Intensive tagging system

Abstract

The application relates to the technical field of Internet of Things, in particular to an intensive label system, which comprises: basic labels distributed on each device to realize asset identity recognition and wireless data transmission, the basic labels being internally provided with electronic codes as digital identity cards; an acquisition module acquires environmental parameters and device parameters; a communication module transmits the environmental parameters and the device parameters to an analysis module based on preset transmission rules; the analysis module determines an environmental influence coefficient of an environment on a device according to a device type, environmental parameters, device parameters and an environment-device model to determine a device state, and determines an abnormal area according to the device state and device position information; and a warning module warns workers according to the digital identity cards and the abnormal area. The application forms a full closed-loop management system of "data acquisition-wireless transmission-intelligent analysis-warning response", and realizes identity identification and state monitoring of assets.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and more particularly to an integrated tagging system. Background Technology

[0002] In modern industrial production and enterprise operations, a large number of devices are distributed across different areas and their operating states are complex and variable. Existing technologies utilize RFID to achieve real-time equipment management, status monitoring, and fault early warning. For example, CNC machine tools can use RFID systems to collect data such as current, power consumption, and vibration to determine the equipment's operating status; after key equipment in power plants is equipped with RFID tags, inspection personnel can read equipment status, maintenance records, and other information in real time; it can also be combined with sensors to monitor equipment operating status in real time. In smart manufacturing, RFID tags on production equipment can record operating status, maintenance records, and service life.

[0003] Chinese Patent Application Publication No. CN119967460A discloses a method for monitoring and fault location of a passive indoor distribution system based on RFID tags. The steps are as follows: S1: The ID information of the RFID tag located at the indoor antenna in the indoor distribution system is read by the indoor distribution system status monitoring module, and the link loss value of the link between the indoor distribution system status monitoring module and the RFID tag is calculated. The link loss result is then uploaded to the cloud decision center; S2: The cloud decision center diagnoses the abnormal location in the indoor distribution system based on the data reported by the indoor distribution system status monitoring module, outputs the faulty device or line, generates a corresponding alarm work order and sends it to the installation and maintenance personnel for subsequent equipment maintenance.

[0004] It is evident that while the above technical solutions can more accurately locate the fault in the link and improve maintenance efficiency, they still have the following problems: the status monitoring function is limited to the communication link, it does not integrate multiple sensors, the degree of multi-sensor fusion is low, and the data collection coverage is insufficient, not involving multi-source data such as environmental parameters and performance parameters of equipment operation and maintenance. Summary of the Invention

[0005] To address these issues, the present invention provides an integrated tagging system to overcome the problems of traditional RFID asset management systems in the prior art, such as limited status monitoring functions, low degree of multi-sensor fusion, and insufficient coverage of equipment maintenance data collection.

[0006] To achieve the above objectives, the present invention provides an integrated labeling system, comprising:

[0007] Basic tags, distributed across various devices, are used for asset identification and wireless data transmission. These basic tags have built-in electronic codes that serve as digital identity cards.

[0008] The data acquisition module is connected to the basic tag and has several built-in sensors for collecting environmental parameters and equipment parameters. The environmental parameters include ambient temperature and ambient humidity, and the equipment parameters include operating current, operating temperature, and equipment location information.

[0009] A communication module, connected to the acquisition module, is used to determine preset transmission rules and transmit the environmental parameters and the device parameters to the analysis module based on the preset transmission rules;

[0010] An analysis module, which is connected to the acquisition module and the communication module respectively, is used to determine the environmental impact coefficient of the environment on the equipment based on the environmental parameters, equipment parameters and the environment-equipment model to determine the equipment status, and to determine the abnormal area based on the equipment type, the equipment status and the equipment location information, wherein the equipment status includes abnormal status and normal status;

[0011] The early warning module is connected to both the basic tag and the analysis module to issue corresponding early warning information based on the digital ID card and the abnormal area.

[0012] Furthermore, the integrated tagging system also includes a power consumption control module, which is connected to both the acquisition module and the analysis module, and is used to determine a power consumption control strategy for data interaction based on the device status, including:

[0013] If the device is in a normal state, the power consumption control module controls the positioning sensor used to obtain device location information to go into sleep mode.

[0014] If the device is in an abnormal state, the power consumption control module activates the positioning sensor to collect the device's location information.

[0015] Furthermore, the communication module determines the preset transmission rules, including:

[0016] A three-dimensional discrete system is constructed, and two sets of pseudo-random sequences are generated based on multiple preset parameter keys and multiple preset initial values ​​of state variables to encrypt the environmental parameters and the device parameters respectively, generating encrypted data and transmitting it to the analysis module.

