Convenient food production equipment monitoring management method and system

Abstract

The invention relates to the technical field of intelligent monitoring of production equipment, in particular to a convenient food production equipment monitoring management method and system. The method comprises the following steps: collecting multi-source data in real time through a multi-dimensional sensor; preprocessing the collected original data; the running state of the equipment is monitored in real time; the abnormal state of the equipment operation state is identified, grading is carried out according to the abnormal severity, and early warning is carried out; generating a maintenance plan, and automatically matching a maintenance work order according to the early warning level; storing equipment operation data, maintenance records and fault cases in a cloud database; the system comprises a data acquisition module, a data processing module, an equipment monitoring module, an abnormity early warning module, a maintenance module and a storage module. Through the above mode, the abnormal state can be recognized in time, and the graded early warning is performed according to the abnormal severity, so that the maintenance personnel can preferentially process the fault which has great influence on the equipment, and the fault processing efficiency and pertinence are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent monitoring of production equipment, and in particular to a convenient food production equipment monitoring management method and system. BACKGROUND

[0002] With the vigorous development of the instant food industry, the production scale of instant food production enterprises is continuously expanding, and the complexity and automation level of production equipment are also increasing. In order to ensure the efficient and stable operation of the production process and improve product quality and production efficiency, it is crucial to monitor and maintain the instant food production equipment in real time and accurately. The traditional monitoring method of instant food production equipment mainly relies on manual regular inspection and simple fixed parameter monitoring. Manual inspection not only consumes a lot of manpower and time, but also is difficult to achieve real-time and comprehensive understanding of the running state of the equipment, and some potential fault hidden dangers are easily missed. At the same time, simple fixed parameter monitoring can only obtain limited running information of the equipment, and cannot comprehensively evaluate the overall running condition of the equipment, making it difficult to accurately identify and judge some complex fault modes.

[0003] With the rapid development of emerging technologies such as the Internet of Things, big data, and artificial intelligence, the use of sensors to collect various physical quantity information such as temperature, pressure, vibration, and speed during the running of the equipment provides a rich source of data for comprehensively and accurately understanding the running state of the equipment. Through real-time collection of multi-source data, detailed running information of the equipment under different working conditions can be obtained, providing a solid foundation for subsequent data analysis and fault diagnosis.

[0004] There are various abnormal states during the running of instant food production equipment, and the influence degree of each abnormal state is different. The existing technology can only realize single-dimensional abnormal alarm, and cannot distinguish the difference in the influence of different abnormal states on the running of the equipment, making it difficult for maintenance personnel to quickly identify high-priority faults, resulting in low fault handling efficiency and insufficient pertinence. SUMMARY

[0005] The present application aims to provide a convenient food production equipment monitoring management method and system, which aims to solve the technical problems that the existing instant food production equipment relies only on single-dimensional abnormal alarm, cannot distinguish the difference in the influence of different abnormal states on the running of the equipment, and makes it difficult for maintenance personnel to quickly identify high-priority faults, resulting in low fault handling efficiency and insufficient pertinence.

[0006] To achieve the above-mentioned purpose, a convenient food production equipment monitoring management method is adopted, which comprises the following steps: Deploying multi-dimensional sensors on the instant food production equipment and collecting multi-source data in real time through the multi-dimensional sensors; The collected raw data is pre-processed, and the data is standardized to obtain a standardized data set; The standardized data set is analyzed in real time, and the running state of the equipment is monitored in real time; Identify the abnormal state of the equipment running state, classify according to the severity of the anomaly, and give an early warning; Generate a maintenance plan and automatically match maintenance work orders according to the warning level; Store the equipment operation data, maintenance records, and fault cases in a cloud database for data traceability.

[0007] Among them, in the step of deploying multi-dimensional sensors on the instant food production equipment and collecting multi-source data in real time through the multi-dimensional sensors: Deploy current, torque, vibration, temperature, pressure, and material humidity sensors on instant food production core equipment to cover three types of monitoring dimensions: equipment operation, material state, and environmental conditions. Core equipment includes mixing, molding, and sterilization; Set the sensor collection frequency and dynamically adjust it according to the equipment working condition; Build a real-time data transmission channel and use 5G+ edge computing technology to transmit raw data collected by sensors to a data processing terminal in real time. Raw data includes time series data, state data, and environmental data; Establish a data collection verification mechanism to detect the integrity of transmitted data. Missing or abnormal data triggers the sensor to re-collect.

