Engineering equipment intelligent management platform based on Internet of Things

Abstract

The invention discloses an engineering equipment intelligent management platform based on the Internet of Things, particularly relates to the field of engineering management, and comprises a data acquisition module, a data preprocessing module, an intelligent judgment module, an equipment performance analysis module, an equipment energy consumption analysis module, an equipment collaborative analysis module, an equipment fault prediction module and a user interaction output module. According to the engineering equipment intelligent management platform based on the Internet of Things, it is ensured that data conforms to a standard format through the data preprocessing module, the subsequent data analysis process is simplified, and the data processing speed is increased; the current state of the equipment is comprehensively evaluated through comprehensive performance parameters through the equipment performance analysis module, unnecessary maintenance work is reduced, and accurate data support is provided for fault prediction and maintenance; by establishing a calculation model for calculating the energy consumption of the engineering equipment through the equipment energy consumption analysis module, the energy consumption of the equipment in different operation states can be accurately quantified, and the change trend of the equipment load and the influence of the change trend on the energy consumption can be evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering management, and more specifically, to an intelligent management platform for engineering equipment based on the Internet of Things. Background Art

[0002] With the rapid development of information technology and intelligent manufacturing, the importance of the Internet of Things in engineering equipment management has become increasingly prominent. By connecting sensors, equipment and systems to the Internet, the Internet of Things enables real-time data collection, monitoring and analysis, greatly improving the management efficiency and intelligence level of engineering equipment.

[0003] The introduction of an IoT-based intelligent management platform for engineering equipment has brought revolutionary changes to the engineering industry. First, it enables real-time monitoring of equipment status. Data collected by sensors can detect potential faults in a timely manner, thereby preventing equipment downtime, significantly reducing maintenance costs and improving equipment availability. Second, with the help of big data analysis and artificial intelligence algorithms, the platform can optimize the workload of equipment, avoid overload or idleness, and improve overall production efficiency. In addition, the platform also supports remote control and intelligent scheduling, reducing manpower requirements and improving the safety and flexibility of operations. These positive impacts not only improve the economic benefits of engineering projects, but also promote the sustainable development of the industry.

[0004] However, in actual use, it still has some shortcomings, such as equipment compatibility issues, challenges in data security and privacy protection, and stability and reliability issues in complex environments. The phenomenon of information islands between different devices and systems also limits the comprehensive application of Internet of Things technology in engineering equipment management. Through the intelligent management platform, various types of equipment and systems can be effectively integrated to achieve unified management and analysis of data, thereby improving these problems and promoting engineering equipment management to a higher level of intelligence and automation. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent management platform for engineering equipment based on the Internet of Things, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An intelligent management platform for engineering equipment based on the Internet of Things, comprising:

[0008] Data acquisition module: used to obtain analog data of target engineering equipment, status data of target engineering equipment and network data of target engineering equipment from target engineering equipment and sensors, form original data set of engineering equipment, and transmit it to data preprocessing module;

[0009] Data preprocessing module: used to perform preliminary data quality check on the original data set transmitted by the data acquisition module and convert analog-to-digital information to obtain unified digital information and then transmit it to the intelligent judgment module;

[0010] Intelligent judgment module: used to analyze the digital information transmitted by the data preprocessing module, establish a pattern recognition model, and transmit the pattern recognition signal to the environment detection module and the fault prediction and maintenance module;

[0011] Equipment performance analysis module: used to analyze the data information of the target engineering equipment transmitted by the data preprocessing module, establish a calculation model to measure the performance of the engineering equipment, generate comprehensive performance parameters, and transmit them to the fault prediction and maintenance module;

[0012] Equipment energy consumption analysis module: used to analyze the data information of the target engineering equipment transmitted by the data preprocessing module, establish a calculation model for calculating the energy consumption of engineering equipment, generate energy consumption parameters, and transmit them to the fault prediction and maintenance module;

