Vehicle temperature data processing method and system based on multi-alarm strategy

Abstract

The application discloses a kind of based on multi-alarm strategy's car ship temperature data processing method and system, the method includes: the temperature data of multiple positions of target car ship is sent to shore end equipment;Determine the first temperature early warning condition of the target car ship based on the temperature data and multiple alarm rules;Receive the second temperature early warning condition determined based on the temperature data sent by the shore end equipment;According to the matching calculation between the first temperature early warning condition and the second temperature early warning condition, determine the temperature alarm information of the target car ship.It can be seen that the application can effectively make up the limitations of single-point monitoring and significantly improve the completeness and reliability of temperature anomaly identification during ship navigation through the interactive verification of off-site data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for processing temperature data of automobiles and ships based on a multi-alarm strategy. Background Technology

[0002] With the rapid development of global maritime trade in automobiles, temperature monitoring and fire early warning systems for car carriers, especially those carrying new energy vehicles, directly impact the overall safety of the vessel and its cargo during navigation. To identify fire hazards in critical areas such as cargo holds, existing monitoring methods typically rely on temperature sensors deployed at the ship's deck for data collection and local alarm generation. However, current ship temperature monitoring technologies are generally limited to independent monitoring at the ship's deck. The lack of technical support for acquiring temperature data from multiple locations on the target car carrier and combining this data with dual early warning systems from both the ship and shore to determine temperature alarm information prevents the use of cross-regional data verification for closed-loop ship-shore collaborative monitoring. This hinders the ability to effectively compensate for the limitations of single-point monitoring, resulting in insufficient completeness and reliability in identifying abnormal temperatures during navigation and limiting the risk control capabilities of transport vessels in complex ocean environments. Therefore, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for processing temperature data of automobiles and ships based on a multi-alarm strategy, which can effectively make up for the limitations of single-point monitoring through the interactive verification of data from different locations and significantly improve the completeness and reliability of temperature anomaly identification during ship navigation.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a method for processing car and ship temperature data based on a multi-alarm strategy, the method comprising: Temperature data from multiple locations on the target vehicle ship is sent to shore-based equipment. Based on the temperature data and multiple alarm rules, the first temperature warning situation of the target car ship is determined. Receive the second temperature warning status obtained from the temperature data sent by the shore-based equipment; The temperature alarm information of the target vehicle vessel is determined based on the matching calculation between the first temperature warning and the second temperature warning.

[0005] As an optional implementation, in the first aspect of the present invention, determining the first temperature warning situation of the target car-boat based on the temperature data and multiple alarm rules includes: For each preset alarm rule, all the temperature data are input into the analysis model corresponding to the alarm rule to obtain the temperature analysis result corresponding to the alarm rule; Based on all the temperature analysis results, the first temperature warning situation for the target car-boat is determined.

[0006] As an optional implementation, in the first aspect of the present invention, the alarm rule is a threshold rule or a distribution rule; the threshold rule includes at least one of a temperature threshold rule and a temperature rise rate threshold rule; the distribution rule includes at least one of a temperature distribution deviation rule and a temperature change distribution rule.

[0007] As an optional implementation, in the first aspect of the invention, determining the first temperature warning situation of the target car-boat based on all the temperature analysis results includes: From all the temperature analysis results, those indicating hazardous situations were filtered out, resulting in multiple hazard analysis results; For any two of the aforementioned hazard analysis results, calculate the hazard similarity between the two hazard analysis results; Calculate the proportion of records in which the alarm rules corresponding to the two hazard analysis results are jointly applied in the historical alarm records stored in the target vehicle vessel; Calculate the weighted sum of the hazard similarity and the record ratio to obtain the result correlation parameter between the two hazard analysis results; Based on all the hazard analysis results and the associated parameters of the results, the first temperature warning situation for the target carboat is determined.

[0008] As an optional implementation, in the first aspect of the present invention, when both hazard analysis results belong to threshold rules, the hazard similarity is the degree of closeness between the threshold difference results of the two hazard analysis results; when both hazard analysis results belong to distribution rules, the hazard similarity is the graph similarity between the temperature distribution maps of the two hazard analysis results; when the two hazard analysis results belong to threshold rules and distribution rules respectively, the hazard similarity is calculated through the following steps: Extract the threshold difference results and temperature distribution maps corresponding to the two aforementioned hazard analysis results, respectively; Based on the temperature locations where the difference is greater than a preset threshold in the threshold difference results, a corresponding difference distribution map is predicted based on a graph prediction model. The similarity between the difference distribution map and the temperature distribution map is calculated to obtain the danger similarity.

