Miniature crane protection system and protection method

Abstract

The invention discloses a miniature crane protection system and a protection method. The miniature crane protection system comprises a sensing detection module, an embedded intelligent decision and data platform module, an execution protection module and a power supply module, the sensing detection module is used for collecting operation parameters of the micro crane in real time, and the operation parameters comprise load data, crane inclination angle data, distance data between the crane and surrounding obstacles, power supply voltage data and equipment state data. Through the priority and relevance algorithm of the middle station module, the problem of traditional single judgment logic is solved, the core risk can be accurately identified when multiple hidden dangers are overlapped, excessive protection or insufficient protection is avoided, and the accident rate is effectively reduced; the storage and prediction functions of the middle station module upgrade the system from'post-response 'to'active prevention', the service life and potential hazards of parts are pre-judged by analyzing historical data, the number of times of non-planned shutdown maintenance is reduced, and certain maintenance cost is saved.

Description

Technical Field

[0001] This invention relates to the field of miniature crane technology, specifically to a miniature crane protection system and protection method. Background Technology

[0002] Mini cranes are widely used in construction, logistics, and home renovation due to their small size, flexible operation, and portability. However, in actual use, mini cranes often face safety hazards such as overloading, tilting, collisions, and power outages, which can easily lead to equipment damage, falling goods, and even personal injury or death. Currently, the existing safety measures for mini cranes are relatively simple, mostly relying on the operator's experience for judgment and control, lacking effective automatic detection and protection mechanisms. Although some cranes are equipped with simple overload alarm devices, they are significantly inadequate in terms of tilt protection, collision warning, and power failure protection. Furthermore, they suffer from problems such as simplistic safety hazard judgment logic and a lack of data storage and intelligent analysis capabilities, resulting in poor protective performance and an inability to fully guarantee the safe operation of the cranes. Therefore, we propose a miniature crane protection system and protection method. Summary of the Invention

[0003] The purpose of this invention is to provide a miniature crane protection system and method, which solves the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a miniature crane protection system, comprising a sensing and detection module, an embedded intelligent decision-making and data platform module, an execution protection module, and a power supply module; The sensing and detection module is used to collect the operating parameters of the mini crane in real time. The operating parameters include load data, crane tilt angle data, distance data between the crane and surrounding obstacles, power supply voltage data, and equipment status data. The embedded intelligent decision-making and data platform module is connected to the perception and detection module and the execution protection module, respectively. It is used to receive the operating parameters collected by the perception and detection module, process the operating parameters to determine the priority and correlation of safety hazards, store the operating parameters and hazard determination results, and perform predictive analysis based on the stored data to generate protection instructions. The execution protection module is used to receive protection instructions from the embedded intelligent decision-making and data platform module and execute corresponding protection actions; The power module is used to provide a stable power supply for the sensing and detection module, the embedded intelligent decision-making and data platform module, and the execution protection module.

[0005] In a preferred embodiment of the present invention, the sensing and detection module includes a weight sensor, a tilt sensor, a distance sensor, a voltage sensor, and a speed sensor; The weight sensor is installed at the hook of the crane to collect load data; The tilt sensor is installed on the main body of the crane and is used to collect the tilt angle data of the crane. The distance sensors are installed around the crane body to collect distance data between the crane and surrounding obstacles; The voltage sensor is installed at the power input terminal of the crane and is used to collect power voltage data; The speed sensor is installed on the output shaft of the crane motor and is used to collect motor speed data from the equipment status data.

[0006] As a preferred embodiment of the present invention, the embedded intelligent decision-making and data platform module includes a hardware architecture and software functional modules; The hardware architecture adopts an integrated design of core board and expansion board, and the expansion board integrates storage submodule, communication submodule and interface submodule; The storage submodule is used to store operating parameters, hazard assessment results, and key configuration parameters; The communication submodule is used to enable bidirectional data interaction between the embedded intelligent decision-making and data platform module and the external cloud. The interface submodule is used to connect the sensing and detection module and the external display device to realize data transmission and information display. The software functional modules adopt a three-layer architecture of driver layer, core algorithm layer and application layer. The driver layer is used to drive peripheral devices to ensure stable data acquisition and transmission. The core algorithm layer is used to realize the priority and correlation determination of hidden dangers and predictive analysis. The application layer is used to output protection commands and display the hidden danger determination results and prediction reports.

