Safety early warning method and device for main beam of bridge crane

Abstract

The invention provides a safety early warning method and device for a main beam of a bridge crane, and belongs to the field of mechanical engineering. The method comprises the following steps: acquiring index data of a plurality of crane samples of the same type; for each item of index data, determining an entropy value corresponding to the index data according to the dispersion degree of all sample index data; calculating the objective weight of each piece of index data according to the proportion of the entropy of each piece of index data to the entropy of the overall index data; performing weighted summation on the multiple index data of the crane to be evaluated according to the objective weight to obtain an early warning index, and performing graded early warning according to the early warning index; the state indexes comprise a stress intensity index, a rigidity index, a crack state index, a corrosion depth index and a maintenance state index. According to the method, a comprehensive evaluation system covering five dimensions including strength, rigidity, cracks, corrosion and maintenance is constructed through a multi-dimensional index system and a weighted fusion model, so that the one-sidedness of single parameter evaluation is overcome, and safety early warning is more reliable.

Description

Technical Field

[0001] This invention relates to the field of mechanical engineering, and in particular to a method and device for early warning of the safety of the main beam of a bridge crane. Background Technology

[0002] Bridge cranes are lifting equipment that spans across workshops, warehouses, and material yards for material handling. Their ends rest on tall concrete pillars or metal supports, resembling a bridge. The bridge frame of the bridge crane runs longitudinally along rails laid on the crane beams on both sides, making full use of the space beneath the bridge frame for material handling without being obstructed by ground equipment. It is the most widely used and numerous type of lifting machinery.

[0003] Currently, the mainstream technical solution for safety evaluation of main beams of bridge cranes is the parameter monitoring method. This involves real-time monitoring of only a single physical quantity, such as stress, deflection, or vibration, at key sections of the main beam. While this type of system has advantages in the precise measurement of local parameters, it neglects the comprehensive impact of multiple coupled factors on the crane's safety status, leading to serious biases in the evaluation results. Monitoring only a single parameter cannot capture potential structural safety hazards. Therefore, it is insufficient to meet the needs of modern crane equipment's full lifecycle safety management. Summary of the Invention

[0004] To address the problems existing in the prior art, the present invention provides a method and device for early warning of the safety of the main beam of a bridge crane.

[0005] This invention provides a method for early warning of the safety of the main beam of a bridge crane, comprising: acquiring index data of multiple samples of cranes of the same type; for each index data, determining the entropy value corresponding to the index data based on the dispersion of all sample index data; calculating the objective weight of each index data based on the proportion of the entropy value of each index data to the overall entropy value of the index data; weighting and summing multiple index data of the crane to be evaluated according to the objective weight to obtain an early warning index, and performing graded early warning according to the early warning index; wherein, the index data is data of state indicators after being normalized to be positively correlated with safety, and the state indicators include stress intensity indicators, stiffness indicators, crack state indicators, corrosion depth indicators, and maintenance state indicators.

[0006] According to the present invention, a method for early warning of the safety of the main beam of a bridge crane, wherein determining the entropy value corresponding to each index data based on the dispersion of all sample index data includes:

[0007]

[0008]

[0009] in, Let the entropy value be the j-th index. For the j-th indicator data, the proportion of the i-th sample to the whole, where m is the number of samples. This represents the j-th indicator data for the i-th sample.

[0010] According to the present invention, a method for early warning of the safety of the main beam of a bridge crane is provided, wherein calculating the objective weight of each indicator data based on the proportion of the entropy value of each indicator data to the total entropy value of the overall indicator data includes:

[0011]

[0012]

[0013] in, Let be the objective weight of the j-th indicator.

[0014] According to the present invention, a method for early warning of the safety of the main beam of a bridge crane includes a weighted summation of multiple indicator data of the crane to be evaluated based on the objective weights. The weighted summation includes: obtaining a combined weight based on the objective weights and subjective weights; and performing a weighted summation of multiple indicator data of the crane to be evaluated based on the combined weights. The subjective weights are obtained by constructing a comparison matrix based on the quantitative values ​​obtained by experts from comparing the importance of different indicators, calculating the geometric mean of each row of the comparison matrix, and then normalizing it.

