An intelligent management system and method based on truck overweight records

Abstract

The present invention relates to the field of highway management technology, and in particular to an intelligent management system and method based on truck overweight records, comprising: a collection layer, a recording layer and a decision layer; identification information and load information of the truck are collected through the collection layer, the collection layer further determines whether the truck is overweight based on the load information, the recording layer synchronously receives the determination result of whether the truck is overweight in the collection layer, records the determination result, and uses the recorded determination result of whether the truck is overweight to evaluate the overweight tendency of the truck. The present invention collects the identification information and load information of the truck, determines the overweight of the truck, and evaluates the overweight tendency of the truck, thereby screening the trucks based on the evaluation results to form an overweight determination target set, and finally uses the overweight determination target set to implement a spot check on the effectiveness of the overweight problem of trucks.

Description

Technical Field

[0001] The present invention relates to the technical field of highway management, and in particular to an intelligent management system and method based on overweight records of trucks. Background Art

[0002] Highway management refers to highway construction management, highway maintenance management, highway administration management, and highway traffic violation management. Highway maintenance management involves truck load management.

[0003] In order to save the cost of transporting goods, some truck drivers often use overloading to achieve this goal. Although this can save the cost of transporting goods to a certain extent, it will undoubtedly cause a high load burden on the road surface where the trucks travel, resulting in a reduction in the service life of the road surface, an increase in the braking distance, and an increase in the carbon emissions of the trucks, which seriously affects the safety of cargo transportation;

[0004] At present, the means of controlling overloaded trucks is to build a weighing scale at the starting section of the highway and impose corresponding penalties. However, this method is inefficient and is not suitable for use when a large number of trucks need to pass. Summary of the invention

[0005] In view of the above-mentioned shortcomings of the prior art, the present invention provides an intelligent management system and method based on truck overweight records, which solves the technical problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] In the first aspect, an intelligent management system based on overweight records of trucks includes: a collection layer, a recording layer and a decision layer;

[0008] The identification information and load information of the truck are collected through the collection layer, and the collection layer further determines whether the truck is overweight based on the load information. The recording layer synchronously receives the determination result of whether the truck is overweight in the collection layer, records the determination result, and uses the recorded determination result of whether the truck is overweight to evaluate the overweight tendency of the truck. The decision layer synchronously receives the overweight tendency of the truck evaluated in the recording layer and refreshes the system operation. In the system refresh operation stage, based on the overweight tendency of the truck, the decision system refreshes the operation state and determines the next round of truck overweight monitoring targets;

[0009] The recording layer includes a receiving module, a management module and an evaluation module. The receiving module is used to receive the determination result of whether the truck is overweight in the collection layer and store the received determination result. The management module is used to traverse the determination results stored in the receiving module and distinguish and manage the determination results. The evaluation module is used to obtain the determination results stored in the receiving module and evaluate the overweight tendency of the truck based on the determination results.

[0010] The evaluation logic of truck overweight tendency in the evaluation module is expressed as follows:

[0011]

[0012] Where: ε is the overweight tendency performance value of the truck; n is the set of judgment results of whether the truck is overweight; t i is the truck posture calibration time during the truck weighing phase before the output of the judgment result of whether the i-th truck is overweight; f i is the truck weighing result before outputting the judgment result of whether the truck is overweight for the i-th time; f rt is the truck overweight judgment value; g is the number of times that the truck weighing result in n is not overweight compared with the truck overweight judgment value; d(p i ,p i+1 ) is the distance between the weighing module that is the source of the judgment result of whether the i-th truck is overweight and the weighing module that is the source of the judgment result of whether the i+1-th truck is overweight; γ is the ratio of the number of times the weighing results of the n trucks are not overweight compared with the overweight judgment value of the trucks to the total number of weighing results in n;

[0013] Among them, the truck posture calibration time in the truck weighing stage, that is, the running time of the weighing module, the larger the truck overweight tendency performance value ε is, the higher the probability of the truck being overweight, and vice versa.

