisition and preprocessing module. The core is to develop a multi-source data acquisition interface to ensure compatibility and support for various industrial protocols to achieve extensive data collection. Subsequently, real-time data cleaning and standardization processing are implemented to ensure data accuracy and consistency. Further design data compression and caching mechanisms to improve data processing efficiency and response speed.

[0157] Enter the data analysis and mining engine development phase, implement a time series-based anomaly detection algorithm to promptly detect and respond to any abnormalities in the production process. In addition, develop a predictive maintenance model that uses the equipment's historical operating data to predict potential failures, thereby enabling advance maintenance and reducing the risk of unexpected downtime. Finally, design a multidimensional data association analysis algorithm to deeply explore the inherent connections between different production parameters and reveal potential production optimization opportunities.

[0158] Through these technical measures, the visual large-screen data management system provides a powerful data support platform for industrial manufacturers, helping enterprises to make data-driven decisions, optimize production processes, and improve production efficiency and product quality.

[0159] It is also possible to form a time series by collecting real-time data from key production equipment (such as blast furnaces), including multi-dimensional parameters such as temperature, pressure, air supply volume, and gas composition. The long short-term memory network (LSTM) training algorithm is used to learn the normal change pattern of equipment parameters and predict the parameter values ​​at future time points. In actual operation, the algorithm compares the predicted value with the actual value. If the deviation exceeds the preset threshold, an abnormal alarm is triggered, and the operator is notified in time to make adjustments to prevent production accidents.

[0160] For key equipment in the factory, such as rolling mills, a predictive maintenance model is developed based on the equipment's historical operating data, maintenance records, and fault logs. The model is built using machine learning algorithms such as random forests, and the input features include the equipment's operating time, load, vibration data, temperature, and the last maintenance time. The model output is a probability prediction of equipment failure in the future, allowing the maintenance team to arrange maintenance in advance based on the prediction results, replace potential problem parts, and avoid unexpected downtime and losses.

[0161] Through these technical measures, industrial manufacturing plants can achieve real-time monitoring and predictive maintenance of the production process, improve production efficiency and equipment reliability, reduce operating costs, and optimize the overall production process.

[0162] In the process of building the technology implementation of intelligent manufacturing system, system integration and optimization are the key links to ensure the efficient and stable operation of the entire platform. First, by integrating the network, data processing and visualization modules, the communication interface between modules is designed to ensure the smooth transmission of data flow and realize a unified system configuration and management interface. At the same time, the system monitoring and logging functions are developed to provide support for fault diagnosis and performance optimization.

[0163] In the performance testing and optimization phase, we designed comprehensive test cases covering various work scenarios, and performed stress tests to evaluate the system's performance under high load. Based on the test results, we carried out targeted system optimization, including implementing load balancing and adjusting cache strategies. In terms of security, enhancements included implementing end-to-end data encryption mechanisms to protect sensitive information, designing a multi-factor authentication system to strengthen access control, and developing intrusion detection and defense modules to monitor and respond to security threats in real time.

[0164] During the deployment and maintenance phase, develop deployment strategies, evaluate the existing environment, design the optimal hardware layout plan, and develop a phased deployment plan to reduce the impact on production. Prepare detailed deployment documents and training materials to ensure smooth system deployment and debugging. Install hardware equipment, configure network parameters, import initial data, and verify data management and visualization functions. Establish a performance monitoring mechanism, regularly evaluate system operation status, collect user feedback, and continuously improve system functions and user experience. Develop a regular maintenance plan, including software updates, hardware inspections, and security audits, to ensure long-term stable operation of the system.

[0165] Through this series of meticulous technical steps, the intelligent manufacturing system can achieve efficient data processing, stable performance and strong security, providing reliable intelligent solutions for industrial manufacturers.