[0009] As an optional implementation, in the first aspect of the invention, determining the first temperature warning situation for the target caravan based on all the hazard analysis results and the associated parameters of the results includes: Each of the aforementioned hazard analysis results is treated as a single graph node, and the result association parameter between any two graph nodes corresponding to the hazard analysis results is determined as the node association feature between the two graph nodes. The graph structure data composed of all the graph nodes and their corresponding node association features is determined as the first temperature warning situation for the target car ship.

[0010] As an optional implementation, in the first aspect of the invention, the shore-based equipment performs the following steps: The temperature data is sent to data processing devices of multiple associated carboats placed in the shore area, so that each of the associated carboats' data processing devices performs the step of determining a temperature warning situation based on the temperature data and multiple alarm rules. Receive the associated temperature warning results after the execution steps sent by all the associated vehicles and ships; For each of the associated temperature warning results, the average similarity between the associated temperature warning result and each other associated temperature warning result is calculated to obtain the support level of the associated temperature warning result; The associated temperature warning result with the highest support is identified as the second temperature warning situation.

[0011] As an optional implementation, in the first aspect of the present invention, determining the temperature alarm information of the target car-boat based on the matching calculation between the first temperature warning situation and the second temperature warning situation includes: Calculate the similarity between the first temperature warning situation and the second temperature warning situation; The first temperature warning is input into a preset hazard prediction model to obtain a first hazard prediction value; The second temperature warning information is input into the hazard prediction model to obtain the second hazard prediction value; The temperature hazard value of the target carboat is obtained by calculating a weighted sum of the first hazard prediction value and the second hazard prediction value; wherein the calculation weights of the first hazard prediction value and the second hazard prediction value are both proportional to the similarity of the situation. When the temperature danger value exceeds the preset danger threshold, the first temperature warning situation is determined as the temperature alarm information of the target vehicle ship and sent to the alarm terminal to trigger an alarm.

[0012] A second aspect of this invention discloses a vehicle / ship temperature data processing system based on a multi-alarm strategy, the system comprising: The transmitting module is used to send temperature data from multiple locations on the target car ship to shore-based equipment. The first determining module is used to determine the first temperature warning situation of the target car ship based on the temperature data and multiple alarm rules. The receiving module is used to receive the second temperature warning status determined based on the temperature data sent by the shore-end equipment; The second determining module is used to determine the temperature alarm information of the target car ship based on the matching calculation between the first temperature warning situation and the second temperature warning situation.

[0013] As an optional implementation, in a second aspect of the invention, the first determining module determines the specific method of the first temperature warning situation of the target car-boat based on the temperature data and multiple alarm rules, including: For each preset alarm rule, all the temperature data are input into the analysis model corresponding to the alarm rule to obtain the temperature analysis result corresponding to the alarm rule; Based on all the temperature analysis results, the first temperature warning situation for the target car-boat is determined.

[0014] As an optional implementation, in a second aspect of the present invention, the alarm rule is a threshold rule or a distribution rule; the threshold rule includes at least one of a temperature threshold rule and a temperature rise rate threshold rule; the distribution rule includes at least one of a temperature distribution deviation rule and a temperature change distribution rule.

[0015] As an optional implementation, in a second aspect of the invention, the first determining module determines the specific method of the first temperature warning situation of the target car-boat based on all the temperature analysis results, including: From all the temperature analysis results, those indicating hazardous situations were filtered out, resulting in multiple hazard analysis results; For any two of the aforementioned hazard analysis results, calculate the hazard similarity between the two hazard analysis results; Calculate the proportion of records in which the alarm rules corresponding to the two hazard analysis results are jointly applied in the historical alarm records stored in the target vehicle vessel; Calculate the weighted sum of the hazard similarity and the record ratio to obtain the result correlation parameter between the two hazard analysis results; Based on all the hazard analysis results and the associated parameters of the results, the first temperature warning situation for the target carboat is determined.

[0016] As an optional implementation, in a second aspect of the invention, when both hazard analysis results belong to threshold rules, the hazard similarity is the degree of closeness between the threshold difference results of the two hazard analysis results; when both hazard analysis results belong to distribution rules, the hazard similarity is the graph similarity between the temperature distribution maps of the two hazard analysis results; when the two hazard analysis results belong to threshold rules and distribution rules respectively, the hazard similarity is calculated through the following steps: Extract the threshold difference results and temperature distribution maps corresponding to the two aforementioned hazard analysis results, respectively; Based on the temperature locations where the difference is greater than a preset threshold in the threshold difference results, a corresponding difference distribution map is predicted based on a graph prediction model. The similarity between the difference distribution map and the temperature distribution map is calculated to obtain the danger similarity.