[0015] According to the present invention, a method for early warning of the safety of the main beam of a bridge crane, before obtaining the combined weight based on the objective weight and the subjective weight, further includes: calculating the eigenvalues ​​of the comparison matrix, calculating the consistency index based on the eigenvalues ​​and the matrix order; calculating the consistency ratio based on the consistency index, and if the consistency ratio does not meet the condition of a preset threshold, then re-obtaining the expert's evaluation result until the obtained consistency ratio meets the condition of the preset threshold.

[0016] According to the present invention, a method for early warning of the safety of the main beam of a bridge crane, wherein obtaining a combined weight based on the objective weight and the subjective weight includes:

[0017]

[0018] in, For combined weights, Subjective weighting, For objective weighting, The preference coefficient is 0 ≤ ≤1.

[0019] According to the present invention, a method for early warning of the safety of the main beam of a bridge crane is provided, in which the preference coefficient is decreased as the number of crane samples increases from small to large. .

[0020] This invention also provides a safety early warning device for the main beam of a bridge crane, comprising: an input module for acquiring index data from multiple samples of cranes of the same type; an entropy module for determining the entropy value corresponding to each index data based on the dispersion of all sample index data; a weighting module for calculating the objective weight of each index data based on the proportion of the entropy value of each index data to the overall entropy value of the index data; and an early warning module for weighted summation of multiple index data of the crane to be evaluated based on the objective weights to obtain an early warning index, and for graded early warning based on the early warning index; wherein the index data are data of state indicators normalized to be positively correlated with safety, and the state indicators include stress intensity indicators, stiffness indicators, crack state indicators, corrosion depth indicators, and maintenance state indicators.

[0021] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the bridge crane main beam safety early warning method as described above.

[0022] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the bridge crane main beam safety early warning method as described above.

[0023] The bridge crane main beam safety early warning method and device provided by this invention treats the crane main beam as a complex system. Its safety is not a simple summation of various indicators. Through a multi-dimensional indicator system and a weighted fusion model, a comprehensive evaluation system covering five dimensions is constructed, including strength, stiffness, cracks, corrosion, and maintenance. These five indicators comprehensively characterize the safety status of the main beam from five perspectives: instantaneous load-bearing capacity, structural deformation, fatigue damage accumulation, material degradation, and operation and maintenance management, as well as the overall performance of their mutual influence. This overcomes the one-sidedness of single-parameter evaluation and makes the safety early warning more reliable. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1This is a flowchart illustrating the safety early warning method for the main beam of a bridge crane provided by the present invention.

[0026] Figure 2 This is a schematic diagram of the indicator data construction process provided by the present invention;

[0027] Figure 3 This is a schematic diagram of the combined weight construction process provided by the present invention;

[0028] Figure 4 This is a structural schematic diagram of the bridge crane main beam safety early warning device provided by the present invention;

[0029] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0031] The following is combined Figures 1-5 The present invention describes a method and apparatus for early warning of the safety of the main beam of a bridge crane. Figure 1 This is a flowchart illustrating the safety early warning method for the main beam of a bridge crane provided by the present invention, as shown below. Figure 1 As shown, the present invention provides a safety early warning method for the main beam of a bridge crane, comprising:

[0032] 101. Obtain indicator data from multiple samples of the same type of crane.

[0033] The index data are normalized data after the condition indicators have been positively correlated with safety. The condition indicators include stress intensity index, stiffness index, crack condition index, corrosion depth index and maintenance condition index.

[0034] like Figure 2 To eliminate the influence of the dimensions and magnitudes of the indicators, all indicators can be mapped to the [0,1] interval using the following formula, with higher values ​​representing better safety. Alternatively, normalization with an inverse correlation to safety can be performed; correspondingly, the smaller the final calculated warning index, the safer the system, as long as all data remains consistent. This embodiment of the invention uses a positive correlation as an example, and the indicator data calculation is shown in Table 1.

[0035]

[0036] 102. For each indicator data, determine the entropy value corresponding to the indicator data based on the dispersion of all sample indicator data.