[0014] Furthermore, the acquisition layer includes a camera module, a weighing module and a determination module, the camera module is used to collect image data of the truck, extract the truck license plate information in the truck image data, the weighing module is used to sense the weight of the truck, the determination module is used to set an overweight determination value, receive the truck weight sensed in the weighing module, and determine whether the truck is overweight based on the comparison between the overweight determination value and the received truck weight;

[0015] Among them, the camera module is deployed on one side of the weighing module, and the collected truck image data includes the truck license plate area image. After collecting the truck image data, the camera module segments the truck image data based on the license plate color, extracts the license plate area image in the truck image data, and further identifies text information in the license plate area image, which is recorded as the truck license plate information. The truck license plate information is the identification information of the truck.

[0016] Furthermore, the weighing module is integrated with a floor scale, a loudspeaker and a distance measuring sensor. When the weighing module senses the weight of the truck, the truck enters the surface of the floor scale, and the weight of the truck is sensed by the floor scale. The distance measuring sensors are provided in several groups, and the several groups of distance measuring sensors are equidistantly deployed at the edge contour positions of the floor scale. The number of deployed distance measuring sensors is proportional to the area of ​​the floor scale. The judgment module receives the weight of the truck sensed by the weighing module and the distance measuring result of the distance measuring sensor in real time. When the sensed weight of the truck exceeds the set overweight judgment value, the truck is judged to be overweight. The distance measuring results of the distance measuring sensor are further traversed in real time to identify whether there is a distance measuring result with a distance measuring value of zero in the distance measuring results of each distance measuring sensor. When the recognition result is no, the judgment module ends its operation. When the recognition result is yes, the loudspeaker is triggered to operate.

[0017] Among them, when the speaker is triggered to run, a text audio is played to prompt the truck driver to control the movement of the truck, so that the position of the truck on the surface of the scale is offset. During the movement of the truck, the ranging sensor runs in real time. When it is detected that there is no ranging result with a ranging value of zero in each ranging result, the speaker is triggered to run again, and a text message is played to prompt the truck driver to park the truck. The scale runs again, and the sensed weight of the truck is fed back to the judgment module again.

[0018] Furthermore, when the management module traverses the judgment results stored in the receiving module, it simultaneously obtains the truck identification information corresponding to each judgment result, and further marks each judgment result based on the truck identification information. The management module performs a differentiated management stage for the judgment results, and differentiates the judgment results in the receiving module based on the truck identification information marked with the judgment results, so that the judgment results are differentiated and stored in the receiving module.

[0019] Furthermore, the evaluation module runs the synchronous statistics receiving module to determine whether the judgment results stored in each differentiated storage interval are greater than ten groups, and the differentiated storage intervals greater than ten groups correspond to trucks, which are used as the decision-making layer to run the processing target set.

[0020] Furthermore, the weighing modules are provided in several groups, and the weighing modules in several groups perform data exchange through the optical fiber network for operation and processing by the determination module of the collection layer in the system;

[0021] Said When the value of is less than 0, the corresponding truck is not used as the truck overweight tendency assessment target and is directly placed in the decision-making layer operation processing target set.

[0022] Furthermore, the decision layer includes a screening module, a feedback module and a refresh module, the screening module is used to identify that the truck corresponding to the processing target set and the truck corresponding to the judgment result stored in the receiving module is a truck in the non-processing target set, and the truck is screened from the two sets as a judgment target for whether it is overweight, the feedback module is used to receive the judgment target screened in the screening module, and feed back the judgment target to the camera module, and the refresh module is used to refresh the system operation;

[0023] Among them, the feedback module feeds back the judgment target to the camera module, and when the refresh module refreshes the system operation, the camera module runs again to collect truck image data. When the extracted truck license plate information is consistent with the truck identification information of any group of trucks corresponding to the judgment target, the system runs further to determine whether the truck is overweight. Otherwise, the refresh camera module runs to collect new truck image data. After all judgment targets are refreshed and run to determine whether they are overweight, the system decision layer runs again.