[0017] Furthermore, after receiving the encrypted data, the analysis module runs the three-dimensional discrete system based on the preset parameter key and the preset initial value of the state variable to generate the two sets of pseudo-random sequences, and performs an XOR operation on the encrypted data and the two sets of pseudo-random sequences to restore the environmental parameters and the device parameters.

[0018] The expression for the three-dimensional discrete system is:

[0019]

[0020] Where x1, x2, x3 are state variables, a 11 ,a 12 ,a 13 ,a 21 ,a 22 ,a 23 ,a 31 ,a 32 ,a 33 ,ε1,σ1,ε2,σ2 are preset parameter keys, mod(·) represents the modulo operation, and mod(ε1x1(i),σ1) and mod(ε2x2(i),σ2) are feedback controllers.

[0021] Furthermore, after receiving the encrypted data, the analysis module runs the three-dimensional discrete system to generate the two sets of pseudo-random sequences, XORs the encrypted data with the two sets of pseudo-random sequences, and restores the environmental parameters and the equipment parameters.

[0022] Furthermore, the analysis module determines the hysteresis temperature and hysteresis humidity based on historical environmental parameters and historical equipment parameters, and constructs an environment-equipment model based on the historical equipment parameters, hysteresis temperature, and hysteresis humidity.

[0023] The historical environmental parameters include historical environmental temperature and historical environmental humidity.

[0024] The historical equipment parameters include historical operating current and historical operating temperature.

[0025] Furthermore, the analysis module constructs a temperature-device autocorrelation function and a humidity-device autocorrelation function based on the historical environmental parameters and the historical device parameters. It determines the temperature lag time and humidity lag time based on the temperature-device autocorrelation function and the humidity-device autocorrelation function, respectively, and determines the lag temperature and lag humidity based on the temperature lag time and the humidity lag time, respectively.

[0026] Furthermore, the analysis module determines the temperature influence factor and humidity influence factor based on the environmental parameters, equipment parameters, and the environment-equipment model, and determines the environmental influence coefficient based on the temperature influence factor and the humidity influence factor.

[0027] Furthermore, the analysis module determines the equipment status based on the environmental impact coefficient and the impact coefficient threshold, wherein,

[0028] If the environmental impact coefficient is greater than the impact coefficient threshold, the equipment status is determined to be abnormal.

[0029] If the environmental impact coefficient is less than or equal to the impact coefficient threshold, the equipment status is determined to be normal.

[0030] Furthermore, the analysis module determines the device area based on the device location information and preset area division rules, and determines the abnormal area based on the device status and the device area. The preset area division rules are determined based on the device type.

[0031] If the device status is abnormal, then the device area is an abnormal area;

[0032] If the device status is normal, then the device area is a normal area.

[0033] Furthermore, the early warning module stores an early warning maintenance binding mapping relationship, and determines the recipient of the early warning information based on the early warning maintenance binding mapping relationship, the digital ID card, and the abnormal area to send an early warning notification;

[0034] The early warning maintenance binding mapping relationship is the mapping relationship between each device and its corresponding digital ID card and the sending object.

[0035] Compared with existing technologies, the advantages of this invention lie in its proposed multimodal fusion integrated tag system. Using RFID communication technology as a carrier, it integrates multiple sensors within a single basic tag, breaking through the limitation of traditional RFID which can only carry identification information, and achieving integrated monitoring with multiple functions per tag. Simultaneously, it designs an encrypted transmission protocol based on decoupled physical channels to realize a ubiquitous self-organizing network between tags, ensuring the secure and reliable transmission of multi-dimensional data such as environmental and equipment parameters. Furthermore, it constructs an environment-equipment model to determine environmental impact coefficients, enabling real-time diagnosis of equipment status and precise location of abnormal areas. Ultimately, it forms a fully closed-loop management system encompassing "data acquisition - wireless transmission - intelligent analysis - early warning response - low-power integration," further enabling the identification and status monitoring of important / special assets, improving the intelligence and precision of equipment management, ensuring stable equipment operation, and reducing energy consumption.

[0036] Furthermore, this invention rationally allocates resources. During normal equipment operation, the power consumption control module keeps the positioning sensor in a dormant state, reducing energy consumption and preventing unnecessary operation of the positioning sensor during normal equipment operation. Simultaneously, when the equipment is in an abnormal state, the power consumption control module can promptly activate the positioning sensor and collect equipment location information, helping maintenance personnel quickly locate the abnormal location and take appropriate measures, thereby improving the overall operating efficiency of the equipment.