[0007] In a preferred embodiment of the present invention, the storage submodule includes a flash memory unit and a ferroelectric memory unit; The flash memory unit is used to store operating parameters and potential hazard assessment results for a preset duration; The ferroelectric storage unit is used to store key configuration parameters, and these key configuration parameters are not lost after power failure.

[0008] In a preferred embodiment of the present invention, the execution protection module includes an alarm unit, a braking unit, and a power-off unit; The alarm unit is used to issue alarm signals of different modes according to the protection instructions; The braking unit is used to brake the hoisting mechanism and traveling mechanism of the crane according to the protection command; The power-off unit is used to cut off the power supply to the crane according to the protection command.

[0009] A method for protecting a miniature crane includes the following steps: Step 1: Parameter Acquisition Step: The sensing and detection module collects the operating parameters of the mini crane in real time and transmits the operating parameters to the embedded intelligent decision-making and data platform module; Step 2, Hazard Identification and Data Synchronization Processing: The embedded intelligent decision-making and data platform module preprocesses the operating parameters, determines the priority and correlation of safety hazards based on the preprocessed parameters, generates protection instructions, and stores the operating parameters, hazard identification results and protection instructions according to the preset strategy. Step 3: Protection Execution Steps: The protection module receives the protection command and executes the corresponding protection action according to the command; Step 4, Predictive Protection Steps: During non-operational periods or work breaks of the crane, the embedded intelligent decision-making and data platform module performs predictive analysis based on stored operating parameters and hazard assessment results, generates a prediction report, and stores or transmits the prediction report to external devices.

[0010] In a preferred embodiment of the present invention, in step two, the embedded intelligent decision-making and data platform module determines the priority of security risks through a preset scoring model. When multiple security risks are superimposed, the correlation between the risks is analyzed to correct the protection instructions and ensure that the protection instructions match the core risks.

[0011] In a preferred embodiment of the present invention, in step two, the preset storage strategy includes: storing the operating parameters and hazard judgment results for the first preset duration in minutes, storing the operating parameters and hazard judgment results for the period from the first preset duration to the second preset duration in hours, and compressing and transmitting the operating parameters and hazard judgment results for the period exceeding the second preset duration to the external cloud.

[0012] In a preferred embodiment of the present invention, step four includes predicting the remaining lifespan of the crane's key components and the potential hidden danger trends within a preset future period. The prediction report includes maintenance recommendations for the key components and preventive measures for potential hidden dangers.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention solves the problem of traditional single-judgment logic by using priority and correlation algorithms in the middleware module. When multiple hidden dangers overlap, it can accurately identify core risks, avoid over-protection or under-protection, and effectively reduce the accident rate. The storage and prediction functions of the middleware module upgrade the system from "post-event response" to "proactive prevention". By analyzing historical data, it predicts the lifespan of components and potential hidden dangers, reducing the number of unplanned downtime maintenance and saving maintenance costs. The middleware module adopts an integrated hardware design, replacing the traditional separate "computing + storage + communication" modules, which significantly reduces hardware costs. Its compact size allows it to be directly embedded in the control cabinet, shortening the retrofit cycle of existing cranes. At the same time, it reduces operation interruptions caused by false triggering of protection, and combined with predictive maintenance, reduces downtime and significantly improves crane operation efficiency. Attached Figure Description

[0014] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart of a miniature crane protection method according to the present invention; Figure 2 This is a framework diagram of a miniature crane protection system according to the present invention. Detailed Implementation

[0015] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0016] like Figure 1-2 As shown, a miniature crane protection system and method are described, and the system consists of the following components: Perception and detection module: The sensing and detection module consists of a weight sensor, tilt sensor, distance sensor, voltage sensor, and speed sensor. Each sensor is installed in a preset position to achieve full-dimensional parameter acquisition. The weight sensor is installed at the hook of the crane, directly contacting the load-bearing components of the cargo, and collects load data in real time during crane operation; The tilt sensor is installed at the center of the crane's main body to ensure accurate capture of the crane's tilt in both horizontal and vertical directions and to collect the crane's tilt angle data. Distance sensors are evenly distributed around the crane body, covering the front, back, left and right directions of the crane body, and monitor the distance data between the crane and surrounding fixed or moving obstacles in real time. A voltage sensor is connected in series in the main power input circuit of the crane to collect power voltage data of the crane power supply system; The speed sensor is installed on the output shaft of the crane motor and collects motor speed data by sensing the rotation frequency of the shaft.