[0017] As an optional implementation, in a second aspect of the invention, the first determining module determines the specific method of the first temperature warning situation of the target car carrier based on all the said hazard analysis results and the result correlation parameters, including: Each of the aforementioned hazard analysis results is treated as a single graph node, and the result association parameter between any two graph nodes corresponding to the hazard analysis results is determined as the node association feature between the two graph nodes. The graph structure data composed of all the graph nodes and their corresponding node association features is determined as the first temperature warning situation for the target car ship.

[0018] As an optional implementation, in a second aspect of the invention, the shore-based equipment performs the following steps: The temperature data is sent to data processing devices of multiple associated carboats placed in the shore area, so that each of the associated carboats' data processing devices performs the step of determining a temperature warning situation based on the temperature data and multiple alarm rules. Receive the associated temperature warning results after the execution steps sent by all the associated vehicles and ships; For each of the associated temperature warning results, the average similarity between the associated temperature warning result and each other associated temperature warning result is calculated to obtain the support level of the associated temperature warning result; The associated temperature warning result with the highest support is identified as the second temperature warning situation.

[0019] As an optional implementation, in a second aspect of the invention, the second determining module determines the specific method of the temperature alarm information of the target car-boat based on the matching calculation between the first temperature warning situation and the second temperature warning situation, including: Calculate the similarity between the first temperature warning situation and the second temperature warning situation; The first temperature warning is input into a preset hazard prediction model to obtain a first hazard prediction value; The second temperature warning information is input into the hazard prediction model to obtain the second hazard prediction value; The temperature hazard value of the target carboat is obtained by calculating a weighted sum of the first hazard prediction value and the second hazard prediction value; wherein the calculation weights of the first hazard prediction value and the second hazard prediction value are both proportional to the similarity of the situation. When the temperature danger value exceeds the preset danger threshold, the first temperature warning situation is determined as the temperature alarm information of the target vehicle ship and sent to the alarm terminal to trigger an alarm.

[0020] A third aspect of this invention discloses another vehicle / ship temperature data processing system based on a multi-alarm strategy, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the vehicle and ship temperature data processing method based on multi-alarm strategy disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the vehicle and ship temperature data processing method based on a multi-alarm strategy disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention acquires temperature data from multiple locations on the target car ship and performs matching calculations based on dual early warning information from both the ship and shore ends to determine temperature alarm information. This enables closed-loop monitoring through ship-shore collaboration, thereby effectively compensating for the limitations of single-point monitoring through cross-regional data interaction and verification, and significantly improving the completeness and reliability of temperature anomaly identification during ship navigation. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a method for processing temperature data of automobiles and ships based on a multi-alarm strategy, as disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the structure of a car and ship temperature data processing system based on a multi-alarm strategy disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another automobile and ship temperature data processing system based on a multi-alarm strategy disclosed in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] This invention discloses a method and system for processing temperature data of car-and-ship vessels based on a multi-alarm strategy. By acquiring temperature data from multiple locations on the target car-and-ship vessel and combining this data with dual early warning information from both the ship and shore ends for matching calculations, temperature alarm information can be determined. This enables closed-loop monitoring through ship-shore collaboration, effectively compensating for the limitations of single-point monitoring through cross-regional data interaction and verification, and significantly improving the completeness and reliability of temperature anomaly identification during ship navigation. Detailed descriptions follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for processing temperature data of automobiles and ships based on a multi-alarm strategy, as disclosed in an embodiment of the present invention. Figure 1 The described multi-alarm strategy-based method for processing temperature data from automobiles and ships can be applied to data processing systems / data processing equipment / data processing servers (including local processing servers or cloud processing servers). For example... Figure 1 As shown, the car and ship temperature data processing method based on a multi-alarm strategy may include the following operations: 101. Send temperature data from multiple locations of the target vehicle ship to shore-based equipment.

[0032] Optionally, the target car carrier can be a roll-on / roll-off ship, a car carrier, or a multi-purpose special-purpose carrier; the present invention does not limit this.

[0033] Optionally, these multiple locations can be distributed on the deck surface, bulkhead, ventilation duct opening, and electrical equipment surface of the vehicle compartment; the present invention does not limit this.

[0034] 102. Based on temperature data and multiple alarm rules, determine the first temperature warning situation for the target car ship.

[0035] Optionally, the alarm rule can be a threshold rule or a distribution rule.

[0036] Optionally, the threshold rule includes at least one of a temperature threshold rule and a temperature rise rate threshold rule.

