CN118734232A

CN118734232A - Digital intelligent workshop supervision method, system and equipment

Info

Publication number: CN118734232A
Application number: CN202411219484.9A
Authority: CN
Inventors: 史卓; 李震; 黎叶平; 周小华; 龚三元
Original assignee: Nanchang Zhongzhan Shuzhi Technology Co ltd
Current assignee: Nanchang Zhongzhan Shuzhi Technology Co ltd
Priority date: 2024-09-02
Filing date: 2024-09-02
Publication date: 2024-10-01

Abstract

The application is applicable to the technical field of workshop digital supervision, and particularly relates to a digital intelligent workshop supervision method, system and equipment, wherein the method comprises the following steps: monitoring a first operation; acquiring operation data of workshop equipment based on a first operation; and carrying out working condition analysis according to the operation data, and determining a workshop supervision method. The digital intelligent workshop supervision method provided by the application can solve the problem that the supervision effect is poor because the data collection, analysis and decision-making require time and cannot be monitored and adjusted in real time or near real time due to the fact that the supervision is performed manually in the workshop supervision process.

Description

Digital intelligent workshop supervision method, system and equipment

Technical Field

The application belongs to the technical field of workshop digital supervision, and particularly relates to a digital intelligent workshop supervision method, system and equipment.

Background

The workshops are main areas of factories where production of products or processing of parts is performed, and can be classified into various types such as electronic workshops, mechanical workshops, chemical workshops, assembly workshops, etc., according to the types of products and the production processes.

In the traditional workshop supervision process, the data and the running conditions of equipment are manually recorded (such as manual recording), and the real-time or near real-time monitoring and adjustment cannot be realized due to the time required for data collection, analysis and decision making, so that the supervision effect is poor.

Disclosure of Invention

The embodiment of the application provides a digital intelligent workshop supervision method, a system and equipment, which can solve the problem that the supervision effect is poor because real-time or near-real-time monitoring and adjustment cannot be realized due to the time required for data collection, analysis and decision making in the workshop supervision process depending on manual operation.

In a first aspect, an embodiment of the present application provides a digital intelligent workshop supervision method, where the method is applied to a digital intelligent workshop supervision device, and the method includes:

monitoring a first operation; wherein the first operation is for instructing the digital intelligent shop supervisory equipment to supervise a shop equipment;

acquiring operation data of the workshop equipment based on a first operation;

Analyzing working conditions according to the operation data, and determining a workshop supervision method; the workshop supervision method is used for adjusting the running state of the workshop equipment.

The technical scheme provided by the embodiment of the application at least has the following technical effects:

According to the digital intelligent workshop supervision method, first, the first operation for indicating the digital intelligent workshop supervision equipment to supervise the workshop equipment is monitored, so that the follow-up operation data of the workshop equipment can be obtained in real time under the condition that the operation data of the workshop equipment are needed, and data support can be provided for the follow-up timely adjustment of production parameters, so that instant response is realized, the influence of faults on production is reduced, and the follow-up supervision effect is improved. Then, based on the operation data of the workshop equipment obtained by the first operation, the state and the performance of the workshop equipment can be known in time, the follow-up response to any abnormal situation or operation problem can be facilitated, necessary measures can be timely taken to avoid potential production interruption or equipment damage, and the follow-up supervision effect can be improved. Finally, working condition analysis is carried out according to the operation data, a workshop supervision method is determined, fluctuation conditions of key indexes such as the operation efficiency and the output rate of workshop equipment can be found, bottleneck factors affecting the efficiency are further identified, the operation state of the workshop equipment is adjusted in a targeted mode, for example, the equipment parameter setting is optimized, the production flow is adjusted, the operation flow of workers is optimized, and the like, so that supervision effects are improved.

In a second aspect, an embodiment of the present application provides a digital intelligent workshop monitoring system, applied to a digital intelligent workshop monitoring device, for implementing the digital intelligent workshop monitoring method of the first aspect, where the digital intelligent workshop monitoring system includes:

the monitoring unit is used for monitoring the first operation; wherein the first operation is for instructing the digital intelligent shop supervisory equipment to supervise a shop equipment;

an acquisition unit configured to acquire operation data of the plant based on a first operation;

The determining unit is used for analyzing working conditions according to the operation data and determining a workshop supervision method; the workshop supervision method is used for adjusting the running state of the workshop equipment.

In a third aspect, an embodiment of the present application provides a digital intelligent shop monitoring device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the digital intelligent shop monitoring method according to any one of the first aspects when executing the computer program.

It will be appreciated that the advantages of the second to third aspects may be found in the relevant description of the first aspect, and are not described in detail herein.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart of a method for monitoring a digital intelligent workshop according to an embodiment of the present application;

FIG. 2 is a flowchart illustrating an implementation of the method for monitoring a digital intelligent workshop according to an embodiment of the present application after step S100;

FIG. 3 is a flowchart of a method for monitoring a digital intelligent workshop according to another embodiment of the present application;

FIG. 4 is a flowchart illustrating an implementation of the method for monitoring a digital intelligent workshop according to an embodiment of the present application after step S130;

FIG. 5 is a flowchart illustrating an implementation of the method for monitoring a digital intelligent workshop according to an embodiment of the present application after step S140;

FIG. 6 is a flowchart of a method for monitoring a digital intelligent workshop according to another embodiment of the present application;

FIG. 7 is a flowchart illustrating an implementation of step S180 in a digital intelligent workshop supervision method according to an embodiment of the present application;

FIG. 8 is a flowchart illustrating an implementation of step S300 in a digital intelligent workshop supervision method according to an embodiment of the present application;

FIG. 9 is a schematic diagram of a digital intelligent workshop supervision system according to an embodiment of the present application;

Fig. 10 is a schematic structural diagram of a digital intelligent workshop supervision apparatus according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.

As used in the present description and the appended claims, the term "if" may be interpreted in context as "when …" or "once" or "in response to a determination" or "in response to detection. Similarly, the phrase "if a condition or event is determined" or "if a condition or event is detected" may be interpreted in the context to mean "upon determination" or "in response to determination" or "upon detection of a condition or event, or" in response to detection of a condition or event.

Furthermore, the terms "first," "second," "third," and the like in the description of the present specification and in the appended claims, are used for distinguishing between descriptions and not necessarily for indicating or implying a relative importance.

Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

In the related art, workshops are main areas of factories where production of products or processing of parts is performed, and they may be classified into various types, such as electronic workshops, mechanical workshops, chemical workshops, assembly workshops, etc., according to the types of products and the manufacturing processes.

In order to solve the problems, the embodiment of the application provides a digital intelligent workshop supervision method, a system and equipment.

In the method, first, the first operation for indicating the digital intelligent workshop supervisory equipment to supervise the workshop equipment is monitored, so that the follow-up operation data of the workshop equipment can be acquired in real time under the condition that the operation data of the workshop equipment are needed, and data support can be provided for the follow-up timely adjustment of production parameters, so that the immediate response is realized, the influence of faults on production is reduced, and the follow-up supervisory effect is improved. Then, based on the operation data of the workshop equipment obtained by the first operation, the state and the performance of the workshop equipment can be known in time, the follow-up response to any abnormal situation or operation problem can be facilitated, necessary measures can be timely taken to avoid potential production interruption or equipment damage, and the follow-up supervision effect can be improved. Finally, working condition analysis is carried out according to the operation data, a workshop supervision method is determined, fluctuation conditions of key indexes such as the operation efficiency and the output rate of workshop equipment can be found, bottleneck factors affecting the efficiency are further identified, the operation state of the workshop equipment is adjusted in a targeted mode, for example, the equipment parameter setting is optimized, the production flow is adjusted, the operation flow of workers is optimized, and the like, so that supervision effects are improved.