[0013] Equipment collaboration analysis module: It is used to analyze the data information of the target engineering equipment transmitted by the data preprocessing module, establish a calculation model to optimize the equipment collaboration efficiency, generate equipment operation adjustment parameters, and transmit them to the fault prediction and maintenance module;

[0014] Equipment failure prediction module: used to analyze the data transmitted by the equipment performance analysis module, equipment energy consumption management module and equipment collaborative analysis module, and predict equipment failure based on the use and maintenance data of the target equipment;

[0015] User interaction output module: used to generate a maintenance report for the distribution parameters of the equipment failure time of the equipment failure prediction module, and make corresponding optimization adjustments or alarm prompts based on the report.

[0016] Preferably, in the data acquisition module, the analog data of the target engineering equipment include the temperature difference between the inside and outside of the engineering equipment, the radiation intensity inside the engineering equipment, the pressure difference between the inside and outside of the engineering equipment, and the voltage change rate of the target engineering equipment per unit time; the status data of the target engineering equipment include the operating status of the target engineering equipment, the set efficiency of the target engineering equipment in converting input energy into useful output per unit time, the actual operating speed of the target engineering equipment, the initial load of the target engineering equipment, the final load of the target engineering equipment, and the operating time of the target engineering equipment; the network data of the target engineering equipment include the frequency of use of the equipment, the start time of the equipment, the stop time of the equipment, and the signal value transmitted between devices.

[0017] Preferably, according to the IoT-based intelligent management platform for engineering equipment according to claim 1, it is characterized in that: the calculation steps of the analog-to-digital information conversion are as follows:

[0018] Step S01: Convert the i-th type of original data information set into digital information. The calculation model is as follows:

[0019]

[0020] Among them, D i represents the converted i-th digital information set, V represents the i-th original data set transmitted by the data acquisition module, V0 represents the minimum data in the i-th original data set transmitted by the data acquisition module, V r It represents the mean value of all data in the original data set of the i-th category transmitted by the data acquisition module;

[0021] Step S02: Remove the filter of the converted i-th type of digital information. The calculation model is as follows:

[0022]

[0023] Among them, f i Represents the digital information after filtering is removed, D i It is represented as the converted digital information set of the i-th category, and N is represented as the number of samples in the digital information set of the i-th category.

[0024] Preferably, the computational model of the pattern recognition is specifically expressed as:

[0025]

[0026] Among them, F represents the identification signal, k represents the number of data types transmitted by the data preprocessing module, x represents the device status data vector set, and f i Represented as the set of the i-th data point, c i Represented as cluster centers.

[0027] Preferably, the calculation model of the engineering equipment performance is specifically expressed as:

[0028]

[0029] Among them, O represents the comprehensive performance parameter of the target engineering equipment, α represents the thermal stability index of the internal components of the target equipment, and ΔT o It is represented by the temperature difference between the internal and external parts of the engineering equipment transmitted by the data preprocessing module, η0 is represented by the set efficiency of the target engineering equipment in converting input energy into useful output within unit time t, R oIt represents the radiation intensity inside the engineering equipment transmitted by the data preprocessing module, λ represents the attenuation rate of the influence of radiation intensity on equipment performance, C represents the flow coefficient of the target equipment, ΔP o It represents the pressure difference between the internal and external parts of the engineering equipment transmitted by the data preprocessing module, and b represents the wear rate of the target equipment.

[0030] Preferably, the calculation model of the energy consumption of the engineering equipment is specifically expressed as:

[0031]

[0032] Among them, E represents the working energy consumption parameter of the target engineering equipment, L0 represents the initial load of the target engineering equipment, and L f Expressed as the final load of the target engineering equipment, P E Expressed as the voltage change rate of the target engineering equipment per unit time, f t It is represented by the fluctuation frequency of the operating status of the target engineering equipment, and ω is represented by the actual operating speed of the target engineering equipment.