[0017] Embedded intelligent decision-making and data platform module The middleware module is the core control unit of the system, adopting an integrated hardware architecture of "core board + expansion board" and a software architecture of "driver layer - core algorithm layer - application layer", as detailed below: Hardware architecture: The core board uses a processor with high-speed computing capabilities to ensure efficient data processing and algorithm execution; the expansion board integrates storage submodules, communication submodules, and interface submodules. The storage submodule includes flash memory units and ferroelectric storage units. The flash memory units are used for long-term storage of recent operating parameters and potential hazard assessment results, while the ferroelectric storage units are used to store key system configuration parameters, and the data is not lost after power failure. The communication submodule supports wireless communication, enabling bidirectional data interaction between the middleware module and the external cloud platform. It can both upload locally stored data and receive configuration update commands from the cloud. The interface submodule includes a signal interface adapted to the sensing and detection module, as well as a communication interface for connecting to external display devices, enabling data transmission and information visualization. Software architecture: Driver layer: Provides drivers for various hardware peripherals to ensure that sensor data can be collected at a fixed frequency and that the hardware devices operate stably; The core algorithm layer includes the analytic hierarchy process (AHP), association rule mining algorithm, and Long Short-Term Memory (LSTM) algorithm, which is the core of intelligent protection. Application layer: The processing results of the core algorithm layer are converted into protection commands and sent to the protection execution module. At the same time, the hazard judgment results and prediction reports are displayed through external display devices.

[0018] Execution protection module The protection module consists of an alarm unit, a braking unit, and a power-off unit. Each unit is connected to the corresponding circuit according to the crane control logic. The alarm unit uses audible and visual alarm equipment, which is installed in a position that is easily visible to the crane operator. It can issue alarm signals in different modes according to different protection commands. The braking unit uses electromagnetic braking equipment, which is linked to the hoisting mechanism motor and the traveling mechanism motor of the crane respectively. After receiving the protection command, it can quickly brake the motor to prevent the mechanism from continuing to run. The power-off unit uses a relay assembly connected in series in the crane's main power circuit. In scenarios where an emergency power cut-off is required, it disconnects the power circuit upon receiving an instruction, ensuring equipment safety.

[0019] Power module The power module uses a rechargeable battery as its core power supply, along with a voltage regulator circuit, to stabilize the battery output voltage within the operating voltage range required by each module. This provides a continuous and stable power supply for the sensing and detection module, the middle platform module, and the execution and protection module, ensuring that the system operates without power interruption throughout the crane's operation.