The digital intelligent workshop supervision method provided by the embodiment of the application can be applied to digital intelligent workshop supervision equipment, and the digital intelligent workshop supervision equipment is the execution subject of the digital intelligent workshop supervision method provided by the embodiment of the application, and the embodiment of the application does not limit the specific type of the digital intelligent workshop supervision equipment.

For example, the digital intelligent workshop monitoring apparatus may include a digital intelligent workshop monitoring device, and a control device electrically connected to the digital intelligent workshop monitoring device. For example, the digital intelligent plant supervision device may include a sensor, a data acquisition device, a digital intelligent plant supervision system installed within the digital intelligent plant supervision device, and the like. The sensors are used to monitor the operating conditions and physical parameters of the plant equipment, for example, the sensors may be temperature sensors, pressure sensors, flow meters, electricity meters, etc. The data acquisition device is used for acquiring data provided by the sensor, including operation parameters of equipment, key indexes in the production process, environmental condition data and the like, and storing the data in a safe database. The digital intelligent workshop supervisory system can process and analyze the data acquired in real time by utilizing a data analysis tool and an algorithm to identify abnormality, trend and optimization opportunities, for example, based on the analysis of the energy consumption data, the digital intelligent workshop supervisory system can identify peaks and valleys of energy consumption and specific reasons of energy waste, and control the digital intelligent workshop supervisory equipment to correspondingly adjust by a control device, so that the energy use strategy can be optimized, the energy waste is reduced, intelligent energy-saving measures are implemented, and the energy cost is reduced to the greatest extent.

For example, the control device may be a single-chip microcomputer, a cell phone, a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a computing device or a computer connected to a wireless modem, a laptop computer, a handheld communication device, a handheld computing device, or the like.

In order to better understand the digital intelligent workshop supervision method provided by the embodiment of the application, the specific implementation process of the digital intelligent workshop supervision method provided by the embodiment of the application is described in an exemplary manner.

Fig. 1 shows a schematic flow chart of a digital intelligent workshop supervision method according to an embodiment of the present application, where the digital intelligent workshop supervision method includes:

S100, monitoring a first operation. Wherein the first operation is for indicating a digital intelligent shop supervisory equipment supervisory shop equipment.

It will be appreciated that the first operation may be defined as some specific trigger condition or event, such as the pressing of a button that initiates supervision, the sending of a supervision command (e.g. which shop floor device or supervision shop floor device is required to be supervised or a certain parameter or a certain index in the production process, etc.), or a change of a sensor signal, etc. For example, a button or switch may be provided for an operator to manually trigger the first operation to control the digital intelligent plant supervisory equipment. For another example, a set of logic rules or algorithms is designed to determine whether the sensor signal reaches a threshold or condition that triggers the supervisory command to trigger the supervisory program, e.g., when the sensor detects that parameters such as temperature, pressure, vibration, etc. exceed a safe range, the supervisory program is triggered to control the digital intelligent plant supervisory equipment to supervise the plant equipment. For another example, supervisory instructions from an operator are received via communication to control the digital intelligent plant supervisory equipment to supervise the plant equipment.

By monitoring the first operation, the method is beneficial to acquiring the operation data of the workshop equipment in real time under the condition that the operation data of the workshop equipment are needed, and can also provide data support for the subsequent timely adjustment of production parameters so as to realize instant response, reduce the influence of faults on production and improve the subsequent supervision effect.

In one possible implementation, referring to fig. 2, the shop equipment includes a first shop device and a second shop device, and after listening to the first operation in step S100, the digital intelligent shop supervision method further includes:

S110, in response to the first operation, the digital intelligent workshop supervision apparatus supervises first production data of the first workshop apparatus. Wherein the first operation is for instructing the digital intelligent workshop supervisory apparatus to supervise and analyze whether the production process of the first workshop apparatus is abnormal.

It will be appreciated that a response rule may be set, and in the case where an instruction triggering the first operation is detected, the first operation is performed, and the content of the first operation is parsed, where the content of the first operation may include which shop equipment needs to be regulated or a certain parameter or a certain index of the production process of the shop equipment is regulated, so that the digital intelligent shop regulation equipment regulates first production data of the first shop equipment, where the first production data may include a temperature parameter, a vibration parameter, etc. of the first shop equipment in the production process, and analyze these parameters to identify whether an abnormal situation exists, where the abnormal situation may include abrupt data fluctuation, a value exceeding a set range, or a pattern inconsistent with the historical data, etc.

So set up, in response to first operation, the first production data of digital wisdom workshop supervisory equipment supervision first workshop device can discover the problem in the production process rapidly, reduces down time and production interruption, helps improving subsequent supervision effect.

S120, in the process of monitoring the first production data of the first workshop device, if the digital intelligent workshop monitoring equipment does not acquire the response signal fed back by the second workshop device, the digital intelligent workshop monitoring equipment sends a second production data acquisition signal to the second workshop device. Wherein the response signal is used by the digital intelligent shop supervisory equipment to determine second production data for the second vehicle installation. The second vehicle device and the first vehicle device are devices responsible for different production stages under the same production task. The second production data acquisition signal is used for indicating the second inter-vehicle device to feed back the second production data to the digital intelligent workshop monitoring equipment so that the digital intelligent workshop monitoring equipment can monitor the production process of the second inter-vehicle device.

It will be appreciated that determining the communication protocol or physical interface between the digitized intelligent shop management device and the first and second vehicle devices, respectively, may use a wired or wireless connection. In the process of monitoring the first production data of the first vehicle device, the digital intelligent vehicle monitoring equipment can periodically detect whether the response signal fed back by the second vehicle device is received or not, and the monitoring can be performed in a polling mode or an event triggering mode. If the feedback signal is not received, that is, the response of the second inter-vehicle device is not obtained, the digital intelligent workshop monitoring apparatus determines that the second production data obtaining signal needs to be sent to the second inter-vehicle device, where the second production data obtaining signal may be a specific command or instruction, so as to instruct the second inter-vehicle device to feed back the second production data to the digital intelligent workshop monitoring apparatus, for example, after the subsequent second inter-vehicle device receives the second production data obtaining signal sent by the digital intelligent workshop monitoring apparatus, the digital intelligent workshop monitoring apparatus prepares and sends the second production data, where the digital intelligent workshop monitoring apparatus receives the second production data of the second inter-vehicle device, where the second production data may include a temperature parameter, a vibration parameter, and the like of the second inter-vehicle device during the production process, and may begin to monitor the production process of the second inter-vehicle device based on the second production data, where the monitoring process may include analysis, alarm processing, and production task adjustment, so as to ensure the normal operation of the production process.