[0037] Furthermore, this invention sets up a three-dimensional discrete system with preset transmission rules. By utilizing the unique chaotic characteristics of the three-dimensional discrete system, environmental parameters and equipment parameters are encrypted during transmission, preventing data interception and unauthorized third parties from cracking the information. This effectively prevents data leakage and malicious attacks, builds a robust data security defense for equipment monitoring and maintenance, ensures complete, accurate, and orderly data transmission, and guarantees data reliability.

[0038] Furthermore, this invention mines the hysteresis relationship between environmental parameters and equipment parameters based on historical environmental parameters and historical equipment parameters, and captures the hysteresis effect of environmental changes on equipment. This enables more accurate equipment status prediction, provides early warning when equipment parameters are actually abnormal, reduces sudden failures, avoids over-responding to immediate environmental changes, improves the intelligence and precision of the system, ensures stable equipment operation, and reduces energy consumption.

[0039] Furthermore, this invention directly binds the device status to the area. When a single device in the area malfunctions, its area is quickly marked as an abnormal area, avoiding the need to check each device individually and improving fault location efficiency. At the same time, the preset area division rules are directly linked to the device type, ensuring that abnormalities of different types of devices can be classified into the most reasonable area unit. This facilitates the subsequent early warning module in determining the responsible person for the corresponding device based on the maintenance binding mapping relationship to send early warning notifications. This further enables the monitoring of the status of important / special assets, improves the intelligence and precision of equipment management, ensures stable equipment operation, and reduces energy consumption. Attached Figure Description

[0040] Figure 1 This is a connection block diagram of the integrated labeling system according to an embodiment of the present invention;

[0041] Figure 2 A flowchart for determining preset transmission rules in an embodiment of the present invention;

[0042] Figure 3 This is a flowchart for determining the hysteresis temperature and hysteresis humidity in an embodiment of the present invention;

[0043] Figure 4 This is a diagram showing the determination of the device status according to an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0045] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0046] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0047] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0048] Please see Figure 1 As shown, Figure 1 This is a connection block diagram of an integrated labeling system according to an embodiment of the present invention. Specifically, the present invention provides an integrated labeling system, including:

[0049] Basic tags, distributed across various devices, are used for asset identification and wireless data transmission. These basic tags have built-in electronic codes that serve as digital identity cards.

[0050] The data acquisition module is connected to the basic tag and has several built-in sensors for collecting environmental parameters and equipment parameters. The environmental parameters include ambient temperature and ambient humidity, and the equipment parameters include operating current, operating temperature, and equipment location information.

[0051] A communication module, connected to the acquisition module, is used to determine preset transmission rules and transmit the environmental parameters and the device parameters to the analysis module based on the preset transmission rules;

[0052] An analysis module, which is connected to the acquisition module and the communication module respectively, is used to determine the environmental impact coefficient of the environment on the equipment based on the environmental parameters, equipment parameters and the environment-equipment model to determine the equipment status, and to determine the abnormal area based on the equipment type, the equipment status and the equipment location information, wherein the equipment status includes abnormal status and normal status;

[0053] The early warning module is connected to both the basic tag and the analysis module to issue corresponding early warning information based on the digital ID card and the abnormal area.

[0054] Understandably, traditional asset management relies on manual ledgers or single RFID tags, failing to link equipment status data in real time, resulting in an information gap of "identified but not status-based." Environmental sensors (such as temperature and humidity) and equipment sensors (such as current and temperature) are deployed independently, leading to severe data silos. For example, increased environmental humidity may exacerbate equipment circuit corrosion, but traditional systems cannot link the data from both to predict faults. Furthermore, the system lacks linkage between geolocation and status data, making it difficult to quickly pinpoint specific equipment and areas when anomalies occur. Basic tags, acting as digital IDs for equipment, feature built-in electronic codes that uniquely identify each device. Wireless data transmission facilitates rapid reading and management of equipment information. The acquisition module incorporates multiple sensors, allowing for different acquisition frequencies to be set for different sensor types. This comprehensive collection of environmental and equipment parameters provides detailed data for subsequent equipment status analysis. For instance, environmental temperature and humidity sensors monitor whether the equipment's operating environment meets requirements, operating current and temperature sensors directly reflect the equipment's operating status, and location sensors determine the equipment's location. The communication module can encrypt and transmit various parameters acquired by the acquisition module to the analysis module according to preset transmission rules, ensuring reliable encrypted data flow within the system. The analysis module determines whether equipment is in an abnormal or normal state by calculating the environmental impact coefficient. Combined with equipment location information, it identifies abnormal areas, enabling in-depth analysis of equipment status and precise location of problematic equipment. The early warning module identifies equipment based on its digital ID tag and, combined with the abnormal areas identified by the analysis module, issues warnings to staff, allowing them to quickly pinpoint the problematic equipment and area and take timely action. The power consumption control module intelligently adjusts power consumption based on the equipment status determined by the analysis module. During normal equipment operation, the positioning sensors are put into sleep mode to reduce unnecessary energy consumption; when equipment malfunctions, the positioning sensors are activated and the sampling frequency of each sensor is increased to ensure more detailed and accurate information is obtained for troubleshooting and handling.