[0166] Reference Figure 6 The present disclosure provides an intelligent industrial production environment optimization system, comprising:

[0167] A network building module 101 is used to build a communication network;

[0168] The data module module 102 is used to obtain the target product, obtain the historical production environment data and historical raw material data of the target product from the industrial equipment module based on the communication network, and transmit it to the processing center;

[0169] An observation value acquisition module 103 is used to acquire historical observation values ​​of a specified production indicator of a target product;

[0170] A correlation analysis module 104 is used for the processing center to perform correlation analysis on various production environment conditions and various raw materials based on historical observation values, and screen out production environment conditions and raw materials related to the specified production indicators of the target product;

[0171] Function construction module 105, used to construct objective function and constraint conditions based on the expected value range of the specified production index of the target product, the production environment conditions and raw materials related to the specified production index of the target product;

[0172] Function solving module 106, used to solve the objective function according to the constraint conditions to obtain ideal production environment data and ideal raw material data;

[0173] A data display module 107, used to transmit the ideal production environment data and the ideal raw material data to the display center for data display based on the communication network;

[0174] Among them, historical production environment data refers to the values ​​of various production environment conditions of the target products recorded within the past preset time; historical raw material data refers to the proportion of each raw material used in the production of the target products within the past preset time; ideal production environment data is the ideal value of the production environment conditions related to the specified production indicators of the target products; ideal raw material data is the ideal proportion of raw materials related to the specified production indicators of the target products.

[0175] The various variations and specific examples of the intelligent industrial production environment optimization method provided above are also applicable to the intelligent industrial production environment optimization system provided in the present invention. Through the above detailed description of the intelligent industrial production environment optimization method, those skilled in the art can clearly know the implementation method of the intelligent industrial production environment optimization system. For the sake of brevity of the specification, it will not be described in detail here.

[0176] The computer device according to the embodiment of the present disclosure includes a memory and a processor. The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may, for example, include a read-only memory (ROM), a hard disk, a flash memory, etc.

[0177] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the computer device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory, so that the computer device performs all or part of the steps of the intelligent industrial production environment optimization method of each embodiment of the present disclosure.

[0178] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0179] like Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure is shown, which is a schematic diagram of the structure of a computer device suitable for implementing the embodiment of the present disclosure. Figure 7 The computer device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0180] like Figure 7 As shown, the computer device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the computer device are also stored. The processor, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0181] Typically, the following devices can be connected to the I / O interface: input devices such as sensors or visual information acquisition devices; output devices such as display screens; storage devices such as tapes, hard disks, etc.; and communication devices. The communication device can allow the computer device to communicate with other devices (such as edge computing devices) wirelessly or by wire to exchange data. Figure 7 A computer device having various devices is shown, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0182] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of the intelligent industrial production environment optimization method of the embodiment of the present disclosure are executed.

[0183] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0184] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the intelligent industrial production environment optimization method of each embodiment of the present disclosure are executed.

[0185] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0186] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0187] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0188] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0189] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0190] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0191] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0192] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0193] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for optimizing an intelligent industrial production environment, characterized in that: include: Build a communication network; Obtain the target product, and based on the communication network, obtain historical production environment data and historical raw material data of the target product from the industrial equipment module, and transmit them to the processing center; Obtain historical observations of specified production indicators of target products; The processing center performs correlation analysis on various production environment conditions and various raw materials based on historical observation values, and screens out production environment conditions and raw materials that are related to the specified production indicators of the target product; Constructing an objective function and constraint conditions based on an expected numerical range of a specified production indicator of the target product, production environment conditions and raw materials related to the specified production indicator of the target product; Solving the objective function according to the constraint conditions to obtain ideal production environment data and ideal raw material data; Transmitting the ideal production environment data and the ideal raw material data to a display center for data display based on the communication network; Among them, the historical production environment data refers to the values ​​of various production environment conditions of the target product recorded within the past preset time; the historical raw material data refers to the proportion of each raw material used in the production of the target product within the past preset time; the ideal production environment data is the ideal value of the production environment conditions related to the specified production indicators of the target product; the ideal raw material data is the ideal proportion of raw materials related to the specified production indicators of the target product.