For example, the first workshop apparatus may be a rough machining of the raw material, for example, melting and shaping the steel material. And the second workshop is responsible for finishing, for example, finishing, cutting, spraying or the like on the molded product. In this case, the operation of the second vehicle device must be performed after the first vehicle device is completed, so that the second vehicle device needs to wait for the first vehicle device to complete and then start working, so that during the initial period of production of the first vehicle device (the first production task is just started), the digital intelligent workshop supervision apparatus cannot acquire the response signal fed back by the second vehicle device, that is, the second vehicle device is still waiting at this time, and no object or product is processed, so that the second production data cannot be fed back. For example, when the first vehicle device completes the first production task and performs the second production task, the second vehicle device can process the product corresponding to the first production task, so that the second vehicle device has a processing object or product, and the second vehicle device can feed back the second production data to the digital intelligent workshop supervisory equipment after receiving the second production data acquisition signal sent by the digital intelligent workshop supervisory equipment.

For example, the first vehicle device may be a work area for producing the component a, the second vehicle device may be a work area for assembling or processing the component, and in the case where the first vehicle device completes the first production task and is ready to perform the second production task, the second vehicle device may initiate a task instruction for adjusting production to the first vehicle to instruct the first vehicle device to produce the component B, so that the production task of the second vehicle device is to assemble or process the component B, instead of assembling or processing the component a for which the first vehicle device has already produced, and thus, the operation of the second vehicle device must be performed after the first vehicle device completes the component B, at which time the second vehicle device is also in a waiting process, without processing the object or product, resulting in failure to feed back the second production data.

So set up, in the in-process of the first production data of supervision first workshop device, if the response signal of second workshop device feedback is not obtained to digital wisdom workshop supervisory equipment, then digital wisdom workshop supervisory equipment sends second production data acquisition signal to second workshop device, help acquireing and integrating the production data of first workshop device and second workshop device, in order to realize the real-time supervision to whole production flow, ensure the integrality and the timeliness of production data, help different workshops, the production activity of different stages can be operated in coordination, improve subsequent supervision effect.

In one possible implementation, referring to fig. 3, the digital intelligent workshop supervision method further includes:

S130, after the digital intelligent workshop supervisory equipment sends the second production data acquisition signal to the second workshop device, and under the condition that the response signal fed back by the second workshop device is not obtained yet, the digital intelligent workshop supervisory equipment pauses sending the second production data acquisition signal to the second workshop device and confirms whether the production task of the first workshop device is completed.

It will be appreciated that the digital intelligent shop supervision may set a predetermined timeout period, for example, 30 seconds, after the second production data acquisition signal is sent, if no response signal is received from the second vehicle device within this period of time, it may be considered that the second vehicle device is not processing an object or product or is malfunctioning, etc., and after the predetermined timeout period is exceeded, a timeout process may be automatically triggered to suspend sending the second production data acquisition signal to the second vehicle device, while checking the status of the production task of the first vehicle device, including confirming whether the task is completed or is in progress or is a sequence of production tasks, for example, confirming whether the production task is a first production task or a second production task. For example, the first workshop device may be a rough working of raw materials, for example, melting and shaping a steel material, and the second workshop is responsible for finishing, for example, engraving or cutting or spraying a shaped product, in which case, the operation of the second workshop device must be performed after the first workshop device is finished to ensure the integrity and quality of the product, so the second workshop device needs to wait for the first workshop device to complete production before starting to work, so even if the second workshop device receives the second production data acquisition signal, the second workshop device cannot feed back the second production data to the digital intelligent workshop supervisory equipment, and therefore, the second production data acquisition signal may be sent to the second workshop device in a pause mode, and the second production data acquisition signal is sent to the second workshop device to acquire the second production data in the process of performing the production task of the second workshop device after determining that the first workshop device is finished.

So set up, after the second production data acquisition signal is sent to the second inter-vehicle device to the digital wisdom workshop supervisory equipment, and under the condition that the response signal of second inter-vehicle device feedback has not yet been obtained, the digital wisdom workshop supervisory equipment pauses to send the second production data acquisition signal to the second inter-vehicle device to confirm whether the production task of first inter-vehicle device is accomplished, can focus on the control to first inter-vehicle device under the circumstances that only first inter-vehicle device is carrying out the production task, this kind of dynamic adjustment can track key production stage effectively, ensure continuity and the efficiency of production line, help improving subsequent supervision effect.

In one possible implementation, referring to fig. 4, in step S130, the digitized intelligent shop supervision device pauses sending the second production data acquisition signal to the second shop device, comprising:

S131, in the case that the digital intelligent workshop supervision equipment transmits the second production data acquisition signal to the second workshop device for the first time, recording the signal quantity of the transmitted second production data acquisition signal from the first time. The signal quantity is used for reflecting the signal quantity sent by the digital intelligent workshop monitoring equipment to the second workshop device or the signal quantity sent by the digital intelligent workshop monitoring equipment to the second workshop device under the condition that the production task of the first workshop device is completed.

It will be appreciated that a signal counter may be provided for recording the number of second production data acquisition signals sent to the second inter-vehicle device. In the case of the digitized intelligent shop management device first sending a second production data acquisition signal to the second shop device, the recording of the number of signals is started, which can be implemented by a special signal counter or event recorder. For example, there may be a database table or log file that records the time stamp and number of signals each time they are sent.

Thus, under the condition that the digital intelligent workshop monitoring equipment sends the second production data acquisition signal to the second workshop device for the first time, the signal quantity of the sent second production data acquisition signal is recorded from the first time, the activity level of the second workshop device can be quantified, further the progress condition of the production process is known in time, for example, when the second production data acquisition signal is sent to the second workshop device for a plurality of times, the response signal fed back by the second workshop device is not obtained yet, the condition that the second workshop device does not process an object or a product or fails can be known, and the like, so that the subsequent adjustment is made based on the information correspondingly, and the subsequent monitoring effect is improved.

And S132, under the condition that the signal quantity is larger than or equal to the preset signal quantity, the digital intelligent workshop supervision equipment pauses to send a second production data acquisition signal to a second workshop device.

It will be appreciated that a preset semaphore may be defined, i.e. the maximum data of the second production data acquisition signal transmitted by the digital intelligent shop supervision device, e.g. 5, 6 or 7 etc. Before each preparation for sending the second production data acquisition signal, checking whether the current value of the signal counter reaches or exceeds a preset semaphore, and if the value of the counter is greater than or equal to the preset semaphore, suspending the sending of the second production data acquisition signal to the second inter-vehicle device by the digital intelligent workshop supervisory equipment.

For example, in the event that the first vehicle device has just begun to process the first production task, the operation of the second vehicle device must be completed at the first vehicle device, and thus, if the signal quantity is greater than or equal to the preset signal quantity during the waiting process (i.e., the second vehicle device is waiting for the first vehicle device to complete the production task), the digital intelligent vehicle monitoring apparatus may suspend sending the second production data acquisition signal to the second vehicle device. In the case where the first vehicle device processes the first production task, the second vehicle device may initiate a task instruction to the first vehicle to adjust production to instruct the first vehicle device to produce the B component, so that the production task of the second vehicle device is to assemble or process the B component, rather than to assemble or process the a component in the first vehicle device having processed the first production task, and thus, the operation of the second vehicle device must be performed after the first vehicle device completes the B component, at which time the second vehicle device is also in a waiting process, so that the digital intelligent vehicle monitoring apparatus may also suspend sending the second production data acquisition signal to the second vehicle device if the signal amount in the waiting process is greater than or equal to the preset signal amount (i.e., the second vehicle device waits for the production task after the first vehicle device completes the modification).