[0024] Furthermore, the screening module screens the target determination stage, processes the trucks directly placed in the target set, skips the screening stage, and directly serves as one of the determination targets fed back by the feedback module. The screening logic of the screening module is expressed as follows:

[0025]

[0026] Where: M1, M2 are the number of filtered processing targets in the processing target set, and the judgment result stored in the receiving module corresponds to the number of filtered processing targets in the set of trucks in the non-processing target set; m all is the total number of processing targets in the processing target set; m skip is the total number of trucks that skip the screening stage in the processing target set; (ε new ) q is the latest truck overweight tendency performance value corresponding to the qth truck group; (ε last ) q is the qth group of trucks compared to (ε new ) q The last calculated truck overweight tendency performance value; λ is a constant; m lost The total number of trucks in the non-processing target set corresponding to the judgment result stored in the receiving module;

[0027] The values ​​of M1 and M2 are rounded up, x[·] is the judgment function, when [·]>0, x[·] takes the value of 1, otherwise, x[·] takes the value of 0, 1>λ>0, and the value of constant λ is substituted into equation (1) and equation (2), and it obeys

[0028] Among them, when the processing target set is screening the judgment target based on M1, the trucks with the largest overweight tendency performance value ε of the corresponding number of trucks in M1 are screened as the judgment target; when the judgment result stored in the receiving module corresponds to the set of trucks in the non-processing target set, when the judgment target is screened based on M2, the same number of trucks as M2 are randomly selected in the set as the judgment target.

[0029] Furthermore, the receiving module is interactively connected to the determination module via a wireless network, the determination module is interactively connected to the weighing module and the camera module via a wireless network, the receiving module is interactively connected to the management module and the evaluation module via a wireless network, the evaluation module is interactively connected to the screening module via a wireless network, and the screening module is interactively connected to the feedback module and the refresh module via a wireless network.

[0030] In a second aspect, a method for intelligent management of truck overweight records includes the following steps:

[0031] Step 1: Collect truck identification information and load information, and determine whether the truck is overweight based on the truck load information;

[0032] Step 2: Obtain the determination result of whether the truck is overweight, record the determination result, mark the determination result using the truck identification information, and further distinguish the recorded determination results based on the marking result;

[0033] Step 3: traverse the recorded judgment results, and evaluate the overweight tendency of the corresponding truck based on the judgment results;

[0034] Step 31: Setting phase of truck overweight tendency assessment logic;

[0035] Step 4: Based on the truck overweight tendency assessment results, the truck overweight determination targets are selected to form a determination target set, and the determination target set is used as a new round of truck overweight determination targets, and overweight determination is performed on each truck corresponding to the determination target set;

[0036] Step 41: the stage of setting the target screening logic for truck overweight determination;

[0037] Step 5: Refresh step execution.

[0038] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0039] The present invention provides an intelligent management system based on overweight records of trucks. During operation, the system collects truck identification information and load information, determines the overweight of trucks, and evaluates the overweight tendency of trucks, thereby screening trucks based on the evaluation results to form an overweight determination target set. Finally, the overweight determination target set is used to implement a spot check on the effectiveness of overweight problems of trucks, so that the process of overweight control of trucks has a certain degree of randomness, and the probability of occurrence of overweight problems of trucks is maintained as much as possible to show a downward trend, thereby improving the efficiency of overweight control of trucks and maintaining the health of highway transportation.

[0040] Furthermore, an intelligent management method based on overweight records of trucks is further configured for the operation of the above system, which provides further operation logic support for the system and ensures more stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is a structural diagram of an intelligent management system based on overweight records of trucks;

[0043] Figure 2 It is a flowchart of an intelligent management method based on overweight records of trucks;

[0044] Figure 3 It is a schematic diagram of the deployment status of the floor scale and the distance measuring sensor used for the integration of the weighing module in the present invention;

[0045] The numbers in the figure represent: 1. Floor scale; 2. Distance measuring sensor. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The present invention will be further described below in conjunction with the embodiments.

[0048] Embodiment 1:

[0049] This embodiment is based on an intelligent management system for overweight records of trucks, such as Figure 1 As shown, it includes: collection layer, recording layer and decision layer;

[0050] The identification information and load information of the truck are collected through the collection layer, and the collection layer further determines whether the truck is overweight based on the load information. The recording layer synchronously receives the determination result of whether the truck is overweight in the collection layer, records the determination result, and uses the recorded determination result of whether the truck is overweight to evaluate the overweight tendency of the truck. The decision layer synchronously receives the overweight tendency of the truck evaluated in the recording layer and refreshes the system operation. In the system refresh operation stage, based on the overweight tendency of the truck, the decision system refreshes the operation state and determines the next round of truck overweight monitoring targets;

[0051] The acquisition layer includes a camera module, a weighing module and a determination module. The camera module is used to collect image data of the truck and extract the truck license plate information from the truck image data. The weighing module is used to sense the weight of the truck. The determination module is used to set an overweight determination value and receive the truck weight sensed by the weighing module. Based on the comparison between the overweight determination value and the received truck weight, it is determined whether the truck is overweight.