[0037] Optionally, the distribution rules include at least one of temperature distribution deviation rules and temperature change distribution rules.

[0038] 103. Receive the second temperature warning status obtained from temperature data determined by the shore-based equipment.

[0039] 104. Based on the matching calculation between the first temperature warning and the second temperature warning, determine the temperature alarm information of the target car ship.

[0040] As can be seen, the above-mentioned embodiments of the invention obtain temperature data from multiple locations on the target car ship and perform matching calculations based on dual early warning situations at the ship and shore ends to determine temperature alarm information. This enables closed-loop monitoring of ship-shore collaboration, thereby effectively compensating for the limitations of single-point monitoring through interactive verification of data from different locations and significantly improving the completeness and reliability of temperature anomaly identification during ship navigation.

[0041] As an optional embodiment, the step described above, determining the first temperature warning situation for the target vehicle vessel based on temperature data and multiple alarm rules, includes: For each preset alarm rule, all temperature data are input into the analysis model corresponding to that alarm rule to obtain the temperature analysis result corresponding to that alarm rule. Based on all temperature analysis results, the first temperature warning situation for the target car ship is determined.

[0042] Optionally, the analysis model can be a classification model based on logistic regression, a decision model based on support vector machines, or a feature extraction model based on deep learning; this invention does not limit the model.

[0043] As can be seen, by inputting temperature data into multivariate analysis models corresponding to different rules to determine the first temperature warning situation through the above optional embodiments, it is possible to comprehensively examine multiple warning characteristics of temperature, thereby capturing subtle thermodynamic anomaly signals from multiple dimensions and greatly enhancing the system's perception depth and coverage of different types of fire hazards.

[0044] As an optional embodiment, the step above, determining the first temperature warning situation for the target car-boat based on all temperature analysis results, includes: The results of all temperature analysis were filtered to identify those with hazardous conditions, resulting in multiple hazard analysis results; For any two hazard analysis results, calculate the hazard similarity between the two hazard analysis results; Calculate the proportion of records in which the alarm rules corresponding to the two hazard analysis results are jointly applied in the historical alarm records stored by the target vehicle vessel; Calculate the weighted sum of hazard similarity and record ratio to obtain the correlation parameter between the two hazard analysis results; Based on all hazard analysis results and associated parameters, the first temperature warning situation for the target car carrier is determined.

[0045] Optionally, the record ratio can be quantified using the Jaccard similarity coefficient or mutual information algorithm to identify coupling characteristics between alarm rules.

[0046] As can be seen, through the above optional embodiments, by screening the hazard analysis results and combining the correlation parameters calculated by the hazard similarity and the proportion of historical application records, it is possible to achieve deep coupling between real-time early warning characteristics and historical alarm experience. This enables the scientific quantification of the current hazard level using data correlation and significantly improves the rigor of the judgment logic for the first temperature warning in complex environments.

[0047] As an optional embodiment, in the above steps, when both hazard analysis results belong to the threshold rule, the hazard similarity is the degree of closeness between the threshold difference results of the two hazard analysis results.

[0048] Optionally, the degree of closeness can be the reciprocal of the difference between the threshold difference results.

[0049] When both hazard analysis results belong to the distribution rule, the hazard similarity is the graph similarity between the temperature distribution maps of the two hazard analysis results.

[0050] Optionally, the graph similarity can be calculated using graph edit distance (GED) or graph isomorphism algorithms.

[0051] When the two hazard analysis results belong to the threshold rule and the distribution rule, respectively, the hazard similarity is calculated through the following steps: Extract the threshold difference results and temperature distribution maps corresponding to the two hazard analysis results, respectively; Based on the temperature locations where the difference exceeds a preset threshold in the threshold difference results, a corresponding difference distribution map is predicted using a graph prediction model. Calculate the graph similarity between the difference distribution map and the temperature distribution map to obtain the hazard similarity.

[0052] Optionally, the graph prediction model can be a generative adversarial network (GAN) or a variational autoencoder (VAE), which can be used to transform scalar difference features into spatial field distribution features. This invention does not limit the model.

[0053] As can be seen, through the above optional embodiments, by adopting differentiated similarity calculation strategies for threshold rules and distribution rules and introducing graph prediction models to process the graph similarity of heterogeneous data, it is possible to achieve accurate measurement and unified feature mapping of alarm features of different dimensions, thereby eliminating the comparison obstacles caused by inconsistent data types and effectively improving the refinement of hazard association analysis.