So set up, under the condition that the semaphore is greater than or equal to predetermine the semaphore, digital wisdom workshop supervisory equipment suspension sends second production data to second inter-vehicle device and acquires the signal, can effectively avoid data flow to the excessive impact of network, for example, under high flow circumstances, the system can appear the overload, leads to service interruption or performance decline, through managing the semaphore, can avoid this problem, ensure that the system can both steady operation under various loads, provide and last reliable supervision service, help improving subsequent supervision effect.

In one possible implementation, the digital intelligent workshop supervision method further includes:

and acquiring the production information under the condition that the digital intelligent workshop supervision equipment pauses to send the second production data acquisition signal to the second workshop device. The production information includes information reflecting whether the second vehicle device fails or whether the second vehicle device needs to wait for the first vehicle device to finish the production task and then process the product produced by the first vehicle device.

It will be appreciated that data may be cached in local storage during suspension of data transmission. For example, the second vehicle may write associated production data (e.g., product numbers, production time stamps, operating status such as failure or non-failure) to a shared local database or cache, and the digital intelligent vehicle monitoring device may periodically poll the local store for updated production information, including information about whether the second vehicle is malfunctioning or whether the second vehicle needs to wait for the first vehicle to complete a production task before processing on the product produced by the first vehicle.

So set up, under the condition that the second production data acquisition signal is sent to the second inter-vehicle device in suspension of digital wisdom workshop supervisory equipment, acquire production information, help planning production order and resource allocation in advance, avoid raw and other materials backlog or supply shortage, guarantee smooth and easy going on of production flow, this helps improving whole production efficiency, reduces inventory cost, realizes lean production and on time production.

And S140, in the case that the production task of the first vehicle device is determined to be completed, the digital intelligent workshop supervision equipment resumes the acquisition task of sending the second production data acquisition signal to the second vehicle device.

It will be appreciated that confirming whether the production mission status of the first vehicle device has been marked as complete or met a predetermined production objective may be confirmed by monitoring the production mission status or production indicator achievement. Under the condition that the production task of the first workshop device is confirmed to be completed, the production task state of the first workshop device is updated into the digital intelligent workshop supervisory system, and after confirming that the production task of the first workshop device is completed, the digital intelligent workshop supervisory system can send a notification or signal to digital intelligent workshop supervisory equipment to indicate that the second production data acquisition signal can be continuously sent to the second workshop device.

So set up, under the circumstances that confirm the production task completion of first inter-vehicle device, digital wisdom workshop supervisory equipment resumes the acquisition task that sends second production data acquisition signal to second inter-vehicle device, can in time adjust tactics and operation to reduce the latency of second inter-vehicle device, this helps accelerating whole production rhythm, shortens production cycle, helps improving holistic supervision effect.

In one possible implementation, referring to fig. 5, S140, in a case where it is determined that the production task of the first vehicle device is completed, the digital intelligent shop supervision device resumes the acquisition task of sending the second production data acquisition signal to the second vehicle device, including:

S141, in the case that the production information comprises information reflecting the transition from the fault state to the non-fault state of the second vehicle device and the completion of the production task of the first vehicle device is determined, the digital intelligent workshop supervision apparatus resumes the acquisition task of sending the second production data acquisition signal to the second vehicle device, so that the digital intelligent workshop supervision apparatus can supervise the production process of the second vehicle device.

It will be appreciated that the fault condition monitoring unit is provided on the second vehicle device to monitor the production information, and a notification, such as a condition change notification, is sent when the production information indicates that the second vehicle device transitions from a fault condition to a non-fault condition, and the condition information of the second vehicle device may be obtained by a sensor, for example, determining that the condition of the second vehicle device is a fault condition if the operating power of the second vehicle device is within a fault range. A task completion monitoring unit is arranged on the first workshop device, and when the production task is completed, a completion signal is sent, and the task state of the first workshop device can be queried periodically or on demand to determine whether the production task is completed. A centralized event management system is deployed, state change information from each workshop device is integrated, explicit event processing logic is formulated, and when a key event (such as a transition from a fault state to a non-fault state and completion of a task of a first workshop device) is received, corresponding operations are automatically triggered, that is, the digital intelligent workshop supervisory equipment resumes the acquisition task of sending the second production data acquisition signal to the second workshop device.

Thus, under the condition that the production information comprises information reflecting the conversion from the fault state to the non-fault state of the second vehicle device and the completion of the production task of the first vehicle device is determined, the digital intelligent workshop monitoring equipment resumes the acquisition task of sending the second production data acquisition signal to the second vehicle device, so that the digital intelligent workshop monitoring equipment can monitor the production process of the second vehicle device, can immediately monitor the second vehicle device after the fault recovery, and is beneficial to improving the monitoring effect.

S142, under the condition that the production information comprises information reflecting that the second vehicle device needs to wait for the first vehicle device to finish the production task and processing the product produced by the first vehicle device, and the production task of the first vehicle device is determined to be finished, the digital intelligent workshop monitoring equipment resumes the acquisition task of sending the second production data acquisition signal to the second vehicle device, so that the digital intelligent workshop monitoring equipment can monitor the production process of the second vehicle device.

It will be appreciated that the acquired production information is parsed, and in the case where the content of the production information indicates that the second vehicle device needs to wait for the first vehicle device to complete a production task and then process a product produced by the first vehicle device, the task state of the first vehicle device may be queried periodically or as needed to determine whether the production task of the first vehicle device has been completed, and in the case where it is determined that the production task of the first vehicle device has been completed, the digital intelligent vehicle monitoring apparatus resumes the acquisition task of sending the second production data acquisition signal to the second vehicle device.

So configured, in the event that the production information includes information reflecting that the second vehicle device needs to wait for the first vehicle device to complete the production task, to process the product produced by the first vehicle device, and the production task of the first vehicle device is determined to be complete, the digital intelligent vehicle monitoring apparatus resumes the acquisition task of sending the second production data acquisition signal to the second vehicle device, the digital intelligent workshop monitoring equipment can monitor the production process of the second workshop device, can automatically trigger the monitoring task of the second workshop device, reduces the manual intervention and waiting time, ensures the continuity and high efficiency of monitoring, and is beneficial to improving the monitoring effect.

In the process of supervising the first production data of the first vehicle device, if the first production data indicates abnormal data, prompting information is generated, and sending of the second production data acquisition signal to the second vehicle device is stopped. The reminding information is used for reminding operators of abnormal production process of the first workshop device.

It is understood that the sensor and the data collection device are installed on the first vehicle device, and the first production data is collected in real time. The range or threshold of normal production data is set based on experience and historical data. The first production data is monitored using a real-time data analysis tool (e.g., a streaming platform) to detect if a set threshold is exceeded. When abnormal data is detected, automatic generation of reminding information can be realized by writing a script or a program, the abnormal data and related information are packaged into a notification message, and the reminding information can be sent to operators and management staff through short messages, mails, display screens in workshops or other instant messaging tools. After the abnormal data is detected, the transmission of the second production data acquisition signal to the second inter-vehicle device may be suspended.

For example, when the second vehicle device needs to process the product just produced by the first vehicle device, the transmission of the second production data acquisition signal to the second vehicle device may be resumed after the operator has handled and resolved the anomaly of the first vehicle device. When the second vehicle device is not required to process the first product just produced by the first vehicle device, but processes the second product produced by the first vehicle device, the operator can resume sending the second production data acquisition signal to the second vehicle device after processing and solving the abnormality of the first vehicle device.