[0037] Weights are determined based on the degree of dispersion of the data. The more dispersed the data and the lower the entropy, the greater the weight.

[0038] 103. Calculate the objective weight of each indicator data based on the proportion of the entropy value of each indicator data to the entropy value of the overall indicator data.

[0039] 104. Based on the objective weights, perform a weighted summation of multiple indicator data of the crane to be evaluated to obtain an early warning index, and conduct graded early warnings based on the early warning index.

[0040] The warning index can be set to a continuous value between [0,1]. The higher the value, the better the safety status, and corresponding warnings will be issued. The safety level classification is shown in Table 2.

[0041] Safe (Green) Note (blue) Warning (yellow) Danger (red) S ≥ 0.80 0.65 ≤ S < 0.80 0.50 ≤ S < 0.65 S < 0.50

[0042] Furthermore, this warning index or warning color can be visualized in real time via the cab display screen, the plant monitoring screen, and a mobile app. When the index falls to the "warning" or "danger" level, the system not only issues an alarm but also automatically generates a diagnostic report, clearly indicating which indicators (such as "crack index too low") caused the index drop, and providing targeted maintenance recommendations (such as "recommend non-destructive testing of the weld seam in the middle of the main beam"). This index can serve as a direct, quantitative basis for determining whether the equipment can continue to operate, whether it needs to be used at reduced load, and for scheduling maintenance plans.

[0043] The bridge crane main beam safety early warning method of this invention treats the crane main beam as a complex system. Its safety is not a simple summation of various indicators. Through a multi-dimensional indicator system and a weighted fusion model, a comprehensive evaluation system is constructed, covering five dimensions: strength (Ms), stiffness (Md), cracks (Mc), corrosion (Mr), and maintenance (Mm). These five indicators comprehensively characterize the safety status of the main beam from five perspectives: instantaneous load-bearing capacity, structural deformation, fatigue damage accumulation, material degradation, and operation and maintenance management, as well as the overall performance of their mutual influence. This overcomes the one-sidedness of single-parameter evaluation and makes the safety early warning more reliable.

[0044] In one embodiment, determining the entropy value corresponding to the indicator data based on the dispersion of all sample indicator data includes:

[0045]

[0046]

[0047] in, Let the entropy value be the j-th index. For the j-th indicator data, the proportion of the i-th sample to the whole, where m is the number of samples. This represents the j-th indicator data for the i-th sample.

[0048] dimensionless index value Arranged into a matrix ,in The sample number. This is the index number. The number is calculated using the above formula. Under this indicator, the first The proportion of each sample value Then calculate the first Entropy value of the item index Among them, it is stipulated that when hour, .

[0049] In one embodiment, calculating the objective weight of each indicator data point based on the proportion of the entropy value of each indicator data point to the total entropy value of the overall indicator data includes:

[0050]

[0051]

[0052] in, Let be the objective weight of the j-th indicator.

[0053] First, calculate the difference coefficient. Normalization yields objective weights .

[0054] In one embodiment, the weighted summation of multiple indicator data of the crane to be evaluated based on the objective weights includes: obtaining a combined weight based on the objective weights and subjective weights; and performing a weighted summation of multiple indicator data of the crane to be evaluated based on the combined weights. The subjective weights are obtained by constructing a comparison matrix based on the quantitative values ​​obtained by experts comparing the importance of different indicators, and by calculating the geometric mean of each row of the comparison matrix and then normalizing it.

[0055] Considering the limitations of objective weights, especially their accuracy when the sample size is small, this embodiment of the invention quantifies the expert's experience judgment to calculate subjective weights and calculates combined weights for the objective weights.

[0056] The sample included several experts, and a 1-9 scale was used to compare five indicators pairwise. For example, for an old crane operating in a corrosive environment, an expert might consider cracks slightly more important than repair, assigning it a value of 3. All comparison results were then entered into a comparison matrix. :

[0057]

[0058] in, Indicates the first The first indicator is relative to the first The importance of each indicator.

[0059] Calculate the subjective weight vector The method is as follows:

[0060] For matrix The geometric mean of each row of elements is calculated, and then the weights are obtained by normalization.