[0054] As an optional embodiment, the step above, determining the first temperature warning situation for the target car carrier based on all hazard analysis results and result correlation parameters, includes: Each hazard analysis result is treated as a single graph node, and the result association parameters between any two graph nodes corresponding to the hazard analysis results are determined as the node association features between the two graph nodes. The graph structure data composed of all graph nodes and their corresponding node association features is determined as the first temperature warning situation for the target car ship.

[0055] Optionally, the graph structure data may include an adjacency matrix, a Laplacian matrix, and node feature vectors, which can be used to characterize multidimensional early warning topologies under complex working conditions. This invention does not impose any limitations on this.

[0056] As can be seen, by constructing the hazard analysis results and their associated parameters into graph structure data containing nodes and associated features to characterize the first temperature warning situation through the above optional embodiments, the topological expression of the warning information can be realized. This allows for a more intuitive revelation of the potential chain reaction logic between different sampling warning results and provides high-quality structured input with spatial correlation characteristics for subsequent risk prediction models.

[0057] As an optional embodiment, in the above steps, the shore-end equipment performs the following steps: Temperature data is sent to data processing devices on multiple associated carboats located in the shore area, so that each associated carboat's data processing device can perform the steps of determining temperature warning status based on temperature data and multiple alarm rules; Receive associated temperature warning results after the execution steps from all associated vehicles and ships; For each associated temperature warning result, the average similarity between the associated temperature warning result and each other associated temperature warning result is calculated to obtain the support level of the associated temperature warning result. The temperature warning result with the highest support level is identified as the second temperature warning situation.

[0058] Optionally, the associated car vessel may be another vessel in the historical voyage record that overlaps with the target car vessel on the route path, carries the same type of vehicle, or has similar navigation environment parameters; this invention does not limit this.

[0059] Optionally, the data processing device can be an edge computing gateway, a shipborne server, or an industrial control computer; the present invention does not limit this.

[0060] Optionally, the specific calculation steps for the associated temperature warning results can refer to the calculation steps for the first temperature warning result mentioned above. It can also be graph structure data calculated based on graph structure. Therefore, the similarity calculation between any two associated temperature warning results can also be performed based on graph similarity algorithm. This invention does not limit this.

[0061] As can be seen, through the above optional embodiments, by calling the data processing resources of multiple related ships for parallel computing by shore-based equipment and selecting the warning result with the highest support as the second temperature warning situation, it is possible to achieve distributed verification based on swarm intelligence. This can effectively filter the random noise generated by a single algorithm by utilizing the redundant verification of multiple samples and greatly enhance the objectivity of the warning decision.

[0062] As an optional embodiment, the step above, determining the temperature alarm information of the target car-boat based on the matching calculation between the first temperature warning situation and the second temperature warning situation, includes: Calculate the similarity between the first and second temperature warning scenarios; The first temperature warning is input into the preset hazard prediction model to obtain the first hazard prediction value; The second temperature warning information is input into the hazard prediction model to obtain the second hazard prediction value; The weighted sum of the first hazard prediction value and the second hazard prediction value is calculated to obtain the temperature hazard value of the target car carrier; wherein the calculation weights of the first hazard prediction value and the second hazard prediction value are both proportional to the similarity of the situation; When the temperature danger value exceeds the preset danger threshold, the first temperature warning situation is identified as the temperature alarm information of the target vehicle vessel and sent to the alarm terminal to trigger an alarm.

[0063] Alternatively, the similarity in this case can be calculated using graph edit distance (GED) or graph isomorphism algorithms.

[0064] Optionally, the alarm terminal may include a bridge display terminal, a crew handheld communication terminal, a shore-based monitoring and command center, and an automatic fire extinguishing control system; however, this invention does not impose any limitations on these components.

[0065] As can be seen, through the above optional embodiments, by calculating the similarity between ship and shore warnings and dynamically allocating weights accordingly to calculate the temperature hazard value, and then sending alarm information to the terminal when the value reaches the standard, it is possible to achieve intelligent adjudication of multi-source warning results, thereby ensuring that the alarm mechanism has both the ability to respond quickly to sudden risks and the ability to effectively reduce the probability of false alarms and missed alarms through similarity weighting.

[0066] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a car / ship temperature data processing system based on a multi-alarm strategy, as disclosed in an embodiment of the present invention. Figure 2 The described multi-alarm strategy-based temperature data processing system for automobiles and ships can be applied to data processing systems / data processing equipment / data processing servers (including local processing servers or cloud processing servers). For example... Figure 2 As shown, the vehicle and ship temperature data processing system based on a multi-alarm strategy may include: The transmitting module 201 is used to transmit temperature data from multiple locations of the target car ship to shore-based equipment.