[0008] Among them, in the step of pre-processing the collected raw data and standardizing the data to obtain a standardized data set: Clean the raw data by removing noise data, missing values, and outliers. Use interpolation to fill in missing data and filter extreme outliers using the 3σ rule; Integrate scattered data from different sensors and different equipment according to timestamps and equipment numbers to form a unified data set; Use principal component analysis to extract key features and remove redundant data; Use Z-score standardization to convert the integrated data set to a standardized data set with a mean of 0 and a variance of 1, eliminating analysis bias caused by different parameter magnitude differences; Verify the consistency of the standardized data set to ensure that data from different batches and different equipment is comparable and meets the analysis requirements.

[0009] Among them, in the step of real-time analysis of the standardized data set and real-time monitoring of the running state of the equipment: A normal operation benchmark model is constructed, a normal operation benchmark threshold interval under different equipment and different working conditions is established based on historical standardized data, combined with equipment process parameters and rated operation range; The real-time standardized data is dynamically compared with the benchmark model, and the characteristic parameters of data deviation and change trend slope are calculated; A correlation analysis model is established in combination with the synergistic relationship of equipment operation parameters, material state parameters and environmental parameters to identify hidden problems of single parameter normality but multiple parameter synergy abnormality; According to the comparison results and the synergy analysis conclusion, the equipment operation state is output in real time, including normal, potential abnormality and explicit abnormality.

[0010] Among the steps of identifying the abnormal state of the equipment operation state, classifying according to the severity of the abnormality, and giving a warning: For the abnormal state determined by real-time analysis, the key features of abnormal occurrence time, abnormal parameter type, deviation amplitude and duration are extracted; Based on the influence degree of the abnormality on production, four levels are divided, wherein the first level is slight abnormality, which does not affect production, the second level is general abnormality, which needs attention, the third level is serious abnormality, which affects product quality, and the fourth level is emergency abnormality, which may cause equipment shutdown; The extracted abnormal features are matched with the classification standard to automatically determine the abnormal level; According to the abnormal level, the corresponding warning mode is triggered, the first and second levels are prompted by system pop-up window, the third level is warned by short message + system notification, and the fourth level is warned by short message + telephone + system emergency pop-up window three times, and is pushed to the equipment administrator and the production responsible person at the same time.

[0011] Among the steps of generating a maintenance plan and automatically matching a maintenance work order according to the warning level: Based on the equipment model, the abnormal type, the abnormal level, a maintenance knowledge base including maintenance process, required tools, consumable list, and maintenance duration is established; According to the abnormal level and the equipment operation state, a targeted maintenance plan is automatically generated, which clearly defines the maintenance time, maintenance content and responsible person; Based on the maintenance plan and the maintenance knowledge base, the corresponding standard maintenance work order is automatically matched, which includes the key contents of filling work order number, equipment information, abnormal description and maintenance requirements; Manual adjustment of the automatically generated work order according to the actual working condition is supported, and the adjusted work order is updated to the maintenance plan and the cloud database at the same time; The matched maintenance work order is pushed to the designated maintenance personnel, and the execution progress of the work order is tracked in real time, including unexecuted, in execution and completed.

[0012] In the step of storing the equipment operation data, maintenance records and fault cases in the cloud database storage, the data traceability is realized. The distributed cloud database is adopted, and the equipment operation data, maintenance records, fault cases and standardized data sets are divided into four data partitions for classified storage. The real-time acquisition data, standardized data, abnormal analysis results, maintenance work orders and execution records are synchronously stored in the cloud database. Multi-dimensional indexes are established for the stored data, and the indexes include equipment number, time range, data type and abnormal level. The equipment historical operation data, maintenance records and fault handling cases are queried through the conditions of equipment number, time interval and abnormal type, so that a complete data traceability chain is formed. A regular data backup mechanism is established, and the data backup includes daily incremental backup and weekly full backup, and the fault case library and maintenance knowledge base are dynamically updated.