[0052] The camera module is deployed on one side of the weighing module. The collected truck image data includes the truck license plate area image. After collecting the truck image data, the camera module segments the truck image data based on the license plate color, extracts the license plate area image from the truck image data, and further identifies the text information in the license plate area image, which is recorded as the truck license plate information. The truck license plate information is the identification information of the truck.

[0053] The recording layer includes a receiving module, a management module and an evaluation module. The receiving module is used to receive the determination result of whether the truck is overweight in the collection layer and store the received determination result. The management module is used to traverse the determination results stored in the receiving module and distinguish and manage the determination results. The evaluation module is used to obtain the determination results stored in the receiving module and evaluate the overweight tendency of the truck based on the determination results.

[0054] The evaluation logic of truck overweight tendency in the evaluation module is expressed as:

[0055]

[0056] Where: ε is the overweight tendency performance value of the truck; n is the set of judgment results of whether the truck is overweight; t i is the truck posture calibration time during the truck weighing phase before the output of the judgment result of whether the i-th truck is overweight; f i is the truck weighing result before outputting the judgment result of whether the truck is overweight for the i-th time; f rt is the truck overweight judgment value; g is the number of times that the truck weighing result in n is not overweight compared with the truck overweight judgment value; d(pi ,p i+1 ) is the distance between the weighing module that is the source of the judgment result of whether the i-th truck is overweight and the weighing module that is the source of the judgment result of whether the i+1-th truck is overweight; γ is the ratio of the number of times the weighing results of the n trucks are not overweight compared with the overweight judgment value of the trucks to the total number of weighing results in n;

[0057] Among them, the truck posture calibration time in the truck weighing stage, that is, the running time of the weighing module, the larger the truck overweight tendency performance value ε, the higher the probability of the truck overweight, and vice versa, the lower the probability of the truck overweight;

[0058] The decision layer includes a screening module, a feedback module and a refresh module. The screening module is used to identify the truck corresponding to the processing target set and the truck corresponding to the judgment result stored in the receiving module as a truck in the non-processing target set, and select the truck from the two sets as the judgment target for overweight. The feedback module is used to receive the judgment target screened in the screening module and feedback the judgment target to the camera module. The refresh module is used to refresh the system operation.

[0059] Among them, the feedback module feeds back the judgment target to the camera module, and when the refresh module refreshes the system operation, the camera module runs again to collect truck image data. When the extracted truck license plate information is consistent with the truck identification information of any group of trucks corresponding to the judgment target, the system further runs to determine whether the truck is overweight. Otherwise, the refresh camera module runs to collect new truck image data. After all judgment targets are refreshed and run to determine whether they are overweight, the system decision layer runs again;

[0060] The receiving module is interactively connected to the determination module through a wireless network, the determination module is interactively connected to the weighing module and the camera module through a wireless network, the receiving module is interactively connected to the management module and the evaluation module through a wireless network, the evaluation module is interactively connected to the screening module through a wireless network, and the screening module is interactively connected to the feedback module and the refresh module through a wireless network.

[0061] In this embodiment, the camera module runs to collect image data of the truck, extracts the truck license plate information from the truck image data, the weighing module synchronously senses the weight of the truck, the judgment module further sets an overweight judgment value, receives the weight of the truck sensed in the weighing module, and determines whether the truck is overweight based on the comparison between the overweight judgment value and the received truck weight. The receiving module runs to receive the judgment result of whether the truck is overweight in the collection layer, and stores the received judgment result. The management module traverses the judgment results stored in the receiving module in real time, distinguishes and manages the judgment results, and then the evaluation module obtains the judgment results stored in the receiving module, and evaluates the overweight tendency of the truck based on the judgment results. The screening module synchronously identifies the truck corresponding to the processing target set and the truck corresponding to the judgment result stored in the receiving module as a truck in the non-processing target set, and selects the truck as the overweight judgment target in the two sets. The feedback module runs to receive the judgment target screened in the screening module, and feeds back the judgment target to the camera module, and finally refreshes the system operation through the refresh module;

[0062] Through the operation of the system in the above embodiment, a more intelligent spot check method is provided for the overweight control of highway trucks, ensuring that the overweight control process of highway trucks is more efficient and accurate, which is conducive to maintaining highway safety and improving the efficiency and effectiveness of overweight control of highway trucks.