2. The intelligent industrial production environment optimization method according to claim 1 is characterized in that: include: The production environment conditions are subjected to correlation analysis, specifically: Obtaining a first correlation coefficient between the production environment condition and a specified production index of a target product; Obtaining a first absolute value of the first correlation coefficient; Determining whether the first absolute value is greater than a first preset threshold; If so, determining that the production environment condition is related to the specified production index of the target product; If not, it is determined that the production environment condition is not relevant to the specified production index of the target product, and the production environment condition is screened out.

3. The intelligent industrial production environment optimization method according to claim 2 is characterized in that: The correlation coefficient between the i-th production environment condition and the specified production index of the target product is: The calculation formula is as follows: Among them, E i is the i-th production environment condition when producing the target product; Z is the specified production index of the target product; E it is the value of the i-th production environment condition when producing the target product recorded at the observation time point t; is the average value of the i-th production environment condition when producing the target product at all observation time points T; Z t is the actual observed value of the specified production indicator of the target product recorded at the observation time point t; It is the average value of the specified production index of the target product recorded at all observation time points T; T is the total number of observation time points.

4. The intelligent industrial production environment optimization method according to claim 3 is characterized in that: include: The raw materials are subjected to correlation analysis, specifically: Obtaining a second correlation coefficient between the raw material and a specified production index of the target product; Obtaining a second absolute value of the second correlation coefficient; Determining whether the second absolute value is greater than a second preset threshold; If so, it is determined that the raw material is related to the specified production index of the target product; If not, it is determined that the raw material is not relevant to the specified production index of the target product, and the raw material is screened out.

5. The intelligent industrial production environment optimization method according to claim 4 is characterized in that: The correlation coefficient between the jth raw material and the specified production index of the target product is The calculation formula is as follows: Among them, E j The jth raw material used to produce the target product; E jt is the proportion of the jth raw material in the production of the target product recorded at the observation time point t; is the average value of the proportion of the jth raw material in the production of the target product recorded at all observation time points T.

6. The intelligent industrial production environment optimization method according to claim 1, characterized in that: The objective function is: The constraints are: L m ≤E m ≤U m ,for all m=1,...,M; L n ≤E n ≤U n ,for all n=1,...,N; AND min ≤Y≤Y max ; Among them, Y is the expected value of the specified production index of the target product; β0 is the intercept term; E m is the value of the mth production environment condition related to the specified production index of the target product; M is the total number of types of production environment conditions related to the specified production index of the target product; is the regression coefficient of the mth production environment condition related to the specified production index of the target product; E n is the proportion of the nth raw material related to the specified production index of the target product; N is the total number of raw material types related to the specified production index of the target product; is the regression coefficient of the nth raw material related to the specified production index of the target product; ε is the error term; L m is the lower limit of the mth production environment condition related to the specified production index of the target product; U m L is the upper limit of the mth production environment condition related to the specified production index of the target product; n is the lower limit of the proportion of the nth raw material related to the specified production index of the target product; U n Y is the upper limit of the proportion of the nth raw material related to the specified production index of the target product; min Y is the expected lower limit of the specified production index of the target product; max Y is the expected upper limit of the specified production index of the target product; min and Y max It constitutes the expected numerical range of the specified production indicators of the target product.

7. The intelligent industrial production environment optimization method according to claim 1, characterized in that: In the communication network, the historical production environment data, the historical raw material data, the ideal production environment data, and the ideal raw material data are transmitted in the form of signals, and the transmitted signals are adjusted to optimize signal transmission, specifically including: Constructing an optimization problem, and obtaining an estimated value of a channel matrix based on the optimization problem; Acquire a precoding matrix based on an estimated value of the channel matrix; Based on the precoding matrix, a transmitted signal is adjusted.

8. A computer device, characterized in that: The computer device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the intelligent industrial production environment optimization method described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the intelligent industrial production environment optimization method described in any one of claims 1-7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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