[0033] Preferably, the calculation model of the equipment collaboration efficiency is specifically expressed as:

[0034]

[0035] Where Q represents the collaborative efficiency of engineering equipment, t s It is represented as the startup time of the device, t f It represents the stop time of the equipment, P represents the work task priority, M represents the running status of the equipment, S j Indicates the signal value received by the target device, S k It is represented as the interference signal value of other device k.

[0036] Preferably, the calculation model for predicting equipment failure is specifically expressed as:

[0037]

[0038] Among them, ψ represents the distribution parameter of equipment failure time, T represents the simulation time, O represents the comprehensive performance parameter of the target engineering equipment, E represents the working energy consumption parameter of the target engineering equipment, and Q represents the collaborative efficiency of the engineering equipment.

[0039] Technical effects and advantages of the present invention:

[0040] 1. The present invention ensures that the data is accurate and conforms to the standard format through the data preprocessing module, which can simplify the subsequent data analysis process and speed up the data processing;

[0041] 2. The present invention uses the equipment performance analysis module to comprehensively evaluate the current status of the equipment through comprehensive performance parameters, reduce unnecessary maintenance work, and provide accurate data support for fault prediction and maintenance;

[0042] 3. The present invention establishes a calculation model for calculating the energy consumption of engineering equipment through the equipment energy consumption analysis module, which can accurately quantify the energy consumption of the equipment under different operating conditions and evaluate the changing trend of the equipment load and its impact on energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] As attached Figure 1 An intelligent management platform for engineering equipment based on the Internet of Things is shown, including a data acquisition module, a data preprocessing module, an intelligent judgment module, an equipment performance analysis module, an equipment energy consumption analysis module, an equipment collaborative analysis module, an equipment fault prediction module and a user interaction output module.

[0046] The data acquisition module is used to obtain analog data of the target engineering equipment, status data of the target engineering equipment and network data of the target engineering equipment from the target engineering equipment and sensors, form an original data set of the engineering equipment, and transmit it to the data preprocessing module.

[0047] The analog data of the target engineering equipment include the temperature difference between the inside and outside of the engineering equipment, the radiation intensity inside the engineering equipment, the pressure difference between the inside and outside of the engineering equipment, and the voltage change rate of the target engineering equipment per unit time.

[0048] The status data of the target engineering equipment includes the operating status of the target engineering equipment, the set efficiency of the target engineering equipment in converting input energy into useful output per unit time, the actual operating speed of the target engineering equipment, the initial load of the target engineering equipment, the final load of the target engineering equipment and the operating time of the target engineering equipment.

[0049] The network data of the target engineering equipment includes the usage frequency of the equipment, the start time of the equipment, the stop time of the equipment, and the signal value transmitted between the equipment.

[0050] The data preprocessing module is used to perform a preliminary data quality check on the original data set transmitted by the data acquisition module and convert the analog-to-digital information to obtain unified digital information and then transmit it to the intelligent judgment module.

[0051] The calculation steps of the analog-to-digital information conversion are as follows:

[0052] Step S01: Convert the i-th type of original data information set into digital information. The calculation model is as follows:

[0053]

[0054] Among them, D i represents the converted i-th digital information set, V represents the i-th original data set transmitted by the data acquisition module, V0 represents the minimum data in the i-th original data set transmitted by the data acquisition module, V r It represents the mean of all data in the i-th type of original data set transmitted by the data acquisition module.

[0055] Step S02: Remove the filter of the converted i-th type of digital information. The calculation model is as follows:

[0056]

[0057] Among them, f i Represents the digital information after filtering is removed, D i It is represented as the converted digital information set of the i-th category, and N is represented as the number of samples in the digital information set of the i-th category.