[0013] The application also provides a convenient food production equipment monitoring and management system, which comprises a data acquisition module, a data processing module, an equipment monitoring module, an abnormal early warning module, a maintenance module and a storage module. The data acquisition module is used for deploying multi-dimensional sensors on the convenient food production equipment, and collecting multi-source data in real time through the multi-dimensional sensors. The data processing module is used for pre-processing the collected raw data, and performing standardized processing on the data to obtain a standardized data set. The equipment monitoring module is used for real-time analysis of the standardized data set, and real-time monitoring of the running state of the equipment. The abnormal early warning module is used for identifying the abnormal state of the equipment running state, classifying according to the abnormal severity, and performing early warning. The maintenance module is used for generating a maintenance plan, and automatically matching a maintenance work order according to the warning level. The storage module is used for storing the equipment operation data, maintenance records and fault cases in the cloud database storage, and realizing data traceability.

[0014] The application discloses a convenient food production equipment monitoring management method and system. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0016] Figure 1 is a step flow chart of the convenient food production equipment monitoring management method of the present application.

[0017] Figure 2 is a step flow chart of S100 of the present application.

[0018] Figure 3 is a step flow chart of S200 of the present application.

[0019] Figure 4 is a step flow chart of S300 of the present application.

[0020] Figure 5 is a step flow chart of S400 of the present application.

[0021] Figure 6 is a step flow chart of S500 of the present application.

[0022] Figure 7 is a step flow chart of S600 of the present application.

[0023] Figure 8 is a structure principle diagram of the convenient food production equipment monitoring management system of the present application.

[0024] 701 - data acquisition module, 702 - data processing module, 703 - equipment monitoring module, 704 - abnormal early warning module, 705 - maintenance module, 706 - storage module. DETAILED DESCRIPTION

[0025] The exemplary embodiments will be described in detail herein with reference to the accompanying drawings. In the following description, the same numbers refer to the same elements throughout the drawings. The embodiments described in the following exemplary embodiments are not meant to represent all embodiments consistent with the present application.

[0026] The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the description of the application and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0027] It will be understood that, although the terms first, second, third, etc. can be used herein to describe various information, these terms are not intended to denote a temporal or chronological order. Rather, these terms are used only to distinguish one from another. For example, a first information can be termed a second information, and similarly, a second information can also be termed a first information, without departing from the scope of the present application. Depending on the context, the word "if' as used herein can be interpreted to mean "when" or "in response to determining".

[0028] Referring to Figures 1-7 The present application provides a convenient food production equipment monitoring management method, comprising the following steps: S100: deploying multi-dimensional sensors on the instant food production equipment, and collecting multi-source data in real time through the multi-dimensional sensors.

[0029] In this embodiment, multi-dimensional sensors are deployed on the instant food production equipment, and multi-source data is collected in real time through the multi-dimensional sensors. The specific process is as follows: S101: deploying current, torque, vibration, temperature, pressure, and material humidity sensors on the core equipment of instant food production, covering three types of monitoring dimensions of equipment operation, material state, and environmental conditions, and the core equipment includes stirring, molding, and sterilization; S102: setting the sensor collection frequency and dynamically adjusting it according to the equipment working condition; S103: building a real-time data transmission channel, using 5G+edge computing technology to transmit the raw data collected by the sensors to the data processing terminal in real time, and the raw data includes time series data, state data, and environmental data; S104: Establish a data collection verification mechanism to detect the integrity of the transmitted data, and trigger the sensor to re-collect when the data is lost or abnormal.

[0030] In the above process, first, deploy current, torque, vibration, temperature, pressure, and material humidity sensors on the core equipment of the convenience food production, covering three types of monitoring dimensions of equipment operation, material state, and environmental conditions, including stirring, molding, and sterilization. Set the sensor collection frequency and dynamically adjust it according to the equipment working condition. Build a real-time data transmission channel, use 5G+ edge computing technology, and transmit the raw data collected by the sensor to the data processing terminal in real time. The raw data includes time series data, state data, and environmental data. Then, establish a data collection verification mechanism to detect the integrity of the transmitted data, and trigger the sensor to re-collect when the data is lost or abnormal.