[0061]

[0062] Obtain the subjective weight vector .

[0063] Finally, a combined weight is calculated based on subjective and objective weights. Then, using the multiple indicator data of the crane to be evaluated and the aforementioned combined weights, a weighted early warning index is obtained.

[0064] In one embodiment, before obtaining the combined weight based on the objective weight and the subjective weight, the method further includes: calculating the eigenvalues ​​of the comparison matrix; calculating a consistency index based on the eigenvalues ​​and the matrix order; calculating a consistency ratio based on the consistency index; and if the consistency ratio does not meet the conditions of a preset threshold, re-obtaining the expert's evaluation results until the obtained consistency ratio meets the conditions of the preset threshold.

[0065] For example, calculating the largest eigenvalue. .

[0066] Calculate the consistency index , where n is the order of the matrix.

[0067] Query the average random consistency index (For a 5th order matrix, RI = 1.12).

[0068] Calculate the consistency ratio .

[0069] like If the consistency of the judgment matrix is ​​satisfactory, then the consistency of the judgment matrix is ​​considered acceptable; otherwise, experts need to re-evaluate it.

[0070] In some embodiments, obtaining the combined weight based on the objective weight and the subjective weight includes:

[0071]

[0072] in, For combined weights, Subjective weighting, For objective weighting, The preference coefficient is 0 ≤ ≤1, specifically as follows Figure 3 As shown.

[0073] In some embodiments, as the number of crane samples increases from small to large, the preference coefficient decreases accordingly. .

[0074] For example, in the initial stage of system deployment, when the data sample is small, it can be set... This leads to a greater reliance on expert experience. As data accumulates, this reliance can be gradually reduced. The weighting is increased to 0.5, determined by objective data, ultimately resulting in the combined weight. .

[0075] Solution Summary:

[0076] Existing methods:

[0077] (1) The evaluation dimensions are singular and lack comprehensiveness: Existing methods focus on single parameters (such as stress or displacement) and fail to integrate multi-dimensional information such as strength, stiffness, cracks, corrosion, and maintenance into a whole safety status index.

[0078] (2) Poor real-time performance and delayed early warning: Both manual inspection and non-destructive testing are periodic tasks, which cannot capture the dynamic changes in the structural state and sudden risks, making it difficult to achieve early warning.

[0079] (3) Highly subjective and lacking quantitative standards: Manual evaluation relies on personal experience, and the evaluation results are difficult to standardize and reproduce, which is not conducive to horizontal comparison and management decision-making between enterprises.

[0080] (4) Lack of foresight and integration with life prediction: Most systems only focus on the current state and do not integrate the fracture mechanics life prediction model with real-time monitoring data, thus failing to correlate it with the remaining service life.

[0081] (5) Data silos and failure to form closed-loop management: The monitoring, evaluation, early warning and maintenance links are independent of each other and have failed to form a closed-loop process of "monitoring-evaluation-decision-maintenance".

[0082] Method of the present invention:

[0083] 1. The evaluation results are more comprehensive and scientific.

[0084] This invention abandons the single-parameter threshold judgment and constructs a five-dimensional index system covering strength, stiffness, cracks, corrosion, and maintenance. It also employs a combined subjective and objective weighting method to determine the weights. Because the weights combine expert experience and objective data laws, the evaluation results not only conform to consensus but also reflect the true distribution characteristics of the current data, greatly reducing the arbitrariness of purely subjective judgments.

[0085] Therefore, the output "comprehensive safety evaluation index" is more credible and can serve as a reliable basis for decision-making.

[0086] 2. The system has self-adaptability.

[0087] The combination of preference coefficient α and entropy weighting in the weighted model enables the system weights to be adjustable. Entropy weighting can automatically sense the degree of variation of each indicator data in the recent period. If crack propagation accelerates within a certain period (i.e., the variation of crack monitoring data sequence increases), entropy weighting will automatically increase the objective weight of crack indicators, thereby amplifying the impact of this risk factor in the comprehensive index and attracting more attention from the system.

[0088] The preference coefficient α can be adjusted according to the equipment life cycle stage (such as the new equipment stage, fatigue stage, and aging stage) to match the evaluation strategy with the equipment status.