[0055] In one specific embodiment, the integrated tag system also includes an energy harvesting module (such as a piezoelectric element + BQ25570 chip), which can convert mechanical energy in the environment, such as vibration and pressure, into electrical energy, thereby enabling the integrated tag system to be self-powered. Through circuit design, power is prioritized for modules such as the acquisition module, analysis module, and early warning module, so that the integrated tag system can still maintain basic functions without batteries, providing emergency power outage capability and improving system reliability.

[0056] This invention proposes a multimodal fusion integrated tagging system. Using RFID communication technology as a carrier, it integrates multiple sensors within a single basic tag, overcoming the limitation of traditional RFID which can only carry identification information, and achieving integrated monitoring with multiple functions per tag. Simultaneously, it designs an encrypted transmission protocol based on decoupled physical channels to realize a ubiquitous self-organizing network between tags, ensuring the secure and reliable transmission of multi-dimensional data such as environmental and equipment parameters. Furthermore, it constructs an environment-equipment model to determine environmental impact coefficients, enabling real-time diagnosis of equipment status and precise location of abnormal areas. Ultimately, it forms a closed-loop management system encompassing "data acquisition - wireless transmission - intelligent analysis - early warning response - low-power integration," further enabling the identification and status monitoring of important / special assets, improving the intelligence and precision of equipment management, ensuring stable equipment operation, and reducing energy consumption.

[0057] Specifically, the integrated tagging system further includes a power consumption control module, which is connected to both the acquisition module and the analysis module, and is used to determine a power consumption control strategy for data interaction based on the device status, including:

[0058] If the device is in a normal state, the power consumption control module controls the positioning sensor used to obtain device location information to go into sleep mode.

[0059] If the device is in an abnormal state, the power consumption control module activates the positioning sensor to collect the device's location information.

[0060] Understandably, the power consumption control module is connected to both the acquisition module and the fusion module, enabling it to acquire device status information in real time and dynamically adjust the operating mode of the positioning sensor based on different device states. When the device is in a normal state, it indicates that the device is operating stably according to the established process and expectations. At this time, the positioning sensor is not essential for the routine monitoring of the device's operation. Based on this, the power consumption control module will issue a command to put the positioning sensor into a sleep state. In the sleep state, the positioning sensor stops acquiring location information to avoid unnecessary power consumption waste. When the device state changes to an abnormal state, such as when the device malfunctions, the power consumption control module can quickly activate the positioning sensor. The acquisition module then collects the device's location information and transmits it to the analysis module for subsequent identification of the abnormal area.

[0061] This invention rationally allocates resources. During normal equipment operation, the power consumption control module keeps the positioning sensor in a dormant state, reducing energy consumption and preventing unnecessary operation of the positioning sensor during normal equipment operation. Simultaneously, when the equipment is in an abnormal state, the power consumption control module can promptly activate the positioning sensor and collect the equipment's location information, helping maintenance personnel quickly locate the abnormal position and take appropriate measures, thereby improving the overall operating efficiency of the equipment.

[0062] Please see Figure 2 The diagram shows a flowchart illustrating the process of determining a preset transmission rule according to an embodiment of the present invention. Specifically, the communication module determines the preset transmission rule by:

[0063] A three-dimensional discrete system is constructed, and two sets of pseudo-random sequences are generated based on multiple preset parameter keys and multiple preset initial values ​​of state variables to encrypt the environmental parameters and the device parameters respectively, generating encrypted data and transmitting it to the analysis module;

[0064] The expression for the three-dimensional discrete system is:

[0065]

[0066] Where x1, x2, x3 are state variables, a 11 ,a 12 ,a 13 ,a 21 ,a 22 ,a 23 ,a 31 ,a 32 ,a 33 ,ε1,σ1,ε2,σ2 are preset parameter keys, mod(·) represents the modulo operation, and mod(ε1x1(i),σ1) and mod(ε2x2(i),σ2) are the calculation results of the feedback controller.