So set up, in the in-process of supervision first production data of first workshop device, if first production data indicates to be abnormal data, then generates the warning information to suspend to send second production data acquisition signal to second workshop device, can in time discover and handle abnormal situation, can avoid in time solving and developing into big trouble because of the minor problem, reduce maintenance cost, effectively prevent the flaw product that probably appears in the production process to improve the quality of final product.

In one possible implementation, referring to fig. 6, the digital intelligent workshop supervision method further includes:

And S150, continuously monitoring the first production data of the first workshop device by the digital intelligent workshop supervision equipment.

It is understood that various sensors, such as temperature, pressure, vibration, position, energy consumption, etc., are installed on the first vehicle device to collect first production data of the first vehicle device in real time. The sensors are connected with the digital intelligent workshop monitoring equipment through the internet of things technology, so that the real-time transmission and processing of data are realized, the digital intelligent workshop monitoring equipment is used for carrying out real-time analysis on the first production data, and abnormal states or trend changes are identified, so that the purpose of continuously monitoring the first production data of the first workshop device is achieved.

So set up, the first production data of first workshop device of digital wisdom workshop supervisory equipment continuous monitoring can realize the continuous, the high-efficient control of first production data to first workshop device, ensures the stability and the product quality of production process, improves production efficiency and resource utilization simultaneously.

S160, in a case where the second vehicle device initiates a production task to the first vehicle device or the second vehicle device receives a production task from the first vehicle device, the digital intelligent workshop supervisory apparatus keeps monitoring the first production data of the first vehicle device, and the digital intelligent workshop supervisory apparatus monitors that the second vehicle device receives a production task from the first vehicle device. The first vehicle device is electrically connected with the second vehicle device.

In the event that the processing requirements change, for example, when the first vehicle device is ready to perform a new task, it sends a signal or command containing detailed information about the task (e.g., type of task, required material, expected output, etc.) to the second vehicle device via an electrical connection, at which point the first vehicle device becomes a product to process C, the second vehicle device may finish processing the product C to obtain a product D, while the first vehicle device continues to process the product a after the product C has been processed. In this case, the digital intelligent workshop supervisory equipment keeps monitoring the first production data of the first workshop device, i.e. the first production data of the a product which the first workshop device continues to process after finishing the C product, while the digital intelligent workshop supervisory equipment monitors the second workshop device receiving the production task from the first workshop device, i.e. the process of finishing the C product by the second workshop device.

In this way, in the event that the second vehicle initiates a production mission to the first vehicle or the second vehicle receives a production mission from the first vehicle, the digital intelligent vehicle monitoring apparatus remains monitoring the first production data of the first vehicle and the digital intelligent vehicle monitoring apparatus monitors the second vehicle receiving a production mission from the first vehicle.

S170, in the event that the second vehicle device refuses to run the production task from the first vehicle device, the digital intelligent vehicle monitoring apparatus keeps monitoring the first production data of the first vehicle device.

It will be appreciated that the digital intelligent shop management device may automatically identify whether the second vehicle device refuses to perform the production task from the first vehicle device, and in the event that refusal is confirmed, obtain a refusal cause, including but not limited to insufficient resources of the second vehicle device, failure of the second vehicle device, second vehicle device production priority adjustment, and the like. Regardless of whether the second vehicle performs a task, the digital intelligent vehicle monitoring apparatus should continuously monitor the first production data of the first vehicle device, including, but not limited to, the operational status of the first vehicle device, the production schedule, the material consumption, the energy usage, etc. The data abnormality is found in the supervision process, such as production efficiency reduction, equipment fault early warning and the like, related personnel can be immediately notified and displayed on the supervision equipment so as to take measures in time later.

Thus, under the condition that the second vehicle device refuses to run the production task from the first vehicle device, the digital intelligent workshop monitoring equipment keeps monitoring the first production data of the first vehicle device, can know the production state and the running condition of the first vehicle device in real time, can adjust the whole production flow with more flexibility even when the second vehicle refuses to execute the task, avoids decision errors caused by information deletion, and is beneficial to improving the whole monitoring effect.

S180, in the case that the second vehicle device runs the production task from the first vehicle device, the second vehicle device transmits second production data to the digital intelligent workshop supervisory equipment in response to the second production data acquisition signal, and the digital intelligent workshop supervisory equipment keeps or pauses supervising the first vehicle device.

It will be appreciated that in the event that the second vehicle receives a production mission pushed by the first vehicle, the receiving process may be initiated and the mission may be initiated, during which the second production data is transmitted to the digital intelligent vehicle monitoring device in response to the second production data acquisition signal, so that the digital intelligent vehicle monitoring device may analyze the second production data to identify anomalies in the production process of the second vehicle, while during monitoring the second production data of the second vehicle, real-time monitoring of the first production data of the first vehicle may be suspended, or, as desired, the first production data of the first vehicle may be monitored at a specific frequency to maintain transparency of the overall production state. For example, the first vehicle device may suspend real-time monitoring of the first vehicle device after completion of the last production task. For another example, for less important production flows, the first production data of the first workshop apparatus may be monitored at a particular frequency to reduce the monitored power consumption of the digital intelligent workshop monitoring apparatus.

So set up, under the condition that second inter-vehicle device is running the production task from first inter-vehicle device, second inter-vehicle device responds to second production data acquisition signal and sends second production data to digital wisdom workshop supervisory equipment, and digital wisdom workshop supervisory equipment keeps or pauses to supervise first inter-vehicle device, can realize the real-time synchronization of production data, this makes supervisory equipment can the real-time access and analysis second inter-vehicle device's production state and performance index to make more accurate, timely decision or adjustment.

In one possible implementation, referring to fig. 7, S180, in a case where the second vehicle device runs a production task from the first vehicle device, the second vehicle device transmits the second production data to the digital intelligent vehicle supervisory equipment in response to the second production data acquisition signal, and the digital intelligent vehicle supervisory equipment maintains or pauses supervising the first vehicle device, comprising:

S181, in response to the second operation, the second vehicle device runs a production mission from the first vehicle device, and the digital intelligent vehicle monitoring apparatus maintains monitoring of the first production data of the first vehicle device.

It will be appreciated that complete production task information for the first vehicle device may be obtained, including task details, priorities, amounts of tasks that have not yet been completed, and the like. And under the condition that the first workshop device has an incomplete production task, sending a signal to the digital intelligent workshop monitoring equipment, wherein the signal indicates that the incomplete task is to be processed even if the current production task is completed, namely the digital intelligent workshop monitoring equipment is required to keep monitoring the first production data of the first workshop device. The second operation may be a click, touch, keyboard-mouse co-operation, automatic triggering, etc., for example, the operator may click on an interactive interface where the digital intelligent workshop supervisory apparatus keeps supervising in case that the operator knows that the first workshop apparatus has not completed the production task, so that the second workshop apparatus runs the production task from the first workshop apparatus, and the digital intelligent workshop supervisory apparatus keeps supervising the first production data of the first workshop apparatus. For another example, a triggering program may be provided to automatically trigger the second operation to cause the second vehicle to run the production mission from the first vehicle in the event that the first vehicle has an incomplete production mission, and the digital intelligent vehicle monitoring apparatus maintains monitoring the first production data of the first vehicle.