[0031] S200: Preprocess the collected raw data and standardize the data to obtain a standardized data set.

[0032] In this embodiment, the collected raw data is preprocessed and standardized to obtain a standardized data set. The specific process is as follows: S201: Clean the raw data, remove noise data, missing values, and abnormal values in the raw data, use interpolation method to supplement missing data, and filter extreme abnormal values through 3σ criterion; S202: Associate and integrate the dispersed data of different sensors and different equipment according to time stamp and equipment number to form a unified format data set; S203: Use principal component analysis method to extract key features and remove redundant data; S204: Use Z-score standardization method to convert the integrated data set to standardized data with mean value of 0 and variance of 1, and eliminate the analysis deviation caused by different parameter magnitude differences; S205: Verify the consistency of the standardized data set to ensure that the data of different batches and different equipment are comparable and meet the analysis requirements.

[0033] In the above process, first, the original data is cleaned to eliminate noise data, missing values and outliers in the original data, the interpolation method is used to supplement the missing data, and the 3σ criterion is used to filter extreme outliers; then the dispersed data of different sensors and different devices are associated and integrated according to the time stamp and device number to form a unified format data set; then the principal component analysis method is used to extract key features and eliminate redundant data; then the Z-score standardization method is used to convert the integrated data set into standardized data with a mean of 0 and a variance of 1, eliminating the analysis bias caused by the difference in the magnitude of different parameters; and the consistency of the standardized data set is verified to ensure that the data of different batches and different devices are comparable and meet the analysis requirements.

[0034] S300: Real-time analysis of the standardized data set to monitor the running state of the equipment in real time.

[0035] In the present embodiment, the standardized data set is analyzed in real time to monitor the running state of the equipment in real time, and the specific process is as follows: S301: Constructing a normal running benchmark model of the equipment, establishing a normal running benchmark threshold interval of different equipment and different working conditions based on historical standardized data, combining with equipment process parameters and rated running range; S302: Dynamically comparing the real-time standardized data with the benchmark model to calculate the characteristic parameters of data deviation and change trend slope; S303: Establishing a correlation analysis model in combination with the synergistic relationship of equipment running parameters, material state parameters and environmental parameters to identify hidden problems of single parameter normal but multiple parameter synergistic abnormal; S304: Real-time output of the running state of the equipment according to the comparison results and synergistic analysis conclusions, including normal, potential abnormal and explicit abnormal.

[0036] In the above process, first, a normal running benchmark model of the equipment is constructed, a normal running benchmark threshold interval of different equipment and different working conditions is established based on historical standardized data, combining with equipment process parameters and rated running range; then the real-time standardized data is dynamically compared with the benchmark model to calculate the characteristic parameters of data deviation and change trend slope; then a correlation analysis model is established in combination with the synergistic relationship of equipment running parameters, material state parameters and environmental parameters to identify hidden problems of single parameter normal but multiple parameter synergistic abnormal; subsequently, the running state of the equipment is output in real time according to the comparison results and synergistic analysis conclusions, including normal, potential abnormal and explicit abnormal.

[0037] S400: Identifying the abnormal state of the running state of the equipment, grading according to the severity of the abnormality, and giving a warning.

[0038] In the embodiment, the abnormal state of the equipment operation state is identified, the abnormality severity is graded, and a warning is given, and the specific process is as follows: S401: For the abnormal state determined by real-time analysis, the key features of abnormal occurrence time, abnormal parameter type, deviation amplitude, and duration are extracted; S402: Based on the influence degree of the abnormality on production, four levels are divided, wherein level one: slight abnormality, no impact on production, level two: general abnormality, need attention, level three: serious abnormality, impact on product quality, and level four: emergency abnormality, may cause equipment shutdown; S403: The extracted abnormality features are matched with the grading standard to automatically determine the abnormality level; S404: According to the abnormality level, the corresponding warning mode is triggered, level one and level two are prompted by system pop-up window, level three is warned by SMS + system notification, and level four is warned by SMS + telephone + system emergency pop-up window, and is simultaneously pushed to the equipment administrator and the production responsible person.