[0067] It is understandable that mod(·) represents the modulo operation. The modulo operation can introduce a nonlinear feedback mechanism to form feedback controllers mod(ε1x1(i),σ1) and mod(ε2x2(i),σ2), so that the calculation results of the three-dimensional discrete system fluctuate within a certain range, thereby enhancing the complexity and randomness of the system and helping to generate more difficult-to-predict pseudo-random sequences for encryption.

[0068] In one specific embodiment, the initial values ​​of the plurality of state variables are the initial values ​​of environmental parameters and the initial values ​​of device parameters. Hash operations can be performed on the environmental parameters and the device parameters respectively to obtain the initial values ​​of environmental parameters and the initial values ​​of device parameters.

[0069] Specifically, after receiving the encrypted data, the analysis module runs the three-dimensional discrete system based on the preset parameter key and the preset initial value of the state variable to generate the two sets of pseudo-random sequences, and performs an XOR operation on the encrypted data and the two sets of pseudo-random sequences to restore the environmental parameters and the device parameters.

[0070] Understandably, equipment monitoring data often contains sensitive information such as equipment operating status, key performance indicators, and location. If this data is leaked or tampered with, it may increase the risk of equipment failure. At the end of the production process, the three-dimensional discrete system is highly complex and unpredictable. Its unique chaotic characteristics allow the data to be encrypted during transmission. Even if the data is intercepted during transmission, it is difficult for an unauthorized third party to decipher the information, which can effectively prevent data leakage and malicious supply.

[0071] In a specific embodiment, the expression for the three-dimensional discrete system is:

[0072]

[0073] Where x1, x2, x3 are state variables, a 11 ,a 12 ,a 13 ,a 21 ,a 22 ,a 23 ,a 31 ,a 32 ,a 33 ε1, σ1, ε2, σ2 are preset parameter keys, mod(·) represents the modulo operation, and mod(ε1x1(i), σ1) and mod(ε2x2(i), σ2) are feedback controllers. The parameter values ​​are as follows:

[0074] a 11 =0.325,a 12 =-0.225,a 13 =-0.955,a 21 =-0.045,a 22 =0.145,a 23 =-0.775,a 31 =0.18,a 32 =-0.18,a 33 =0.37, ε1=6×10 3 σ1=1×10 5 ε2=4×10 3 σ² = 2 × 10 5 When the three-dimensional discrete system is in the given state, the three Lyapunov exponents are:

[0075] LE1=1.8915, LE2=1.8877, LE3=1.87,

[0076] Those skilled in the art will recognize that the Lyapunov exponent represents the numerical characteristic of the average exponential divergence rate of adjacent trajectories in phase space, and is one of the numerical features used to identify chaotic motion. Since all three Lyapunov exponents are greater than zero, it indicates that the three-dimensional discrete system exhibits chaotic behavior simultaneously in multiple independent directions. Therefore, under the above parameter values, the three-dimensional discrete system behaves as a hyperchaotic system, and the signal of a hyperchaotic system is more complex and resistant to disruption.

[0077] In this invention, the communication module is the encryption end, and the analysis module is the decryption end. Environmental parameters and device parameters are read byte-by-byte and denoted as m1(i) and m2(i), respectively. At the encryption end, the state variables x1, x2, x3 (denoted as m1, m2, m3) generated iteratively by the hyperchaotic system (three-dimensional discrete system) of the communication module are used. Two different pseudo-random sequences are constructed to encrypt m1(i) and m2(i). The encrypted environment parameters and device parameters are denoted as p1(i) and p2(i), respectively. The specific mathematical expressions are as follows:

[0078]

[0079] In the formula, floor(·) represents rounding down, and the symbol is... This indicates a bitwise XOR operation.

[0080] Similarly, during the decryption process in the analysis module, the hyperchaotic system of the iterative analysis module generates state variables x1, x2, x3 (denoted as...). ),use Two sets of pseudo-random sequences are constructed to decrypt p1(i) and p2(i), and the decrypted data is denoted as p1(i) and p2(i). and The specific mathematical expressions are as follows:

[0081]

[0082] When the initial values ​​of the environmental parameters, device parameters, and preset key of the analysis module's hyperchaotic system are equal to those of the communication module's hyperchaotic system, the state variables are equal, and decryption can be performed correctly. That is, for each byte, there is... Otherwise, decryption will fail.