So set up, in response to the second operation, the production task that the second inter-vehicle device was run from first inter-vehicle device, and the first production data of digital wisdom workshop supervisory equipment keeps the supervision first inter-vehicle device can know the production state and the task progress of every workshop in real time to optimize the control resource allocation, avoid wasting of resources and repeated work, help improving the supervision effect.

Or S182, in response to the third operation, the second vehicle device runs a production task from the first vehicle device, and the digital intelligent vehicle supervision apparatus pauses supervision of the first vehicle device. The second operation is different from the third operation, the second operation is used for indicating that the first workshop device has other incomplete production tasks after the current production task is completed, and the third operation is used for indicating that the first workshop device has no other incomplete production tasks after the current production task is completed.

It will be appreciated that complete production task information for the first vehicle device may be obtained, including task details, priorities, amounts of tasks that have not yet been completed, and the like. And under the condition that the first workshop device does not have incomplete production tasks, sending a signal to the digital intelligent workshop monitoring equipment to indicate that the first workshop device has completed all production tasks and has no production tasks to be processed, namely, the digital intelligent workshop monitoring equipment is not required to keep monitoring the first production data of the first workshop device. The third operation may be a click, touch, keyboard-mouse co-operation, auto-triggering, etc., e.g., the operator may click on an interactive interface where the digital intelligent vehicle monitoring apparatus remains monitoring to cause the second vehicle to run the production task from the first vehicle and the digital intelligent vehicle monitoring apparatus pauses monitoring the first vehicle in case that the operator knows that the first vehicle has no incomplete production task. For another example, a triggering program may be provided to automatically trigger the third operation to cause the second vehicle to run the production task from the first vehicle without the first vehicle having an incomplete production task, and the digital intelligent vehicle supervision apparatus to suspend supervision of the first vehicle.

So set up, in response to the third operation, the production task that the second inter-vehicle device was run from first inter-vehicle device, and the first inter-vehicle device of digital wisdom workshop supervisory equipment suspension supervision can know the production state and the task progress of every workshop in real time to optimize the control resource allocation, avoid wasting of resources and repeated work, help improving the supervision effect.

S200, acquiring operation data of workshop equipment based on the first operation.

It will be appreciated that analyzing the first operation, determining what the goals and demands of the data acquisition are, e.g., which parameters (temperature, pressure, speed, etc.), what the frequency of the data acquisition is (real-time, per second, per hour, etc.), selecting the appropriate sensor for data acquisition to obtain the operational data of the plant equipment may include temperature data, vibration data, pressure data, energy consumption data, etc. of the plant equipment, based on the demands of the first operation. For example, vibration conditions of rotating equipment in workshop equipment in a production process need to be acquired, and vibration data can be acquired in real time through a vibration sensor for subsequent supervision tasks.

The method and the device have the advantages that the operation data of the workshop equipment are acquired based on the first operation, the state and the performance of the workshop equipment can be known in time, the follow-up operation can be fast responded to any abnormal situation or operation problem, necessary measures are timely taken to avoid potential production interruption or equipment damage, and the follow-up supervision effect is improved.

S300, analyzing working conditions according to the operation data, and determining a workshop supervision method. The workshop supervision method is used for adjusting the running state of workshop equipment.

It will be appreciated that, first, useful features are extracted from the operational data. For example: average value: representing the average state, maximum/minimum value of the device over a period of time: reflecting the extreme operating conditions and standard deviations of the equipment: measuring the fluctuation degree of the data, analyzing the trend of the data by a moving average, weighting average and other methods, for example, extracting 7 vibration index data [10, 15, 12, 14, 13, 16, 18] from the running data, calculating the average value of the first three data to obtain a first moving average value of (10+15+12)/3=12.33, moving a window to the right by one data point, calculating the average value of the 2 nd to 4 th data to obtain a second moving average value of (15+12+14)/3=13.67, and by the method, all the moving average values can be calculated, and further, the trend of the vibration index data can be analyzed to show an increasing trend. The device conditions are then classified, such as: normal conditions, abnormal conditions, fault conditions, early warning conditions, etc., for example, when vibration of the workshop apparatus exceeds a normal range but does not reach a fault level, the vibration is marked as an 'early warning condition'; when the set threshold is exceeded, it is marked as "failure mode". Finally, a workshop supervision method is formulated according to the working condition analysis result, for example, normal working conditions: maintaining the current running state of the equipment, and checking and maintaining periodically. Early warning working conditions: precautions are taken, such as reducing plant load, increasing cooling time, etc. Abnormal working conditions: and immediately notifying maintenance personnel to perform field inspection and repair. Failure conditions: emergency shutdown, comprehensive maintenance and troubleshooting, and for example, time series analysis or machine learning models (such as ARIMA, LSTM) may be used to predict future states of the device, identify anomalies (such as devices that are about to fail), set an advanced maintenance schedule for the predicted anomalies, and adjust the time or frequency of operation to reduce downtime, such as normal conditions: the conveyor belt of the workshop equipment has a temperature of between 60 and 70 ℃ and maintains the existing operation, periodic lubrication and maintenance. Early warning working conditions: the motor temperature is between 75-85 ℃, the load of the conveyor belt is reduced, and the cooling time is increased. Abnormal working conditions: the motor temperature exceeded 85 ℃, immediately informing maintenance personnel to check the cooling system and the motor itself. Failure conditions: the motor temperature exceeds 90 ℃, and the motor is stopped in an emergency mode for detailed inspection and maintenance.

The device is arranged in such a way, working condition analysis is carried out according to the operation data, a workshop supervision method is determined, fluctuation conditions of key indexes such as the operation efficiency and the output rate of workshop equipment can be found, bottleneck factors affecting the efficiency are further identified, the operation state of the workshop equipment is adjusted in a targeted manner, such as equipment parameter setting, production flow adjustment, worker operation flow optimization and the like, and supervision effects are improved.

In one possible implementation, referring to fig. 8, S300, a method for determining a shop supervision method according to operating data and operating condition analysis includes:

s310, analyzing the acquired operation data of the workshop equipment to identify abnormal working condition data of the workshop equipment.