[0039] In the above process, first, for the abnormal state determined by real-time analysis, the key features of abnormal occurrence time, abnormal parameter type, deviation amplitude, and duration are extracted; then, based on the influence degree of the abnormality on production, four levels are divided, wherein level one: slight abnormality, no impact on production, level two: general abnormality, need attention, level three: serious abnormality, impact on product quality, and level four: emergency abnormality, may cause equipment shutdown; then, the extracted abnormality features are matched with the grading standard to automatically determine the abnormality level; subsequently, according to the abnormality level, the corresponding warning mode is triggered, level one and level two are prompted by system pop-up window, level three is warned by SMS + system notification, and level four is warned by SMS + telephone + system emergency pop-up window, and is simultaneously pushed to the equipment administrator and the production responsible person.

[0040] S500: Maintenance plan is generated, and maintenance work order is automatically matched according to the warning level.

[0041] In the embodiment, the maintenance plan is generated, and the maintenance work order is automatically matched according to the warning level, and the specific process is as follows: S501: Based on the equipment model, the abnormality type, and the abnormality level, a maintenance knowledge base containing maintenance process, required tools, consumable list, and maintenance duration is established; S502: According to the abnormality level and the equipment operation state, a targeted maintenance plan is automatically generated, and the maintenance time, maintenance content, and responsible person are specified; S503: Based on the maintenance plan and the maintenance knowledge base, the corresponding standard maintenance work order is automatically matched, and the key content of the work order includes filling work order number, equipment information, abnormality description, and maintenance requirements; S504: Support manual fine-tuning of automatically generated work orders according to actual working conditions, and update the maintenance plan and cloud database after adjustment. S505: Push the completed maintenance work order to the designated maintenance personnel, and track the execution progress in real time, including unexecuted, in execution, and completed.

[0042] In the above process, first, based on the equipment model, abnormal type, abnormal level, a maintenance knowledge base containing maintenance process, required tools, consumables list, and maintenance duration is established; then according to the abnormal level and the equipment running state, the targeted maintenance plan is automatically generated, and the maintenance time, maintenance content, and responsible person are clear; then based on the maintenance plan and the maintenance knowledge base, the corresponding standard maintenance work order is automatically matched, the work order includes the key content of filling work order number, equipment information, abnormal description, and maintenance requirements; then support manual fine-tuning of automatically generated work orders according to actual working conditions, and update the maintenance plan and cloud database after adjustment; then push the completed maintenance work order to the designated maintenance personnel, and track the execution progress in real time, including unexecuted, in execution, and completed.

[0043] S600: Store the equipment running data, maintenance records, and fault cases in the cloud database storage, and trace the data.

[0044] In this embodiment, the equipment running data, maintenance records, and fault cases are stored in the cloud database storage, and the data is traced, the specific process is: S601: Use a distributed cloud database to divide the equipment running data, maintenance records, fault cases, and standardized data sets into four data partitions, and store the data in categories; S602: Real-time synchronization of real-time data collection, standardized data, abnormal analysis results, maintenance work orders, and execution records to the cloud database; S603: Establish multi-dimensional index for stored data, including equipment number, time range, data type, and abnormal level; S604: Query the equipment historical running data, maintenance records, and fault handling cases through the conditions of equipment number, time interval, and abnormal type, and form a complete data trace chain; S605: Establish a regular data backup mechanism, including daily incremental backup and weekly full backup, and dynamically update the fault case library and maintenance knowledge base.

[0045] In the above process, first, a distributed cloud database is used to divide four data partitions of device operation data, maintenance records, fault cases, and standardized data sets, and store the data classified; then real-time collected data, standardized data, abnormal analysis results, maintenance work orders, and execution records are synchronized to the cloud database in real time; multi-dimensional indexes are established for the stored data, including device number, time range, data type, and abnormal level; through the conditions of device number, time interval, and abnormal type, device historical operation data, maintenance records, and fault handling cases are queried to form a complete data trace chain; and a regular data backup mechanism is established, including daily incremental backup and weekly full backup, and the fault case library and maintenance knowledge base are dynamically updated.