[0083] This invention establishes a three-dimensional discrete system with preset transmission rules. By utilizing the unique chaotic characteristics of the three-dimensional discrete system, environmental and equipment parameters are encrypted during transmission to prevent data interception and unauthorized third parties from cracking the information. This effectively prevents data leakage and malicious attacks, builds a robust data security defense for equipment monitoring and maintenance, ensures complete, accurate, and orderly data transmission, and guarantees data reliability.

[0084] Specifically, the analysis module determines the hysteresis temperature and hysteresis humidity based on historical environmental parameters and historical equipment parameters, and constructs an environment-equipment model based on the historical equipment parameters, hysteresis temperature, and hysteresis humidity.

[0085] The historical environmental parameters include historical environmental temperature and historical environmental humidity.

[0086] The historical equipment parameters include historical operating current and historical operating temperature.

[0087] It is understandable that the equipment itself has heat capacity during operation. When the ambient temperature changes, heat needs to be gradually transferred to the inside of the equipment through conduction, convection, and other means. The thermal resistance of the multi-layered structure will delay the temperature response, and the thermal resistance of each layer will cause a lag in the change of the equipment's operating temperature. At the same time as the equipment's operating temperature changes, the operating current will also be affected and fluctuate. Furthermore, when the ambient temperature is lower than the dew point temperature of the equipment surface, water vapor in the air will condense into liquid water, which will have a cumulative effect, causing short circuits and corrosion. Therefore, the impact of ambient humidity on equipment parameters also has a lag. Changes in ambient temperature and humidity will not be immediately reflected in equipment parameters; there is a certain time delay. This lag relationship can be explored by analyzing historical environmental parameters and historical equipment parameters.

[0088] In a specific embodiment, using historical equipment parameters (operating current and operating temperature), hysteresis temperature, and hysteresis humidity as input features, and temperature influence factor and humidity influence factor as output features, the environment-equipment model is constructed as follows:

[0089]

[0090] Wherein, α1, α2, and α3 are the weighting coefficients of the normalized equipment operating temperature, operating current, and hysteresis environmental parameters (hysteresis temperature and hysteresis humidity) on the influencing factors.

[0091] Wherein, α1 ranges from 0.3 to 0.35, preferably 0.32; α2 ranges from 0.3 to 0.35, preferably 0.32; and α3 ranges from 0.3 to 0.4, preferably 0.36. In implementation, the ranges and preferred values ​​of α1, α2, and α3 can be determined according to actual conditions, and are not specifically limited here, nor will they be elaborated further. The sum of α1, α2, and α3 is 1.

[0092] Please see Figure 3As shown, it is a flowchart for determining hysteresis temperature and hysteresis humidity in an embodiment of the present invention; specifically, the analysis module constructs a temperature-device autocorrelation function and a humidity-device autocorrelation function based on the historical environmental parameters and the historical device parameters, determines the temperature hysteresis time and humidity hysteresis time based on the temperature-device autocorrelation function and the humidity-device autocorrelation function respectively, and determines the hysteresis temperature and hysteresis humidity based on the temperature hysteresis time and the humidity hysteresis time respectively.

[0093] Understandably, the temperature-equipment autocorrelation function can be used to analyze the correlation between historical ambient temperature and historical equipment parameters, identifying the time difference corresponding to the maximum correlation, i.e., the temperature lag time. Similarly, the humidity-equipment autocorrelation function can be used to find the humidity lag time of the impact of humidity changes on equipment parameters. This allows for the determination of the lag temperature and lag humidity.

[0094] In a specific embodiment, the analysis module determines the temperature correlation between ambient temperature and equipment parameters at different lag times τ based on the historical ambient temperature at time point t and the historical equipment parameters at time point t+τ (which can be obtained according to the correlation coefficient formula). The temperature correlation value is [-1, 1]. By traversing different lag times, the lag time τ at which the temperature correlation reaches its peak can be found, which is the temperature lag time. The humidity lag time is calculated similarly. Based on the temperature lag time, the lag temperature X(t-temperature lag time) corresponding to the historical ambient temperature at the measured current ambient temperature at the temperature lag time point can be extracted. Based on the humidity lag time, the lag humidity Y(t-humidity lag time) corresponding to the historical ambient humidity at the measured current ambient humidity at the humidity lag time point can be extracted.