It will be appreciated that, first, reasonable ranges of various parameters are set, and an abnormality can be judged when the actual value exceeds this range. For example, for a temperature sensor, a normal range between 20 ℃ and 40 ℃, if suddenly jumped to 60 ℃, could indicate a malfunction of the plant equipment. Then, an abnormal situation is identified by monitoring the trend of the data, for example, for a pressure sensor, if the pressure value continues to rise or fall beyond the normal fluctuation range, it indicates that the apparatus is in question. The data collected by the sensor is then subjected to spectral analysis using signal processing techniques to find abnormal frequency components, for example, for a vibration sensor, which are indicative of equipment failure. The machine learning algorithm or model is trained by historical data (e.g., the historical data is classified by tag, labeled "normal" or "failure" status, the data is divided into training and testing sets, 70% -80% of the data is used for training, 20% -30% of the data is used for testing, a suitable machine learning model is selected and trained using the training set, and validated using the testing set to arrive at a final machine learning algorithm or model), so that the machine learning algorithm or model can identify anomalies similar to known failure modes, e.g., a certain device will have a particular vibration mode before failure, faults may be predicted by a pattern recognition algorithm. Finally, the comprehensive judgment is performed by combining a plurality of parameters, because a single parameter may be affected by external factors, for example, combining a plurality of sensor parameters such as temperature, humidity and pressure, comprehensive analysis is performed to judge whether the equipment is in an abnormal state, for example, for each sensor parameter, a reasonable upper and lower limit threshold value (due to the change of environment and operation conditions, the parameter threshold value can be dynamically adjusted according to the need) is determined according to historical data and normal operation state, and the condition exceeding the threshold value range can be regarded as potential abnormality. Analyzing the correlation between different parameters to understand their inherent relationship, when some parameters change, other related parameters also change, which can be used as the basis for abnormality judgment (for example, if the production efficiency of the equipment suddenly decreases, related current and vibration data can be checked, such as the problem that the current increases and then the vibration increases and the temperature increases are found, the problem that the current increases and the vibration increases and the temperature increases are described), according to the relative importance of each parameter, a comprehensive scoring algorithm is designed, and state signals of different parameters are weighted and summed to obtain a total score, so as to judge the state of the equipment, for example, 4 parameters are assumed to evaluate the state of the equipment: temperature of, Vibration, current and pressure may be set to their relative importance and weights assigned to each parameter, such as 30% temperature, 25% vibration, 20% current, 25% pressure, and measured values (or normalized values) for each parameter multiplied by the corresponding weights and added to obtain an overall score. for example, if the temperature is 80, the vibration is 3, the current is 10, and the pressure is 150, the calculation method is total score= (0.3×80) + (0.25×3) + (0.2×10) + (0.25×150) =64.25, and the state score may be predefined: 30-60 is in abnormal state, 60-80 is in good state, and 80-100 is in excellent state.

The device is arranged in such a way, the acquired operation data of the workshop equipment are analyzed to identify the abnormal working condition data of the workshop equipment, potential faults and abnormal conditions can be found and processed in time, and the equipment is prevented from being stopped due to the faults, so that the operation efficiency of the equipment is improved.

S320, matching the abnormal working condition data with a preset fault diagnosis library to determine a workshop supervision method corresponding to the identified abnormal working condition data.

For example, the preset fault diagnosis library includes fault type a: feature 1, feature 2, feature 3, fault type B: feature 4, feature 5, feature 6, fault type C: feature 7, feature 8, feature 9, for the detected abnormal condition data, the following features are extracted: the method comprises the steps of comparing the extracted features with features in a fault diagnosis library, calculating the similarity of each fault type and the extracted features, selecting the fault type with the highest similarity, for example, matching the features 1 and 3 with the fault type A, matching the features 5 with the fault type B, and judging that the fault type A is the cause of the current abnormality according to the similarity. For the fault type a, the preset supervision method may be: the running speed of the equipment is reduced, and specific components are inspected and replaced.

By means of the arrangement, the abnormal working condition data are matched with the preset fault diagnosis library, so that a workshop supervision method corresponding to the identified abnormal working condition data is determined, the problem of equipment or a production line can be identified rapidly and accurately, and compared with a traditional supervision method relying on manual experience, the efficiency is improved greatly.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.

Corresponding to the method for monitoring and managing a digital intelligent workshop in the above embodiment, the embodiment of the application further provides a system for monitoring and managing a digital intelligent workshop, and each unit of the system can implement each step of the method for monitoring and managing a digital intelligent workshop. Fig. 9 is a block diagram of a digital intelligent shop supervision system according to an embodiment of the present application, and for convenience of explanation, only a portion related to the embodiment of the present application is shown.

Referring to fig. 9, the digital intelligent shop supervision system includes:

and the monitoring unit is used for monitoring the first operation. Wherein the first operation is for indicating a digital intelligent shop supervisory equipment supervisory shop equipment.

And the acquisition unit is used for acquiring the operation data of the workshop equipment based on the first operation.

And the determining unit is used for analyzing the working condition according to the operation data and determining a workshop supervision method. The workshop supervision method is used for adjusting the running state of workshop equipment.

It should be noted that, because the content of information interaction and execution process between the above systems/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein.

It will be clearly understood by those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in practical application, the above-mentioned functional allocation may be performed by different functional units according to needs, that is, the internal structure of the digital intelligent workshop supervisory system is divided into different functional units, so as to perform all or part of the above-mentioned functions. The functional units in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present application. The specific working process of the units in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

The embodiment of the application also provides a digital intelligent workshop supervision device, and fig. 10 is a schematic structural diagram of the digital intelligent workshop supervision device according to an embodiment of the application. As shown in fig. 10, the digital intelligent shop monitoring device 6 of this embodiment includes: at least one processor 60 (only one shown in fig. 10), at least one memory 61 (only one shown in fig. 10), and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60, which processor 60, when executing the computer program 62, causes the digital intelligent shop management device 6 to perform the steps of any of the various digital intelligent shop management method embodiments described above, or causes the digital intelligent shop management device 6 to perform the functions of the various elements of the system embodiments described above.

Illustratively, the computer program 62 may be partitioned into one or more units that are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more units may be a series of computer program instruction segments capable of performing a specific function describing the execution of the computer program 62 in the digital intelligent shop supervision device 6.

For example, the digital intelligent shop monitoring device 6 may include a digital intelligent shop monitoring device and a control device electrically connected to the digital intelligent shop monitoring device. For example, the digital intelligent plant supervision device may include a sensor, a data acquisition device, a digital intelligent plant supervision system installed within the digital intelligent plant supervision device, and the like. The sensors are used to monitor the operating conditions and physical parameters of the plant equipment, for example, the sensors may be temperature sensors, pressure sensors, flow meters, electricity meters, etc. The data acquisition device is used for acquiring data provided by the sensor, including operation parameters of equipment, key indexes in the production process, environmental condition data and the like, and storing the data in a safe database. The digital intelligent workshop supervisory system can process and analyze the data acquired in real time by using a data analysis tool and an algorithm to identify abnormality, trend and optimization opportunities, for example, based on the analysis of the energy consumption data, the digital intelligent workshop supervisory system can identify peaks and valleys of energy consumption and specific causes of energy waste, and control the digital intelligent workshop supervisory equipment 6 to correspondingly adjust by using the control device, so that the energy use strategy can be optimized, the energy waste can be reduced, intelligent energy-saving measures can be implemented, and the energy cost can be reduced to the greatest extent. The digital intelligent plant supervision device 6 may include, but is not limited to, a processor 60, a memory 61. It will be appreciated by those skilled in the art that fig. 10 is merely an example of the digital intelligent shop monitoring device 6 and is not limiting of the digital intelligent shop monitoring device 6, and may include more or less components than illustrated, or may combine certain components, or different components, such as may also include input and output devices, network access devices, buses, etc.

The Processor 60 may be a central processing unit (Central Processing Unit, CPU), the Processor 60 may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 61 may in some embodiments be an internal storage unit of the digital intelligent shop management device 6, such as a hard disk or a memory of the digital intelligent shop management device 6. The memory 61 may in other embodiments also be an external storage device of the Digital intelligent shop monitoring device 6, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the Digital intelligent shop monitoring device 6. Further, the memory 61 may also comprise both an internal memory unit and an external memory device of the digital intelligent shop supervision device 6. The memory 61 is used for storing an operating system, application programs, boot loader (BootLoader), data, other programs, etc., such as program codes of the computer program. The memory 61 may also be used for temporarily storing data that has been output or is to be output.