[0046] Referring to Figure 8 The application also provides a convenient food production equipment monitoring management system, comprising a data acquisition module 701, a data processing module 702, an equipment monitoring module 703, an abnormal early warning module 704, a maintenance module 705, and a storage module 706; wherein: The data acquisition module 701 is used to deploy multi-dimensional sensors on the convenient food production equipment and collect multi-source data in real time through the multi-dimensional sensors. The data processing module 702 is used to pre-process the collected raw data and perform standardized processing on the data to obtain a standardized data set. The equipment monitoring module 703 is used to perform real-time analysis on the standardized data set and real-time monitoring on the running state of the equipment. The abnormal early warning module 704 is used to identify the abnormal state of the running state of the equipment, grade according to the severity of the abnormality, and perform early warning. The maintenance module 705 is used to generate a maintenance plan and automatically match a maintenance work order according to the warning level. The storage module 706 is used to store the device operation data, maintenance records, and fault cases in a cloud database storage for data trace.

[0047] In this embodiment, the data acquisition module 701 deploys multi-dimensional sensors on the convenient food production equipment and collects multi-source data in real time through the multi-dimensional sensors; the data processing module 702 pre-processes the collected raw data and performs standardized processing on the data to obtain a standardized data set; the equipment monitoring module 703 performs real-time analysis on the standardized data set and real-time monitoring on the running state of the equipment; the abnormal early warning module 704 identifies the abnormal state of the running state of the equipment, grades according to the severity of the abnormality, and performs early warning; the maintenance module 705 generates a maintenance plan and automatically matches a maintenance work order according to the warning level; and the storage module 706 stores the device operation data, maintenance records, and fault cases in a cloud database storage for data trace.

[0048] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the application embrace any and all variations of the present application that fall within the scope of the general inventive concept as defined in the claims and that the application may be practiced otherwise than is specifically described herein. For example, the present application can be used in conjunction with a variety of different types of devices and systems.

[0049] It is to be understood that the application is not limited to particular details described herein and / or illustrated in the drawings. Rather, the application can be practiced with variations that fall within the scope of the general inventive concept as defined in the claims.

Claims

1. A method of monitoring and managing a convenience food production facility, characterized by, The method comprises the following steps: Deploying multi-dimensional sensors on instant food production equipment and collecting multi-source data in real time through the multi-dimensional sensors; Preprocessing the collected raw data and standardizing the data to obtain a standardized data set; Real-time analysis of the standardized data set to monitor the running state of the equipment in real time; Identifying the abnormal state of the equipment running state, classifying according to the severity of the abnormality, and giving an early warning; Generating a maintenance plan and automatically matching a maintenance work order according to the warning level; Storing the equipment running data, maintenance records, and fault cases in a cloud database for data traceability.

2. The method of claim 1, wherein In the step of deploying multi-dimensional sensors on instant food production equipment and collecting multi-source data in real time through the multi-dimensional sensors: For instant food production core equipment, deploy current, torque, vibration, temperature, pressure, and material humidity sensors to cover three monitoring dimensions of equipment operation, material state, and environmental conditions. The core equipment includes stirring, molding, and sterilization. Set the sensor collection frequency and dynamically adjust it according to the equipment working condition. Build a real-time data transmission channel and use 5G+edge computing technology to transmit the raw data collected by the sensors to the data processing terminal in real time. The raw data includes time series data, state data, and environmental data. Establish a data collection verification mechanism to detect the integrity of the transmitted data. Missing or abnormal data triggers the sensor to re-collect.

3. The method of claim 1, wherein the method further comprises: In the step of preprocessing the collected raw data and standardizing the data to obtain a standardized data set: Clean the raw data by removing noise data, missing values, and outliers. Use interpolation method to supplement missing data and filter extreme outliers through 3σ rule. Integrate the scattered data from different sensors and different equipment according to the timestamp and equipment number to form a unified data set. Use principal component analysis to extract key features and remove redundant data. Use Z-score standardization method to convert the integrated data set to standardized data with mean value of 0 and variance of 1, eliminating analysis bias caused by different parameter magnitude differences. Verify the consistency of the standardized data set to ensure that the data from different batches and different equipment are comparable and meet the analysis requirements.