[0089] 3. The safety supervision model has shifted from post-event alarms to pre-event warnings.

[0090] This invention deeply integrates a fatigue life prediction model based on fracture mechanics into the evaluation system. Traditional stress monitoring can only issue an alarm when the stress exceeds the allowable value, which is a reactive measure. However, this system, by tracking crack propagation, can issue an early warning through the continuous decrease of the warning index S, even when the load-bearing capacity of the main beam has not yet significantly decreased, but the remaining safe life is insufficient.

[0091] This provides a valuable window of time for scheduling maintenance, preparing spare parts, and organizing manpower, thus avoiding unplanned downtime caused by sudden accidents.

[0092] 4. Operation and maintenance decisions have shifted from vague experience to precise closed-loop systems.

[0093] All the aforementioned advantages ultimately converge on one point: generating a highly reliable, adaptive, and forward-looking comprehensive early warning index S. Since the early warning index S is calculated by weighting various sub-indicators, the system can reverse-engineer to identify which sub-indicators(s) contribute most to the decrease in the S value, thereby accurately locating the source of the fault. This reduces over-reliance on the experience of frontline personnel, realizing a shift from "people finding problems" to "problems finding people," and from "qualitative analysis" to "quantitative decision-making," forming an efficient "monitoring-assessment-early warning-decision-maintenance" management closed loop.

[0094] The safety warning device for the main beam of a bridge crane provided by the present invention is described below. The safety warning device for the main beam of a bridge crane described below can be referred to in correspondence with the safety warning method for the main beam of a bridge crane described above.

[0095] Figure 4 This is a structural schematic diagram of the bridge crane main beam safety early warning device provided by the present invention, as shown below. Figure 4 As shown, the main beam safety early warning device for bridge cranes includes: an input module 401, an entropy module 402, a weighting module 403, and an early warning module 404. The input module 401 acquires index data from multiple samples of the same type of crane. The entropy module 402 determines the entropy value corresponding to each index data based on the dispersion of all sample index data. The weighting module 403 calculates the objective weight of each index data based on the proportion of its entropy value to the overall entropy value. The early warning module 404 performs a weighted summation of multiple index data of the crane to be evaluated based on the objective weights to obtain an early warning index, and then performs graded early warnings based on the early warning index. The index data consists of state indicators normalized to a positive correlation with safety, including stress intensity, stiffness, crack condition, corrosion depth, and maintenance condition indicators.

[0096] The apparatus embodiments provided in this invention are for implementing the above-described method embodiments. For specific processes and details, please refer to the above-described method embodiments, which will not be repeated here.

[0097] The bridge crane main beam safety early warning device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned bridge crane main beam safety early warning method embodiment. For the sake of brevity, any parts not mentioned in the bridge crane main beam safety early warning device embodiment can be referred to the corresponding content in the aforementioned bridge crane main beam safety early warning method embodiment.

[0098] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 5As shown, the electronic device may include: a processor 501, a communication interface 502, a memory 503, and a communication bus 504. The processor 501, communication interface 502, and memory 503 communicate with each other via the communication bus 504. The processor 501 can call logical instructions in the memory 503 to execute a bridge crane main beam safety early warning method. This method includes: acquiring indicator data from multiple samples of cranes of the same type; for each indicator data, determining the entropy value corresponding to the indicator data based on the dispersion of all sample indicator data; calculating the objective weight of each indicator data based on the proportion of the entropy value of each indicator data to the overall indicator data entropy value; weighting and summing multiple indicator data of the crane to be evaluated according to the objective weight to obtain an early warning index, and performing graded early warning based on the early warning index; wherein the indicator data is data of state indicators normalized to be positively correlated with safety, and the state indicators include stress intensity indicators, stiffness indicators, crack state indicators, corrosion depth indicators, and maintenance state indicators.