[0058] In this embodiment, it should be specifically explained that the data preprocessing module can ensure that the data for subsequent processing is accurate and conforms to the standard format by performing a preliminary data quality check on the received original data set and converting the analog signal into a digital signal through an analog-to-digital conversion function; by performing analog-to-digital conversion on the original data set transmitted by the data acquisition module, the analog signal can be converted into a digital signal, thereby ensuring the consistency and processability of the data, improving the comparability and analysis efficiency of the data, ensuring the consistency and standardization of the data, and reducing errors in the data processing process.

[0059] The intelligent judgment module is used to analyze the digital information transmitted by the data preprocessing module, establish a pattern recognition model, and transmit the pattern recognition signal to the environment detection module and the fault prediction and maintenance module.

[0060] The computational model of the pattern recognition is specifically expressed as follows:

[0061]

[0062] Among them, F represents the identification signal, k represents the number of data types transmitted by the data preprocessing module, x represents the device status data vector set, and f i Represented as the set of the i-th data point, c i Represented as cluster centers.

[0063] In this embodiment, it should be specifically explained that the recognition signal F in the pattern recognition module includes signal 0 and signal 1, signal 0 indicates that no fault is detected, and the data transmitted by the data preprocessing module are transmitted to the environment detection module, and signal 1 indicates that a fault is detected, and the signal is transmitted to the fault prediction and maintenance module; the argmax k Represented as output

[0064]

[0065] The largest value in .

[0066] In this embodiment, it should be specifically explained that the intelligent judgment module analyzes the data transmitted by the data preprocessing module and establishes a pattern recognition model, which can identify equipment abnormalities, intelligently judge whether the equipment needs maintenance or adjustment, and enhance the self-diagnosis capability of the equipment; by transmitting the pattern signal to the environmental detection module and the fault prediction and maintenance module, it can timely detect equipment abnormalities, reduce unplanned downtime, improve equipment utilization, accurately judge the equipment status, reduce false alarms and missed alarms, and improve the efficiency and accuracy of maintenance work; by working in collaboration with the environmental detection module and the fault prediction and maintenance module, it can form a closed-loop management to ensure that the equipment operates in the best condition and extend the life of the equipment.

[0067] The equipment performance analysis module is used to analyze the data information of the target engineering equipment transmitted by the data preprocessing module, establish a calculation model for measuring the performance of the engineering equipment, generate comprehensive performance parameters, and transmit them to the fault prediction and maintenance module.

[0068] The calculation model of the engineering equipment performance is specifically expressed as follows:

[0069]

[0070] Among them, O represents the comprehensive performance parameter of the target engineering equipment, α represents the thermal stability index of the internal components of the target equipment, and ΔT o It is represented by the temperature difference between the internal and external parts of the engineering equipment transmitted by the data preprocessing module, η0 is represented by the set efficiency of the target engineering equipment in converting input energy into useful output within unit time t, R o It represents the radiation intensity inside the engineering equipment transmitted by the data preprocessing module, λ represents the attenuation rate of the influence of radiation intensity on equipment performance, C represents the flow coefficient of the target equipment, ΔPo It represents the pressure difference between the internal and external parts of the engineering equipment transmitted by the data preprocessing module, and b represents the wear rate of the target equipment.

[0071] In this embodiment, it should be specifically explained that the thermal stability index is a fixed value of the adaptability and stability of the internal components of the equipment to temperature changes. A higher thermal stability index means that the equipment components can maintain performance unchanged over a wider temperature range; the temperature difference between the inside and outside of the equipment affects the heat exchange efficiency and thermal stress state inside the equipment. Excessive temperature difference leads to thermal fatigue and thus causes performance degradation of the engineering equipment; the radiation intensity inside the equipment is related to the heat generated by the equipment, affecting the thermal management and cooling requirements of the equipment. Excessive radiation intensity may cause the equipment to overheat, affecting performance and safety; the flow coefficient is related to the heat exchange capacity of the engineering equipment; the pressure difference between the inside and outside of the equipment affects the flow of the fluid inside the equipment, thereby affecting the equipment's transportation capacity, efficiency and operational stability.