[0063] See also Figure 3 As shown, the figure further shows the deployment posture of the floor scale and the distance measuring sensor in the weighing module to ensure that the weighing module weighs the truck more accurately.

[0064] Embodiment 2:

[0065] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The intelligent management system based on overweight records of trucks in Example 1 is further described in detail:

[0066] The weighing module is integrated with a floor scale, a loudspeaker and a distance sensor. When the weighing module senses the weight of the truck, the truck drives onto the surface of the floor scale, and the weight of the truck is sensed by the floor scale. There are several groups of distance sensors, and the groups of distance sensors are equidistantly deployed at the edge contour of the floor scale. The number of deployed distance sensors is proportional to the area of ​​the floor scale. The judgment module receives the weight of the truck sensed by the weighing module and the distance measurement result of the distance sensor in real time. When the sensed weight of the truck exceeds the set overweight judgment value, the truck is judged to be overweight. The distance measurement results of the distance sensor are further traversed in real time to identify whether there is a distance measurement result with a distance measurement value of zero in the distance measurement results of each distance measurement sensor. When the recognition result is no, the judgment module ends its operation. When the recognition result is yes, the loudspeaker is triggered to operate.

[0067] Among them, when the speaker is triggered to run, a text audio is played to prompt the truck driver to control the movement of the truck, so that the position of the truck on the surface of the scale is offset. During the movement of the truck, the ranging sensor runs in real time. When it is detected that there is no ranging result with a ranging value of zero in each ranging result, the speaker is triggered to run again, and a text message is played to prompt the truck driver to park the truck. The scale runs again, and the sensed weight of the truck is fed back to the judgment module again.

[0068] Through the above settings, the composition structure and operation logic of the weighing module are further limited to ensure the stable operation of the weighing module, to weigh the trucks to be managed, and to provide necessary operation data support for the operation of subsequent modules in the system.

[0069] like Figure 1 As shown, when the management module traverses the judgment results stored in the receiving module, it simultaneously obtains the truck identification information corresponding to each judgment result, and further marks each judgment result based on the truck identification information. The management module performs a differentiated management stage for the judgment results. In the receiving module, the judgment results are differentiated and processed based on the truck identification information marked with the judgment results, so that the judgment results are differentiated and stored in the receiving module.

[0070] Through the above settings, the management module further differentiates and manages the determination results stored in the receiving module, so that the determination results stored in the receiving module have traceability conditions and provide necessary operation data support for the operation of the modules in the system.

[0071] like Figure 1 As shown, the evaluation module runs the synchronous statistics receiving module to determine whether the judgment results stored in each differentiated storage interval are greater than ten groups, and the differentiated storage intervals greater than ten groups correspond to trucks, which serve as the decision-making layer to run the processing target set.

[0072] Through the above settings, further logical operation support is provided for the truck overweight tendency evaluation logic set in the evaluation module.

[0073] like Figure 1 As shown, there are several groups of weighing modules, and the weighing modules in the groups perform data exchange through the optical fiber network for operation and processing by the determination module of the collection layer in the system;

[0074] When the value of is less than 0, the corresponding truck is not used as the truck overweight tendency assessment target and is directly placed in the decision-making layer operation processing target set.

[0075] Through the above settings, the decision-making layer operation and processing target set is further limited.