4. The method for monitoring and managing convenience food production equipment as described in claim 1, characterized in that, In the step of real-time analysis of the standardized data set to monitor the running state of the equipment in real time: Construct a normal operation benchmark model based on historical standardized data, combining equipment process parameters and rated operating range to establish normal operation threshold intervals for different equipment and different working conditions. Compare the real-time standardized data with the benchmark model dynamically to calculate the data deviation and change trend slope. Establish a correlation analysis model based on the synergistic relationship between equipment operating parameters, material state parameters, and environmental parameters to identify hidden problems where a single parameter is normal but multiple parameters are abnormal. According to the comparison results and the synergistic analysis conclusion, output the equipment running state in real time, including normal, potential abnormality, and explicit abnormality.

5. The method for monitoring and managing convenience food production equipment as described in claim 1, characterized in that, In the step of identifying the abnormal state of the equipment running state, classifying according to the severity of the abnormality, and giving an early warning: For real-time analysis of abnormal state, extract the key features of abnormal occurrence time, abnormal parameter type, deviation amplitude, and duration; Based on the impact of abnormality on production, divide it into four levels, among which the first level is slight abnormality, which does not affect production, the second level is general abnormality, which needs attention, the third level is serious abnormality, which affects product quality, and the fourth level is emergency abnormality, which may cause equipment downtime; Match the extracted abnormal features with the grading standards to automatically determine the abnormal level; According to the abnormal level, trigger the corresponding warning mode, the first and second levels are prompted by system pop-up window, the third level is warned by SMS + system notification, and the fourth level is warned by SMS + phone + system emergency pop-up window, and is pushed to the device administrator and production manager at the same time.

6. The method of monitoring and managing a convenience food production facility as claimed in claim 1, wherein, In the step of generating maintenance plan and automatically matching maintenance work order according to warning level: Based on device model, abnormal type and abnormal level, establish a maintenance knowledge base containing maintenance process, required tools, consumable list and maintenance duration; According to the abnormal level and the running state of the equipment, automatically generate targeted maintenance plan, and clearly define the maintenance time, maintenance content and responsible person; Based on maintenance plan and maintenance knowledge base, automatically match the corresponding standard maintenance work order, which includes filling in work order number, equipment information, abnormal description and maintenance requirements; Support manual fine-tuning of automatically generated work order according to actual working conditions, and update the maintenance plan and cloud database after adjustment; Push the matched maintenance work order to the designated maintenance personnel, and track the execution progress in real time, including unexecuted, in execution and completed.

7. The method of monitoring and managing a convenience food production facility as claimed in claim 1, wherein, In the step of storing device running data, maintenance record and fault case in cloud database: Use distributed cloud database to divide device running data, maintenance record, fault case and standardized data set into four data partitions for classified storage; Real-time acquisition data, standardized data, abnormal analysis results, maintenance work order and execution record are synchronized to the cloud database in real time; Establish multi-dimensional index for stored data, including equipment number, time range, data type and abnormal level; Query the historical running data, maintenance record and fault handling case of the equipment by equipment number, time interval and abnormal type, forming a complete data trace chain; Establish a regular data backup mechanism, including daily incremental backup and weekly full backup, and dynamically update the fault case library and maintenance knowledge base.

8. A convenient food production facility monitoring management system employing the convenient food production facility monitoring management method according to claim 1, characterized by, It includes data acquisition module, data processing module, equipment monitoring module, abnormal warning module, maintenance module and storage module; The data acquisition module is used to deploy multi-dimensional sensors on the convenience food production equipment, and to collect multi-source data in real time through multi-dimensional sensors; The data processing module is used to preprocess the collected raw data and standardize the data to obtain standardized data set; The equipment monitoring module is used to analyze the standardized data set in real time and monitor the running state of the equipment in real time; The abnormal warning module is used to identify the abnormal state of the equipment running state, classify according to the severity of the abnormality, and give warning; The maintenance module is used for generating a maintenance plan and automatically matching a maintenance work order according to an early warning level. The storage module is used for storing equipment operation data, maintenance records and fault cases in a cloud database storage and tracing data.

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