[0072] In this embodiment, it should be specifically explained that the equipment performance analysis module monitors the temperature difference between the internal and external parts of the engineering equipment, evaluates the stability of the equipment components at different temperatures, ensures that the equipment can maintain efficient operation within a wide range of operating temperatures, and prevents thermal fatigue problems caused by excessive temperature differences, thereby ensuring the long-term stability of the equipment and reducing maintenance costs; by comprehensively calculating various parameters to obtain comprehensive performance parameters and timely transmitting the comprehensive performance parameters to the fault prediction and maintenance module, it can comprehensively evaluate the current status of the equipment, reduce unnecessary maintenance work, and provide accurate data support for fault prediction and maintenance.

[0073] The equipment energy consumption analysis module is used to analyze the data information of the target engineering equipment transmitted by the data preprocessing module, establish a calculation model for calculating the energy consumption of the engineering equipment, generate energy consumption parameters, and transmit them to the fault prediction and maintenance module.

[0074] The calculation model of the energy consumption of the engineering equipment is specifically expressed as follows:

[0075]

[0076] Among them, E represents the working energy consumption parameter of the target engineering equipment, L0 represents the initial load of the target engineering equipment, and L f Expressed as the final load of the target engineering equipment, P E Expressed as the voltage change rate of the target engineering equipment per unit time, f t It is represented by the fluctuation frequency of the operating status of the target engineering equipment, and ω is represented by the actual operating speed of the target engineering equipment.

[0077] In this embodiment, it should be specifically explained that the equipment energy consumption analysis module can accurately quantify the energy consumption of the equipment under different operating conditions and evaluate the changing trend of the equipment load and its impact on energy consumption by establishing a calculation model for calculating the energy consumption of engineering equipment; by integrating relevant parameters, quasi-energy consumption parameters are generated to promote early fault detection, reduce unplanned downtime, optimize equipment operation strategies, and reduce overall operating costs.

[0078] The equipment collaboration analysis module is used to analyze the data information of the target engineering equipment transmitted by the data preprocessing module, establish a calculation model for optimizing the equipment collaboration efficiency, generate equipment operation adjustment parameters, and transmit them to the fault prediction and maintenance module.

[0079] The calculation model of the equipment collaboration efficiency is specifically expressed as follows:

[0080]

[0081] Where Q represents the collaborative efficiency of engineering equipment, t s It is represented as the startup time of the device, t f It represents the stop time of the equipment, P represents the work task priority, M represents the running status of the equipment, S j Indicates the signal value received by the target device, S k It is represented as the interference signal value of other device k.

[0082] In this embodiment, it should be specifically explained that the calculation model of the equipment collaboration efficiency in the equipment collaboration analysis module quantifies the collaboration efficiency between devices; the priority of the work task is divided into three levels, P=3 for emergency tasks, P=2 for important tasks, and P=1 for general tasks; the operating status of the equipment has only two states, busy and idle, which are represented by 0 and 1 respectively.

[0083] In this embodiment, it should be specifically explained that the device collaboration analysis module can identify whether the device is busy or idle by monitoring the operating status of the device, optimize task scheduling, and ensure that urgent and important tasks are given priority; by analyzing the signal value received by the target device, the collaboration quality between devices is evaluated to improve the overall system performance; by generating device operation adjustment parameters and transmitting them to the fault prediction and maintenance module, the collaboration efficiency between devices is improved, ensuring that tasks are efficiently executed according to their priority and minimizing interference between devices.

[0084] The equipment failure prediction module is used to analyze the comprehensive performance parameters transmitted by the equipment performance analysis module, the working energy consumption parameters of the target engineering equipment transmitted by the equipment energy consumption management module, and the collaborative efficiency of the engineering equipment transmitted by the equipment collaborative analysis module, and predict equipment failure based on the usage and maintenance data of the target equipment.