Embodiments of the present application also provide a computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of any of the various method embodiments described above.

Embodiments of the present application provide a computer program product that, when run on a digital intelligent plant supervision apparatus, causes the digital intelligent plant supervision apparatus to implement the steps of any of the various method embodiments described above.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to the digital intelligent plant supervisory equipment, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided herein, it should be understood that the disclosed digital intelligent plant supervision apparatus, digital intelligent plant supervision system, and digital intelligent plant supervision method may be implemented in other manners. For example, the above described embodiments of the digital intelligent plant supervision device and digital intelligent plant supervision system are merely illustrative, e.g. the division of the units is only one logical functional division, and there may be additional divisions in the actual implementation, e.g. multiple units or components may be combined or may be integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. A method of digital intelligent plant supervision, the method being applied to digital intelligent plant supervision equipment, the method comprising:

acquiring operation data of the workshop equipment based on a first operation;

Analyzing working conditions according to the operation data, and determining a workshop supervision method; the workshop supervision method is used for adjusting the running state of the workshop equipment;

the shop apparatus includes a first shop device and a second shop device, and after listening to the first operation, the method further includes:

In response to a first operation, the digital intelligent shop monitoring device monitors first production data of the first shop device; wherein the first operation is for instructing the digital intelligent workshop supervisory apparatus to supervise and analyze whether the production process of the first workshop apparatus is abnormal;

In the process of supervising the first production data of the first vehicle device, if the digital intelligent vehicle supervision equipment does not acquire a response signal fed back by the second vehicle device, the digital intelligent vehicle supervision equipment transmits a second production data acquisition signal to the second vehicle device; wherein the response signal is used by the digital intelligent shop supervisory equipment to determine second production data for the second inter-vehicle device; the second workshop device and the first workshop device are devices which are responsible for different production stages under the same production task; the second production data acquisition signal is used for indicating the second vehicle device to feed back the second production data to the digital intelligent workshop monitoring equipment so that the digital intelligent workshop monitoring equipment can monitor the production process of the second vehicle device.

2. The digital intelligent plant supervision method according to claim 1, further comprising:

After the digital intelligent workshop supervisory equipment sends the second production data acquisition signal to the second workshop device, and in the case that the response signal fed back by the second workshop device is not acquired yet, the digital intelligent workshop supervisory equipment pauses sending the second production data acquisition signal to the second workshop device and confirms whether the production task of the first workshop device is completed;

in the event that it is determined that the production task of the first vehicle device is complete, the digital intelligent vehicle monitoring apparatus resumes the acquisition task of sending the second production data acquisition signal to the second vehicle device.

3. The digital intelligent plant supervision method according to claim 2, wherein the digital intelligent plant supervision apparatus pauses the transmission of the second production data acquisition signal to the second plant, comprising:

Recording, from the first start, a signal quantity of the second production data acquisition signal transmitted, in the case where the digitized intelligent shop supervision device transmits the second production data acquisition signal to the second inter-vehicle device for the first time; the signal quantity is used for reflecting the signal quantity sent by the digital intelligent workshop monitoring equipment to the second workshop device or the signal quantity sent by the digital intelligent workshop monitoring equipment to the second workshop device under the condition that the production task of the first workshop device is completed;

in the event that the semaphore is greater than or equal to a preset semaphore, the digitized intelligent shop supervision device pauses the transmission of the second production data acquisition signal to the second inter-vehicle device.

4. The digital intelligent shop management method according to claim 2, wherein the method further comprises:

Acquiring production information in case the digital intelligent workshop supervisory equipment pauses the transmission of the second production data acquisition signal to the second workshop apparatus; the production information includes information reflecting whether the second vehicle device fails or whether the second vehicle device needs to wait for the first vehicle device to finish a production task and then process the product produced by the first vehicle device.

5. The digital intelligent plant supervision method according to claim 4, wherein in the case where it is determined that the production task of the first vehicle device is completed, the digital intelligent plant supervision apparatus resumes the acquisition task of transmitting the second production data acquisition signal to the second vehicle device, comprising:

In the case where the production information includes information reflecting a transition from a fault state to a non-fault state of the second inter-vehicle device and it is determined that a production task of the first inter-vehicle device is completed, the digital intelligent workshop supervisory apparatus resumes an acquisition task of transmitting the second production data acquisition signal to the second inter-vehicle device to enable the digital intelligent workshop supervisory apparatus to supervise a production process of the second inter-vehicle device;

And under the condition that the production information comprises information reflecting that the second vehicle device needs to wait for the first vehicle device to finish the production task and process products produced by the first vehicle device, and the production task of the first vehicle device is determined to be finished, the digital intelligent workshop supervision equipment resumes the acquisition task of sending the second production data acquisition signal to the second vehicle device, so that the digital intelligent workshop supervision equipment can supervise the production process of the second vehicle device.

6. The digital intelligent plant supervision method according to claim 4, further comprising:

the digital intelligent shop supervision device continuously monitors the first production data of the first shop device;

In the event that the second vehicle initiates a production mission to or receives a production mission from the first vehicle, the digital intelligent vehicle monitoring apparatus remains monitoring first production data of the first vehicle, and the digital intelligent vehicle monitoring apparatus monitors that the second vehicle receives a production mission from the first vehicle; wherein the first vehicle-to-vehicle device is electrically connected to the second vehicle-to-vehicle device;

In the event that the second vehicle device refuses to run a production mission from the first vehicle device, the digital intelligent vehicle monitoring apparatus remains monitoring the first production data of the first vehicle device;

In the event that the second vehicle device is running a production mission from the first vehicle device, the second vehicle device sends the second production data to the digital intelligent plant supervisory apparatus in response to the second production data acquisition signal, and the digital intelligent plant supervisory apparatus remains or pauses supervising the first vehicle device.

7. The digital intelligent plant supervision method according to claim 6, wherein, in a case where the second inter-plant runs a production task from the first inter-plant, the second inter-plant transmits the second production data to the digital intelligent plant supervision apparatus in response to the second production data acquisition signal, and the digital intelligent plant supervision apparatus keeps or pauses supervising the first inter-plant apparatus, comprising:

In response to a second operation, the second vehicle device runs a production mission from the first vehicle device, and the digital intelligent shop supervision apparatus remains supervising the first production data of the first vehicle device;

Or in response to a third operation, the second vehicle device runs a production mission from the first vehicle device and the digital intelligent shop supervision apparatus pauses supervising the first vehicle device; the second operation is different from the third operation, the second operation is used for indicating that the first inter-vehicle device has other incomplete production tasks after the current production task is completed, and the third operation is used for indicating that the first inter-vehicle device has no other incomplete production tasks after the current production task is completed;

the working condition analysis is performed according to the operation data, and a workshop supervision method is determined, which comprises the following steps:

analyzing the acquired operation data of the workshop equipment to identify abnormal working condition data of the workshop equipment;

And matching the abnormal working condition data with a preset fault diagnosis library to determine a workshop supervision method corresponding to the identified abnormal working condition data.

8. A digital intelligent shop monitoring system, applied to a digital intelligent shop monitoring device, for implementing the digital intelligent shop monitoring method according to any one of claims 1 to 7, the digital intelligent shop monitoring system comprising:

9. A digital intelligent plant supervision device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any one of claims 1 to 7 when executing the computer program.