[0067] The first determination module 202 is used to determine the first temperature warning situation of the target car ship based on temperature data and multiple alarm rules.

[0068] The receiving module 203 is used to receive the second temperature warning status determined based on temperature data sent by the shore-end equipment.

[0069] The second determining module 204 is used to determine the temperature alarm information of the target car ship based on the matching calculation between the first temperature warning situation and the second temperature warning situation.

[0070] As can be seen, the above-mentioned embodiments of the invention obtain temperature data from multiple locations on the target car ship and perform matching calculations based on dual early warning situations at the ship and shore ends to determine temperature alarm information. This enables closed-loop monitoring of ship-shore collaboration, thereby effectively compensating for the limitations of single-point monitoring through interactive verification of data from different locations and significantly improving the completeness and reliability of temperature anomaly identification during ship navigation.

[0071] As an optional embodiment, the first determining module determines the specific method of the first temperature warning situation for the target car-boat based on temperature data and multiple alarm rules, including: For each preset alarm rule, all temperature data are input into the analysis model corresponding to that alarm rule to obtain the temperature analysis result corresponding to that alarm rule. Based on all temperature analysis results, the first temperature warning situation for the target car ship is determined.

[0072] As can be seen, by inputting temperature data into multivariate analysis models corresponding to different rules to determine the first temperature warning situation through the above optional embodiments, it is possible to comprehensively examine multiple warning characteristics of temperature, thereby capturing subtle thermodynamic anomaly signals from multiple dimensions and greatly enhancing the system's perception depth and coverage of different types of fire hazards.

[0073] As an optional embodiment, the alarm rule is a threshold rule or a distribution rule; the threshold rule includes at least one of a temperature threshold rule and a temperature rise rate threshold rule; the distribution rule includes at least one of a temperature distribution deviation rule and a temperature change distribution rule.

[0074] As can be seen, the above optional embodiments limit the rule types of alarm rules to achieve comprehensive temperature alarm monitoring and help improve the completeness and reliability of temperature anomaly identification during ship navigation.

[0075] As an optional embodiment, the first determining module determines the specific method of the first temperature warning situation for the target car-boat based on all temperature analysis results, including: The results of all temperature analysis were filtered to identify those with hazardous conditions, resulting in multiple hazard analysis results; For any two hazard analysis results, calculate the hazard similarity between the two hazard analysis results; Calculate the proportion of records in which the alarm rules corresponding to the two hazard analysis results are jointly applied in the historical alarm records stored by the target vehicle vessel; Calculate the weighted sum of hazard similarity and record ratio to obtain the correlation parameter between the two hazard analysis results; Based on all hazard analysis results and associated parameters, the first temperature warning situation for the target car carrier is determined.

[0076] As can be seen, through the above optional embodiments, by screening the hazard analysis results and combining the correlation parameters calculated by the hazard similarity and the proportion of historical application records, it is possible to achieve deep coupling between real-time early warning characteristics and historical alarm experience. This enables the scientific quantification of the current hazard level using data correlation and significantly improves the rigor of the judgment logic for the first temperature warning in complex environments.

[0077] As an optional embodiment, when both hazard analysis results belong to the threshold rule, the hazard similarity is the degree of closeness between the threshold difference results of the two hazard analysis results; when both hazard analysis results belong to the distribution rule, the hazard similarity is the graph similarity between the temperature distribution maps of the two hazard analysis results; when the two hazard analysis results belong to the threshold rule and the distribution rule respectively, the hazard similarity is calculated through the following steps: Extract the threshold difference results and temperature distribution maps corresponding to the two hazard analysis results, respectively; Based on the temperature locations where the difference exceeds a preset threshold in the threshold difference results, a corresponding difference distribution map is predicted using a graph prediction model. Calculate the graph similarity between the difference distribution map and the temperature distribution map to obtain the hazard similarity.

[0078] As can be seen, through the above optional embodiments, by adopting differentiated similarity calculation strategies for threshold rules and distribution rules and introducing graph prediction models to process the graph similarity of heterogeneous data, it is possible to achieve accurate measurement and unified feature mapping of alarm features of different dimensions, thereby eliminating the comparison obstacles caused by inconsistent data types and effectively improving the refinement of hazard association analysis.

[0079] As an optional embodiment, the first determining module determines the specific method for the first temperature warning situation of the target car carrier based on all hazard analysis results and result correlation parameters, including: Each hazard analysis result is treated as a single graph node, and the result association parameters between any two graph nodes corresponding to the hazard analysis results are determined as the node association features between the two graph nodes. The graph structure data composed of all graph nodes and their corresponding node association features is determined as the first temperature warning situation for the target car ship.