[0085] The calculation model for predicting equipment failure is specifically expressed as follows:

[0086]

[0087] Among them, ψ represents the distribution parameter of equipment failure time, T represents the simulation time, O represents the comprehensive performance parameter of the target engineering equipment, E represents the working energy consumption parameter of the target engineering equipment, and Q represents the collaborative efficiency of the engineering equipment.

[0088] In this embodiment, it should be specifically explained that the fault prediction module fits the actual fault data well, thereby describing the distribution of equipment failure time, and formulating maintenance plans and replacement strategies based on the fault prediction results to reduce unexpected failures and repair costs; the simulation time is defined based on historical data.

[0089] In this embodiment, it should be specifically explained that the equipment failure prediction module constructs a distribution model of equipment failure time, predicts the failure probability and expected life of the equipment, and fits it with the actual failure data. It can formulate maintenance plans and replacement strategies in advance, reduce unplanned downtime, ensure the continuity of equipment operation, improve the overall reliability of the equipment, extend the service life of the equipment, and improve the efficiency and accuracy of equipment maintenance work.

[0090] The user interaction output module is used to generate a maintenance report for the distribution parameters of the equipment failure time of the equipment failure prediction module, and perform corresponding optimization adjustments or alarm prompts according to the report.

[0091] In this embodiment, it should be specifically explained that the user interaction output module generates a maintenance report based on the distribution parameters of the equipment failure time of the fault prediction module, lists the distribution of equipment failure time, including the average failure interval time, failure rate and failure maintenance report of the equipment, helps users understand the possibility and time point of failure, displays the failure probability and remaining life of the equipment at different time points, and provides preventive maintenance suggestions, including maintenance time and maintenance type.

[0092] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0093] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent management platform for engineering equipment based on the Internet of Things, characterized in that: include: Data acquisition module: used to obtain analog data of target engineering equipment, status data of target engineering equipment and network data of target engineering equipment from target engineering equipment and sensors, form original data set of engineering equipment, and transmit it to data preprocessing module; Data preprocessing module: used to perform preliminary data quality check on the original data set transmitted by the data acquisition module and convert analog-to-digital information to obtain unified digital information and then transmit it to the intelligent judgment module; Intelligent judgment module: used to analyze the digital information transmitted by the data preprocessing module, establish a pattern recognition model, and transmit the pattern recognition signal to the environment detection module and the fault prediction and maintenance module; Equipment performance analysis module: used to analyze the data information of the target engineering equipment transmitted by the data preprocessing module, establish a calculation model to measure the performance of the engineering equipment, generate comprehensive performance parameters, and transmit them to the fault prediction and maintenance module; Equipment energy consumption analysis module: used to analyze the data information of the target engineering equipment transmitted by the data preprocessing module, establish a calculation model for calculating the energy consumption of engineering equipment, generate energy consumption parameters, and transmit them to the fault prediction and maintenance module; Equipment collaboration analysis module: It is used to analyze the data information of the target engineering equipment transmitted by the data preprocessing module, establish a calculation model to optimize the equipment collaboration efficiency, generate equipment operation adjustment parameters, and transmit them to the fault prediction and maintenance module; Equipment failure prediction module: used to analyze the data transmitted by the equipment performance analysis module, equipment energy consumption management module and equipment collaborative analysis module, and predict equipment failure based on the use and maintenance data of the target equipment; User interaction output module: used to generate a maintenance report for the distribution parameters of the equipment failure time of the equipment failure prediction module, and make corresponding optimization adjustments or alarm prompts based on the report.