[0076] like Figure 1As shown, the screening module screens the target determination stage, processes the trucks that are directly placed in the target set, skips the screening stage, and directly serves as one of the determination targets fed back by the feedback module. The screening logic of the screening module is expressed as:

[0077]

[0078] Where: M1, M2 are the number of filtered processing targets in the processing target set, and the judgment result stored in the receiving module corresponds to the number of filtered processing targets in the set of trucks in the non-processing target set; m all is the total number of processing targets in the processing target set; m skip is the total number of trucks that skip the screening stage in the processing target set; (ε new ) q is the latest truck overweight tendency performance value corresponding to the qth truck group; (ε last ) q is the qth group of trucks compared to (ε new ) q The last calculated overweight tendency performance value of trucks; λ is a constant; mlost is the total number of trucks in the non-processing target set corresponding to the judgment result stored in the receiving module;

[0079] The values ​​of M1 and M2 are rounded up, x[·] is the judgment function, when [·]>0, x[·] takes the value of 1, otherwise, x[·] takes the value of 0, 1>λ>0, and the value of constant λ is substituted into equation (1) and equation (2), and it obeys

[0080] Among them, when the processing target set is screening the judgment target based on M1, the trucks with the largest overweight tendency performance value ε of the corresponding number of trucks in M1 are screened as the judgment target; when the judgment result stored in the receiving module corresponds to the set of trucks in the non-processing target set, when the judgment target is screened based on M2, the same number of trucks as M2 are randomly selected in the set as the judgment target.

[0081] Through the above settings, the screening logic of the screening module is further limited to ensure that the new round of system operation has the specified operation processing goals.

[0082] Embodiment 3:

[0083] In terms of specific implementation, based on Example 1, this example refers to Figure 2 The intelligent management system based on overweight records of trucks in Example 1 is further described in detail:

[0084] An intelligent management method based on overweight records of trucks, comprising the following steps:

[0085] Step 1: Collect truck identification information and load information, and determine whether the truck is overweight based on the truck load information;

[0086] Step 2: Obtain the determination result of whether the truck is overweight, record the determination result, mark the determination result using the truck identification information, and further distinguish the recorded determination results based on the marking result;

[0087] Step 3: traverse the recorded judgment results, and evaluate the overweight tendency of the corresponding truck based on the judgment results;

[0088] Step 31: Setting phase of truck overweight tendency assessment logic;

[0089] Step 4: Based on the truck overweight tendency assessment results, the truck overweight determination targets are selected to form a determination target set, and the determination target set is used as a new round of truck overweight determination targets, and overweight determination is performed on each truck corresponding to the determination target set;

[0090] Step 41: the stage of setting the target screening logic for truck overweight determination;

[0091] Step 5: Refresh step execution.

[0092] In summary, during operation, the system and method in the above embodiments collect truck identification information and load information, determine that the truck is overweight, and evaluate the overweight tendency of the truck. Based on the evaluation results, the trucks are screened to form an overweight determination target set, and finally the overweight determination target set is used to implement a spot check on the effectiveness of truck overweight problems, so that the truck overweight control process has a certain degree of randomness, and the probability of truck overweight problems is maintained as much as possible. The efficiency of truck overweight control is improved, and the health of road transportation is maintained.

[0093] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will 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. An intelligent management system based on overweight records of trucks, characterized in that: include: Collection layer, recording layer and decision-making layer; The identification information and load information of the truck are collected through the collection layer, and the collection layer further determines whether the truck is overweight based on the load information. The recording layer synchronously receives the determination result of whether the truck is overweight in the collection layer, records the determination result, and uses the recorded determination result of whether the truck is overweight to evaluate the overweight tendency of the truck. The decision layer synchronously receives the overweight tendency of the truck evaluated in the recording layer and refreshes the system operation. In the system refresh operation stage, based on the overweight tendency of the truck, the decision system refreshes the operation state and determines the next round of truck overweight monitoring targets; The recording layer includes a receiving module, a management module and an evaluation module. The receiving module is used to receive the determination result of whether the truck is overweight in the collection layer and store the received determination result. The management module is used to traverse the determination results stored in the receiving module and distinguish and manage the determination results. The evaluation module is used to obtain the determination results stored in the receiving module and evaluate the overweight tendency of the truck based on the determination results. The evaluation logic of truck overweight tendency in the evaluation module is expressed as follows: Where: ε is the overweight tendency performance value of the truck; n is the set of judgment results of whether the truck is overweight; t i It is the truck posture calibration time during the truck weighing phase before the output of the judgment result of whether the i-th truck is overweight; f i is the truck weighing result before outputting the judgment result of whether the truck is overweight for the i-th time; f rt is the truck overweight judgment value; g is the number of times that the truck weighing result in n is not overweight compared with the truck overweight judgment value; d(p i ,p i+1 ) is the distance between the weighing module that is the source of the judgment result of whether the i-th truck is overweight and the weighing module that is the source of the judgment result of whether the i+1-th truck is overweight; γ is the ratio of the number of times the weighing results of the n trucks are not overweight compared with the overweight judgment value of the trucks to the total number of weighing results in n; Among them, the truck posture calibration time in the truck weighing stage, that is, the running time of the weighing module, the larger the truck overweight tendency performance value ε is, the higher the probability of the truck being overweight, and vice versa.