[0095] This invention mines the hysteresis relationship between environmental and equipment parameters based on historical environmental and equipment parameters, and captures the hysteresis effect of environmental changes on equipment. This enables more accurate equipment status prediction, provides early warning when equipment parameters are actually abnormal, reduces sudden failures, avoids over-responding to immediate environmental changes, improves the intelligence and precision of the system, ensures stable equipment operation, and reduces energy consumption.

[0096] Specifically, the analysis module determines the temperature influence factor and humidity influence factor based on the environmental parameters, equipment parameters, and the environment-equipment model, and determines the environmental influence coefficient based on the temperature influence factor and the humidity influence factor.

[0097] In a specific embodiment, the temperature influence factor and humidity influence factor can be calculated by substituting the environmental parameters and the equipment parameters into the environment-equipment model. The environmental influence coefficient = first weight × temperature influence factor + second weight × humidity influence factor. The sum of the first weight and the second weight is 1. Since environmental temperature has a greater impact on the equipment's operating state than environmental humidity, the first weight is greater than the second weight. The value range of the first weight is 0.6 to 0.8, preferably 0.7. The value range of the second weight is 0.2 to 0.4, preferably 0.3. In practice, the value range and preferred values ​​of the first and second weights can be determined according to the actual situation, and are not specifically limited here, nor will they be elaborated further.

[0098] Please see Figure 4 As shown, this is a determination diagram of the equipment status according to an embodiment of the present invention. Specifically, the analysis module determines the equipment status based on the environmental influence coefficient and the influence coefficient threshold, wherein...

[0099] If the environmental impact coefficient is greater than the impact coefficient threshold, the equipment status is determined to be abnormal.

[0100] If the environmental impact coefficient is less than or equal to the impact coefficient threshold, the equipment status is determined to be normal.

[0101] It is understood that the environmental impact coefficient is a value between 0 and 1, and the threshold value of the impact coefficient is a preset critical value based on the environmental tolerance, reliability requirements, and application scenario of the equipment. If the environmental impact coefficient is greater than the threshold value, it indicates that the environmental impact exceeds the safe range of the equipment design, which may lead to performance degradation, failure, or shortened lifespan. If the environmental impact coefficient is less than the threshold value, it is considered that the environmental impact is within the acceptable range of the equipment, and no intervention is required.

[0102] In a specific embodiment, the influence coefficient threshold value ranges from 0.6 to 0.75. Preferably, the influence coefficient threshold value is 0.65. In practice, the range and preferred value of the influence coefficient threshold value can be determined based on the sensitivity of the actual equipment and the influence coefficient corresponding to historical equipment failures. No specific limitation is made here, nor will it be elaborated further.

[0103] Specifically, the analysis module determines the device area based on device location information and preset area division rules, and determines abnormal areas based on the device status and the device area. The preset area division rules are determined based on the device type.

[0104] If the device status is abnormal, then the device area is an abnormal area;

[0105] If the device status is normal, then the device area is a normal area.

[0106] In a specific embodiment, the preset area division rule can be based on the device type to divide the area (for example, dividing devices of the same type into the same area, or dividing devices with the same environmental requirements into the same area) to divide the location range of the devices into several device areas. The corresponding device area is determined based on the device location information of each device. Then, when the device is in an abnormal state, state mapping is performed. The device area of ​​the corresponding device with an abnormal state is defined as an abnormal area, and the device area with all devices in a normal state is defined as a normal area. The abnormal area can automatically trigger the corresponding control and warning strategy.

[0107] Specifically, the early warning module stores an early warning maintenance binding mapping relationship. Based on the early warning maintenance binding mapping relationship, the digital ID card, and the abnormal area, the target of the early warning information is determined to send the early warning notification.

[0108] The early warning maintenance binding mapping relationship is the mapping relationship between each device and its corresponding digital ID card and the sending object.

[0109] In a specific embodiment, when the device status of a single device becomes abnormal, the analysis module can determine the corresponding abnormal area based on the device status of the single device. By querying and maintaining the binding mapping relationship based on the digital ID card (electronic code) corresponding to the single device, the responsible person (sending object), the device area to which it belongs, and the device type can be obtained. The device area to which it belongs is the abnormal area. The device manager can be notified by sending an SMS to the responsible person, marking the abnormal location on a mobile phone or operation and maintenance app and sending information notification. The device area of ​​the device can be updated in real time based on the device location information.

[0110] This invention directly binds the device status to the device area. When a single device in the area malfunctions, its area is quickly marked as an abnormal area, avoiding the need to check each device individually and improving fault location efficiency. At the same time, the preset area division rules are directly linked to the device type, ensuring that abnormalities of different types of devices can be classified into the most reasonable area unit. This facilitates the subsequent early warning module to determine the responsible person for the corresponding device based on the maintenance binding mapping relationship and send early warning notifications. This further enables the status monitoring of important / special assets, improves the intelligence and precision of equipment management, ensures stable equipment operation, and reduces energy consumption.