[0020] The specific implementation steps are as follows: Step S1: Parameter Acquisition Step The sensors in the sensing and detection module start collecting data at a fixed frequency: the weight sensor collects the current cargo load data, the tilt sensor collects the fuselage tilt angle data, the distance sensor collects the distance data to surrounding obstacles, the voltage sensor collects the power supply voltage data, and the speed sensor collects the motor speed data. After data collection, each sensor transmits its operating parameters to the driver layer of the central platform module through the interface submodule. The driver layer performs preliminary signal conversion on the parameters to ensure that the data format meets the requirements for subsequent processing. Step S2: Hazard identification and data synchronization processing steps The software architecture of the middle platform module processes data according to the following process: Data preprocessing: The driver layer transmits the converted operating parameters to the core algorithm layer. The core algorithm layer uses the Kalman filter algorithm to filter the parameters, eliminating abnormal fluctuations in data caused by environmental interference during sensor acquisition, and obtaining stable preprocessed parameters. Hazard priority and correlation determination: The core algorithm layer calls the preset scoring model and uses the analytic hierarchy process to compare the preprocessed operating parameters with the preset safety standards. By constructing a judgment matrix and calculating weights, it assigns a corresponding score to each potential safety hazard (such as abnormal load, abnormal tilt, too close distance, and abnormal voltage), and determines the priority of the hazard based on the score. If multiple safety hazards are triggered simultaneously, the algorithm further uses association rule mining to analyze the correlation between the hazards: for example, mining the correlation between "abnormal load" and "abnormal tilt" to determine whether the abnormal load is the cause of the abnormal tilt, analyzing the association rules between "too close distance" and "crane travel direction", and correcting the protection instructions based on the correlation results to ensure that the protection instructions are prioritized for the core hazards. Data storage: While generating protection instructions, the core algorithm layer transmits the pre-processed operating parameters, hazard judgment results, and protection instructions to the storage submodule according to a preset storage strategy: data from recent periods is summarized and stored at the minute level for easy short-term traceability; data from slightly earlier periods is summarized and stored at the hour level to reduce storage space; and data older than a certain period is compressed and uploaded to the external cloud through the communication submodule to achieve long-term data backup. Step S3: Protection Execution Steps The application layer of the middleware module transmits the protection instructions generated by the core algorithm layer to the execution protection module: If the protection command targets a low-priority hazard (such as a slight voltage fluctuation), the alarm unit will activate the corresponding mode of audible and visual alarm to alert the operator without triggering any other actions. If the protection command targets a medium-priority hazard (such as being close to an obstacle or being slightly overloaded), the alarm unit will activate the alarm while the braking unit brakes the hoisting or traveling mechanism to prevent the crane from continuing to move in the dangerous direction. If the protection command targets a high-priority hazard (such as severe tilting or emergency voltage abnormality), the alarm unit and braking unit will activate simultaneously, while the power-off unit will receive the command to disconnect the main power circuit, completely cutting off the crane's power supply and preventing the accident from escalating. Step S4: Predictive Protection Steps This step is a non-real-time auxiliary process, executed only when the crane is not in operation (such as during nighttime shutdown) or during work breaks (such as between two cargo handling operations): Status determination: The application layer of the middle platform module monitors the crane's operation status in real time. When it is determined that the crane has entered a non-operation period or a work gap, a predictive analysis process is triggered. Predictive analytics: The core algorithm layer reads historical operating parameters (such as long-term motor speed changes, braking unit activation counts, and tilt angle fluctuation trends) from the storage submodule and calls the Long Short-Term Memory (LSTM) network algorithm. Based on historical operating data of key components (such as braking units and motors), an LSTM prediction model is constructed to analyze the performance degradation law of the components. Through model training and iteration, the remaining life of the components is predicted. Based on long-term hazard triggering records, the LSTM algorithm is used to learn from the time series data of hazard occurrences, analyze the frequency, time period, and related factors of hazard occurrences, and predict the trend of potential hazards that may appear in the future (such as overload hazards that are easily triggered in specific work scenarios). Report generation and processing: The core algorithm layer generates a prediction report based on the prediction results. The report includes maintenance suggestions for key components and preventive measures for potential hazards. The application layer stores the prediction report in the storage submodule and transmits it to the external cloud or operator terminal device through the communication submodule, providing a basis for subsequent maintenance and operation optimization.

[0021] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0022] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A miniature crane protection system, characterized in that: It includes a perception and detection module, an embedded intelligent decision-making and data platform module, an execution protection module, and a power supply module; The sensing and detection module is used to collect the operating parameters of the mini crane in real time. The operating parameters include load data, crane tilt angle data, distance data between the crane and surrounding obstacles, power supply voltage data, and equipment status data. The embedded intelligent decision-making and data platform module is connected to the perception and detection module and the execution protection module, respectively. It is used to receive the operating parameters collected by the perception and detection module, process the operating parameters to determine the priority and correlation of safety hazards, store the operating parameters and hazard determination results, and perform predictive analysis based on the stored data to generate protection instructions. The execution protection module is used to receive protection instructions from the embedded intelligent decision-making and data platform module and execute corresponding protection actions; The power module is used to provide a stable power supply for the sensing and detection module, the embedded intelligent decision-making and data platform module, and the execution protection module.