[0080] As can be seen, by constructing the hazard analysis results and their associated parameters into graph structure data containing nodes and associated features to characterize the first temperature warning situation through the above optional embodiments, the topological expression of the warning information can be realized. This allows for a more intuitive revelation of the potential chain reaction logic between different sampling warning results and provides high-quality structured input with spatial correlation characteristics for subsequent risk prediction models.

[0081] As an optional embodiment, the shore-based equipment performs the following steps: Temperature data is sent to data processing devices on multiple associated carboats located in the shore area, so that each associated carboat's data processing device can perform the steps of determining temperature warning status based on temperature data and multiple alarm rules; Receive associated temperature warning results after the execution steps from all associated vehicles and ships; For each associated temperature warning result, the average similarity between the associated temperature warning result and each other associated temperature warning result is calculated to obtain the support level of the associated temperature warning result. The temperature warning result with the highest support level is identified as the second temperature warning situation.

[0082] As can be seen, through the above optional embodiments, by calling the data processing resources of multiple related ships for parallel computing by shore-based equipment and selecting the warning result with the highest support as the second temperature warning situation, it is possible to achieve distributed verification based on swarm intelligence. This can effectively filter the random noise generated by a single algorithm by utilizing the redundant verification of multiple samples and greatly enhance the objectivity of the warning decision.

[0083] As an optional embodiment, the second determining module determines the specific method of the temperature alarm information of the target car-boat based on the matching calculation between the first temperature warning situation and the second temperature warning situation, including: Calculate the similarity between the first and second temperature warning scenarios; The first temperature warning is input into the preset hazard prediction model to obtain the first hazard prediction value; The second temperature warning information is input into the hazard prediction model to obtain the second hazard prediction value; The weighted sum of the first hazard prediction value and the second hazard prediction value is calculated to obtain the temperature hazard value of the target carboat; optionally, the calculation weights of the first hazard prediction value and the second hazard prediction value are both proportional to the similarity of the situation; When the temperature danger value exceeds the preset danger threshold, the first temperature warning situation is identified as the temperature alarm information of the target vehicle vessel and sent to the alarm terminal to trigger an alarm.

[0084] As can be seen, through the above optional embodiments, by calculating the similarity between ship and shore warnings and dynamically allocating weights accordingly to calculate the temperature hazard value, and then sending alarm information to the terminal when the value reaches the standard, it is possible to achieve intelligent adjudication of multi-source warning results, thereby ensuring that the alarm mechanism has both the ability to respond quickly to sudden risks and the ability to effectively reduce the probability of false alarms and missed alarms through similarity weighting.

[0085] Example 3 Please see Figure 3 , Figure 3 This is another car and ship temperature data processing system based on a multi-alarm strategy disclosed in the embodiments of the present invention. Figure 3 The described multi-alarm strategy-based temperature data processing system for automobiles and ships is applied in a data processing system / data processing equipment / data processing server (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the vehicle and ship temperature data processing system based on a multi-alarm strategy may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the car and ship temperature data processing method based on the multi-alarm strategy described in Embodiment 1.

[0086] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the car and ship temperature data processing method based on a multi-alarm strategy described in Embodiment 1.

[0087] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the car and ship temperature data processing method based on a multi-alarm strategy described in Embodiment 1.

[0088] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0089] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0090] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0091] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0093] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0095] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0096] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0097] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0098] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0099] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0100] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0101] Finally, it should be noted that the car and ship temperature data processing method and system based on multi-alarm strategy disclosed in the embodiments of the present invention are only preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. 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. Such 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 processing temperature data of automobiles and ships based on a multi-alarm strategy, characterized in that, The method includes: Temperature data from multiple locations on the target vehicle ship is sent to shore-based equipment. Based on the temperature data and multiple alarm rules, the first temperature warning situation of the target car ship is determined. Receive the second temperature warning status obtained from the temperature data sent by the shore-based equipment; The temperature alarm information of the target vehicle vessel is determined based on the matching calculation between the first temperature warning and the second temperature warning.

2. The method for processing car and ship temperature data based on a multi-alarm strategy according to claim 1, characterized in that, The determination of the first temperature warning status for the target car-boat based on the temperature data and multiple alarm rules includes: For each preset alarm rule, all the temperature data are input into the analysis model corresponding to the alarm rule to obtain the temperature analysis result corresponding to the alarm rule; Based on all the temperature analysis results, the first temperature warning situation for the target car-boat is determined.