2. According to claim 1, an intelligent management platform for engineering equipment based on the Internet of Things is characterized by: The data acquisition module, the analog data of the target engineering equipment includes the temperature difference between the inside and outside of the engineering equipment, the radiation intensity inside the engineering equipment, the pressure difference between the inside and outside of the engineering equipment, and the voltage change rate of the target engineering equipment per unit time; the status data of the target engineering equipment includes the operating status of the target engineering equipment, the set efficiency of the target engineering equipment in converting input energy into useful output per unit time, the actual operating speed of the target engineering equipment, the initial load of the target engineering equipment, the final load of the target engineering equipment and the operating time of the target engineering equipment; the network data of the target engineering equipment includes the frequency of use of the equipment, the start time of the equipment, the stop time of the equipment, and the signal value transmitted between devices.

3. According to the Internet of Things-based intelligent management platform for engineering equipment according to claim 1, it is characterized by: The calculation steps of the analog-to-digital information conversion are as follows: Step S01: Convert the i-th type of original data information set into digital information. The calculation model is as follows: Among them, D i represents the converted i-th digital information set, V represents the i-th original data set transmitted by the data acquisition module, V0 represents the minimum data in the i-th original data set transmitted by the data acquisition module, V r It represents the mean value of all data in the original data set of the i-th category transmitted by the data acquisition module; Step S02: Remove the filter of the converted i-th type of digital information. The calculation model is as follows: Among them, f i Represents the digital information after filtering is removed, D i It is represented as the converted digital information set of the i-th category, and N is represented as the number of samples in the digital information set of the i-th category.

4. According to claim 1, an intelligent management platform for engineering equipment based on the Internet of Things is characterized by: The computational model of the pattern recognition is specifically expressed as follows: Among them, F represents the identification signal, k represents the number of data types transmitted by the data preprocessing module, x represents the device status data vector set, and f i Represented as the set of the i-th data point, c i Represented as cluster centers.

5. According to the Internet of Things-based intelligent management platform for engineering equipment according to claim 1, it is characterized by: The calculation model of the engineering equipment performance is specifically expressed as follows: Among them, O represents the comprehensive performance parameter of the target engineering equipment, α represents the thermal stability index of the internal components of the target equipment, and ΔT o It is represented by the temperature difference between the internal and external parts of the engineering equipment transmitted by the data preprocessing module, η0 is represented by the set efficiency of the target engineering equipment in converting input energy into useful output within unit time t, R o It represents the radiation intensity inside the engineering equipment transmitted by the data preprocessing module, λ represents the attenuation rate of the influence of radiation intensity on equipment performance, C represents the flow coefficient of the target equipment, ΔP o It represents the pressure difference between the internal and external parts of the engineering equipment transmitted by the data preprocessing module, and b represents the wear rate of the target equipment.

6. The intelligent management platform for engineering equipment based on the Internet of Things according to claim 1 is characterized by: The calculation model of the energy consumption of the engineering equipment is specifically expressed as follows: Among them, E represents the working energy consumption parameter of the target engineering equipment, L0 represents the initial load of the target engineering equipment, and L f Expressed as the final load of the target engineering equipment, P E Expressed as the voltage change rate of the target engineering equipment per unit time, f t It is represented by the fluctuation frequency of the operating status of the target engineering equipment, and ω is represented by the actual operating speed of the target engineering equipment.

7. The intelligent management platform for engineering equipment based on the Internet of Things according to claim 1 is characterized by: The calculation model of the equipment collaboration efficiency is specifically expressed as follows: Where Q represents the collaborative efficiency of engineering equipment, t s It is represented as the startup time of the device, t f It represents the stop time of the equipment, P represents the work task priority, M represents the running status of the equipment, S j Indicates the signal value received by the target device, S k It is represented as the interference signal value of other device k.

8. The intelligent management platform for engineering equipment based on the Internet of Things according to claim 1 is characterized by: The calculation model for predicting equipment failure is specifically expressed as follows: Among them, ψ represents the distribution parameter of equipment failure time, T represents the simulation time, O represents the comprehensive performance parameter of the target engineering equipment, E represents the working energy consumption parameter of the target engineering equipment, and Q represents the collaborative efficiency of the engineering equipment.

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