2. The miniature crane protection system according to claim 1, characterized in that: The sensing and detection module includes a weight sensor, a tilt sensor, a distance sensor, a voltage sensor, and a speed sensor; The weight sensor is installed at the hook of the crane to collect load data; The tilt sensor is installed on the main body of the crane and is used to collect the tilt angle data of the crane. The distance sensors are installed around the crane body to collect distance data between the crane and surrounding obstacles; The voltage sensor is installed at the power input terminal of the crane and is used to collect power voltage data; The speed sensor is installed on the output shaft of the crane motor and is used to collect motor speed data from the equipment status data.

3. The miniature crane protection system according to claim 1, characterized in that: The embedded intelligent decision-making and data platform module includes a hardware architecture and software functional modules; The hardware architecture adopts an integrated design of core board and expansion board, and the expansion board integrates storage submodule, communication submodule and interface submodule; The storage submodule is used to store operating parameters, hazard assessment results, and key configuration parameters; The communication submodule is used to enable bidirectional data interaction between the embedded intelligent decision-making and data platform module and the external cloud. The interface submodule is used to connect the sensing and detection module and the external display device to realize data transmission and information display. The software functional modules adopt a three-layer architecture of driver layer, core algorithm layer and application layer. The driver layer is used to drive peripheral devices to ensure stable data acquisition and transmission. The core algorithm layer is used to realize the priority and correlation determination of hidden dangers and predictive analysis. The application layer is used to output protection commands and display the hidden danger determination results and prediction reports.

4. The miniature crane protection system according to claim 3, characterized in that: The storage submodule includes flash memory units and ferroelectric memory units; The flash memory unit is used to store operating parameters and potential hazard assessment results for a preset duration; The ferroelectric storage unit is used to store key configuration parameters, and these key configuration parameters are not lost after power failure.

5. The miniature crane protection system and method according to claim 1, characterized in that: The execution protection module includes an alarm unit, a braking unit, and a power-off unit; The alarm unit is used to issue alarm signals of different modes according to the protection instructions; The braking unit is used to brake the hoisting mechanism and traveling mechanism of the crane according to the protection command; The power-off unit is used to cut off the power supply to the crane according to the protection command.

6. A method for protecting a miniature crane, applicable to the method for protecting a miniature crane as described in any one of claims 1-5, characterized in that: The methods and steps include the following: Step 1: Parameter Acquisition Step: The sensing and detection module collects the operating parameters of the mini crane in real time and transmits the operating parameters to the embedded intelligent decision-making and data platform module; Step 2, Hazard Identification and Data Synchronization Processing: The embedded intelligent decision-making and data platform module preprocesses the operating parameters, determines the priority and correlation of safety hazards based on the preprocessed parameters, generates protection instructions, and stores the operating parameters, hazard identification results and protection instructions according to the preset strategy. Step 3: Protection Execution Steps: The protection module receives the protection command and executes the corresponding protection action according to the command; Step 4, Predictive Protection Steps: During non-operational periods or work breaks of the crane, the embedded intelligent decision-making and data platform module performs predictive analysis based on stored operating parameters and hazard assessment results, generates a prediction report, and stores or transmits the prediction report to external devices.

7. A method for protecting a miniature crane according to claim 6, characterized in that: In step two, the embedded intelligent decision-making and data platform module determines the priority of security risks through a preset scoring model. When multiple security risks overlap, it analyzes the correlation between the risks to correct the protection instructions and ensure that the protection instructions match the core risks.

8. A method for protecting a miniature crane according to claim 6, characterized in that: In step two, the preset storage strategy includes: storing the operating parameters and hazard judgment results for the first preset duration in minutes, storing the operating parameters and hazard judgment results for the period from the first preset duration to the second preset duration in hours, and compressing and transmitting the operating parameters and hazard judgment results exceeding the second preset duration to the external cloud.

9. A method for protecting a miniature crane according to claim 6, characterized in that: In step four, the predictive analysis includes predicting the remaining lifespan of the crane's key components and the potential hidden danger trends within a preset future period. The prediction report includes maintenance recommendations for the key components and preventive measures for potential hidden dangers.

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