3. The method for processing car and ship temperature data based on a multi-alarm strategy according to claim 2, characterized in that, The alarm rule is a threshold rule or a distribution rule; the threshold rule includes at least one of a temperature threshold rule and a temperature rise rate threshold rule; the distribution rule includes at least one of a temperature distribution deviation rule and a temperature change distribution rule.

4. The method for processing car and ship temperature data based on a multi-alarm strategy according to claim 2, characterized in that, The step of determining the first temperature warning situation for the target car-boat based on all the temperature analysis results includes: From all the temperature analysis results, those indicating hazardous situations were filtered out, resulting in multiple hazard analysis results; For any two of the aforementioned hazard analysis results, calculate the hazard similarity between the two hazard analysis results; Calculate the proportion of records in which the alarm rules corresponding to the two hazard analysis results are jointly applied in the historical alarm records stored in the target vehicle vessel; Calculate the weighted sum of the hazard similarity and the record ratio to obtain the result correlation parameter between the two hazard analysis results; Based on all the hazard analysis results and the associated parameters of the results, the first temperature warning situation for the target carboat is determined.

5. The method for processing car and ship temperature data based on a multi-alarm strategy according to claim 4, characterized in that, When both of the aforementioned hazard analysis results belong to the threshold rule, the hazard similarity is the degree of closeness between the threshold difference results of the two aforementioned hazard analysis results; When both hazard analysis results belong to the distribution rule, the hazard similarity is the graph similarity between the temperature distribution maps of the two hazard analysis results; when the two hazard analysis results belong to the threshold rule and the distribution rule respectively, the hazard similarity is calculated through the following steps: Extract the threshold difference results and temperature distribution maps corresponding to the two aforementioned hazard analysis results, respectively; Based on the temperature locations where the difference is greater than a preset threshold in the threshold difference results, a corresponding difference distribution map is predicted based on a graph prediction model. The similarity between the difference distribution map and the temperature distribution map is calculated to obtain the danger similarity.

6. The method for processing car and ship temperature data based on a multi-alarm strategy according to claim 4, characterized in that, The determination of the first temperature warning status for the target caravan based on all the hazard analysis results and the associated parameters of the results includes: Each of the aforementioned hazard analysis results is treated as a single graph node, and the result association parameter between any two graph nodes corresponding to the hazard analysis results is determined as the node association feature between the two graph nodes. The graph structure data composed of all the graph nodes and their corresponding node association features is determined as the first temperature warning situation for the target car ship.

7. The method for processing car and ship temperature data based on a multi-alarm strategy according to claim 1, characterized in that, The shore-based equipment performs the following steps: The temperature data is sent to data processing devices of multiple associated carboats placed in the shore area, so that each of the associated carboats' data processing devices performs the step of determining a temperature warning situation based on the temperature data and multiple alarm rules. Receive the associated temperature warning results after the execution steps sent by all the associated vehicles and ships; For each of the associated temperature warning results, the average similarity between the associated temperature warning result and each other associated temperature warning result is calculated to obtain the support level of the associated temperature warning result; The associated temperature warning result with the highest support is identified as the second temperature warning situation.

8. The method for processing car and ship temperature data based on a multi-alarm strategy according to claim 1, characterized in that, The step of determining the temperature alarm information of the target car-boat based on the matching calculation between the first temperature warning situation and the second temperature warning situation includes: Calculate the similarity between the first temperature warning situation and the second temperature warning situation; The first temperature warning is input into a preset hazard prediction model to obtain a first hazard prediction value; The second temperature warning information is input into the hazard prediction model to obtain the second hazard prediction value; The weighted sum of the first hazard prediction value and the second hazard prediction value is calculated to obtain the temperature hazard value of the target carboat; wherein the calculation weights of the first hazard prediction value and the second hazard prediction value are both proportional to the similarity of the situation; When the temperature danger value exceeds the preset danger threshold, the first temperature warning situation is determined as the temperature alarm information of the target vehicle ship and sent to the alarm terminal to trigger an alarm.

9. A vehicle and ship temperature data processing system based on a multi-alarm strategy, characterized in that, The system includes: The transmitting module is used to send temperature data from multiple locations on the target car ship to shore-based equipment. The first determining module is used to determine the first temperature warning situation of the target car ship based on the temperature data and multiple alarm rules. The receiving module is used to receive the second temperature warning status determined based on the temperature data sent by the shore-end equipment; The second determining module is used to determine the temperature alarm information of the target car ship based on the matching calculation between the first temperature warning situation and the second temperature warning situation.

10. A vehicle and ship temperature data processing system based on a multi-alarm strategy, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the vehicle and ship temperature data processing method based on a multi-alarm strategy as described in any one of claims 1-8.

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