[0111] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An integrated labeling system, characterized in that, include: Basic tags, distributed across various devices, are used for asset identification and wireless data transmission. These basic tags have built-in electronic codes that serve as digital identity cards. The data acquisition module is connected to the basic tag and has several built-in sensors for collecting environmental parameters and equipment parameters. The environmental parameters include ambient temperature and ambient humidity, and the equipment parameters include operating current, operating temperature, and equipment location information. A communication module, connected to the acquisition module, is used to determine preset transmission rules and transmit the environmental parameters and the device parameters to the analysis module based on the preset transmission rules; The analysis module, connected to both the acquisition module and the communication module, is used to determine the hysteresis temperature and hysteresis humidity based on historical environmental parameters and historical equipment parameters, including... Based on the historical environmental parameters and the historical equipment parameters, a temperature-equipment autocorrelation function and a humidity-equipment autocorrelation function are constructed. The temperature lag time and the humidity lag time are determined based on the temperature-equipment autocorrelation function and the humidity-equipment autocorrelation function, respectively. The lag temperature and the lag humidity are determined based on the temperature lag time and the humidity lag time, respectively. The temperature lag time is determined by analyzing the correlation between historical ambient temperature and historical equipment parameters using the temperature-equipment autocorrelation function to find the time difference corresponding to the maximum correlation. The humidity lag time is determined by analyzing the correlation between historical environmental humidity and historical equipment parameters using the humidity-equipment autocorrelation function to find the time difference corresponding to the maximum correlation. And construct an environment-equipment model based on the historical equipment parameters, hysteresis temperature, and hysteresis humidity; And determine the temperature influence factor and humidity influence factor based on the environmental parameters, equipment parameters and the environment-equipment model respectively, and determine the environmental influence coefficient based on the temperature influence factor and the humidity influence factor; And determine the equipment status based on the environmental impact coefficient and the impact coefficient threshold, wherein, If the environmental impact coefficient is greater than the impact coefficient threshold, the equipment status is determined to be abnormal. If the environmental impact coefficient is less than or equal to the impact coefficient threshold, the equipment status is determined to be normal. The historical environmental parameters include historical environmental temperature and historical environmental humidity; The historical equipment parameters include historical operating current and historical operating temperature; And determine abnormal areas based on device type, device status and device location information, wherein the device status includes abnormal status and normal status; The early warning module is connected to both the basic tag and the analysis module to issue corresponding early warning information based on the digital ID card and the abnormal area.

2. The integrated labeling system according to claim 1, characterized in that, The centralized tagging system further includes a power consumption control module, which is connected to both the acquisition module and the analysis module. This module is used to determine a power consumption control strategy for data interaction based on the device status, including: If the device is in a normal state, the power consumption control module controls the positioning sensor used to obtain device location information to go into sleep mode. If the device is in an abnormal state, the power consumption control module activates the positioning sensor to collect the device's location information.

3. The integrated labeling system according to claim 1, characterized in that, The communication module determines the preset transmission rules, including: A three-dimensional discrete system is constructed, and two sets of pseudo-random sequences are generated based on multiple preset parameter keys and multiple preset initial values ​​of state variables to encrypt the environmental parameters and the device parameters respectively, generating encrypted data and transmitting it to the analysis module.

4. The integrated labeling system according to claim 3, characterized in that, After receiving the encrypted data, the analysis module runs the three-dimensional discrete system based on the preset parameter key and the preset initial value of the state variable to generate the two sets of pseudo-random sequences. The encrypted data is then XORed with the two sets of pseudo-random sequences to restore the environmental parameters and the device parameters.

5. The integrated labeling system according to claim 4, characterized in that, The analysis module determines the device region based on device location information and preset region division rules, and determines abnormal regions based on the device status and device region. The preset region division rules are determined based on the device type. If the device status is abnormal, then the device area is an abnormal area; If the device status is normal, then the device area is a normal area.

6. The integrated labeling system according to claim 5, characterized in that, The early warning module stores an early warning maintenance binding mapping relationship. Based on the early warning maintenance binding mapping relationship, the digital ID card, and the abnormal area, the target of the early warning information is determined to send the early warning notification. The early warning maintenance binding mapping relationship is the mapping relationship between each device and its corresponding digital ID card and the sending object.

Images