[0099] Furthermore, the logical instructions in the aforementioned memory 503 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the bridge crane main beam safety early warning method provided by the above methods. The method includes: acquiring index data of multiple samples of cranes of the same type; for each index data, determining the entropy value corresponding to the index data based on the dispersion of all sample index data; calculating the objective weight of each index data based on the proportion of the entropy value of each index data to the overall index data entropy value; weighting and summing multiple index data of the crane to be evaluated according to the objective weight to obtain an early warning index, and performing graded early warning according to the early warning index; wherein, the index data is data of state indicators after being normalized to be positively correlated with safety, and the state indicators include stress intensity indicators, stiffness indicators, crack state indicators, corrosion depth indicators, and maintenance state indicators.

[0101] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0102] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for early warning of the safety of the main beam of a bridge crane, characterized in that, include: Obtain indicator data from multiple samples of the same type of crane; For each indicator data, the entropy value corresponding to the indicator data is determined based on the degree of dispersion of all sample indicator data. Calculate the objective weight of each indicator data based on the proportion of the entropy value of each indicator data to the entropy value of the overall indicator data. The multiple indicator data of the crane to be evaluated are weighted and summed according to the objective weights to obtain the early warning index, and the early warning is classified according to the early warning index. The index data are normalized data after the condition indicators are positively correlated with safety. The condition indicators include stress intensity index, stiffness index, crack condition index, corrosion depth index and maintenance condition index.

2. The method for early warning of the safety of the main beam of a bridge crane according to claim 1, characterized in that, The step of determining the entropy value corresponding to the indicator data based on the dispersion of all sample indicator data includes: ； ； in, Let the entropy value be the j-th index. For the j-th indicator data, the proportion of the i-th sample to the whole, where m is the number of samples. This represents the j-th indicator data for the i-th sample.

3. The method for early warning of the safety of the main beam of a bridge crane according to claim 2, characterized in that, The step of calculating the objective weight of each indicator data point based on the proportion of the entropy value of each indicator data point to the total entropy value of the overall indicator data includes: ； ； in, Let be the objective weight of the j-th indicator.

4. The method for early warning of the safety of the main beam of a bridge crane according to claim 2, characterized in that, The weighted summation of multiple indicator data of the crane to be evaluated based on the objective weights includes: The combined weights are obtained based on the objective weights and subjective weights; The weighted sum of multiple indicator data of the crane to be evaluated is performed according to the combined weights. The subjective weights are obtained by constructing a comparison matrix based on the quantitative values ​​obtained by experts comparing the importance of different indicators, and by calculating the geometric mean of each row of the comparison matrix and then normalizing it.

5. The method for early warning of the safety of the main beam of a bridge crane according to claim 4, characterized in that, Before obtaining the combined weight based on the objective weight and the subjective weight, the method further includes: Calculate the eigenvalues ​​of the comparison matrix, and then calculate the consistency index based on the eigenvalues ​​and the matrix order. The consistency ratio is calculated based on the consistency index. If the consistency ratio does not meet the preset threshold, the expert evaluation results are obtained again until the consistency ratio meets the preset threshold.

6. The method for early warning of the safety of the main beam of a bridge crane according to claim 4, characterized in that, The process of obtaining the combined weight based on the objective weight and the subjective weight includes: ； in, For combined weights, Subjective weighting, For objective weighting, The preference coefficient is 0 ≤ ≤1.

7. The method for early warning of the safety of the main beam of a bridge crane according to claim 6, characterized in that, As the number of crane samples increases from small to large, the corresponding preference coefficient decreases from large to small. .

8. A safety early warning device for the main beam of a bridge crane, characterized in that, include: The input module is used to obtain indicator data from multiple samples of the same type of cranes; The entropy module is used to determine the entropy value of each indicator data based on the dispersion of all sample indicator data. The weighting module is used to calculate the objective weight of each indicator data based on the proportion of the entropy value of each indicator data to the entropy value of the overall indicator data. The early warning module is used to perform weighted summation of multiple indicator data of the crane to be evaluated according to the objective weights to obtain an early warning index, and to perform graded early warnings based on the early warning index. The index data are normalized data after the condition indicators are positively correlated with safety. The condition indicators include stress intensity index, stiffness index, crack condition index, corrosion depth index and maintenance condition index.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the bridge crane main beam safety early warning method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the bridge crane main beam safety early warning method as described in any one of claims 1 to 7.

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