2. According to claim 1, the intelligent management system based on overweight records of trucks is characterized in that: The acquisition layer includes a camera module, a weighing module and a determination module. The camera module is used to collect image data of the truck and extract the truck license plate information from the truck image data. The weighing module is used to sense the weight of the truck. The determination module is used to set an overweight determination value, receive the truck weight sensed in the weighing module, and determine whether the truck is overweight based on the comparison between the overweight determination value and the received truck weight. Among them, the camera module is deployed on one side of the weighing module, and the collected truck image data includes the truck license plate area image. After collecting the truck image data, the camera module segments the truck image data based on the license plate color, extracts the license plate area image in the truck image data, and further identifies text information in the license plate area image, which is recorded as the truck license plate information. The truck license plate information is the identification information of the truck.

3. The intelligent management system based on overweight records of trucks according to claim 2 is characterized in that: The weighing module is integrated with a floor scale, a loudspeaker and a distance measuring sensor. When the weighing module senses the weight of the truck, the truck drives into the surface of the floor scale, and the weight of the truck is sensed by the floor scale. The distance measuring sensors are provided in several groups, and the several groups of distance measuring sensors are equidistantly deployed at the edge contour positions of the floor scale. The number of deployed distance measuring sensors is proportional to the area of ​​the floor scale. The judgment module receives the weight of the truck sensed by the weighing module and the distance measuring result of the distance measuring sensor in real time. When the sensed weight of the truck exceeds the set overweight judgment value, the truck is judged to be overweight. The distance measuring results of the distance measuring sensor are further traversed in real time to identify whether there is a distance measuring result with a distance measuring value of zero in the distance measuring results of each distance measuring sensor. When the recognition result is no, the judgment module ends the operation. When the recognition result is yes, the loudspeaker is triggered to operate. Among them, when the speaker is triggered to run, a text audio is played to prompt the truck driver to control the movement of the truck, so that the position of the truck on the surface of the scale is offset. During the movement of the truck, the ranging sensor runs in real time. When it is detected that there is no ranging result with a ranging value of zero in each ranging result, the speaker is triggered to run again, and a text message is played to prompt the truck driver to park the truck. The scale runs again, and the sensed weight of the truck is fed back to the judgment module again.

4. The intelligent management system based on overweight records of trucks according to claim 1 is characterized in that: When the management module traverses the judgment results stored in the receiving module, it simultaneously obtains the truck identification information corresponding to each judgment result, and further marks each judgment result based on the truck identification information. The management module performs a differentiated management stage for the judgment results. In the receiving module, the judgment results are differentiated and processed based on the truck identification information marked with the judgment results, so that the judgment results are differentiated and stored in the receiving module.

5. The intelligent management system based on overweight records of trucks according to claim 1 is characterized in that: The evaluation module runs synchronous statistics to determine whether the judgment results stored in each differentiated storage interval in the receiving module are greater than ten groups, and the differentiated storage intervals greater than ten groups correspond to trucks, which serve as the decision-making layer to run the processing target set.

6. The intelligent management system based on overweight records of trucks according to claim 1 is characterized in that: The weighing modules are provided in several groups, and the weighing modules in several groups perform data exchange through the optical fiber network for operation and processing by the determination module of the collection layer in the system; Said When the value of is less than 0, the corresponding truck is not used as the truck overweight tendency assessment target and is directly placed in the decision-making layer operation processing target set.

7. The intelligent management system based on overweight records of trucks according to claim 1 is characterized in that: The decision layer includes a screening module, a feedback module and a refresh module. The screening module is used to identify that the truck corresponding to the processing target set and the truck corresponding to the judgment result stored in the receiving module is a truck in the non-processing target set, and the truck is selected from the two sets as the judgment target for overweight. The feedback module is used to receive the judgment target screened in the screening module and feed back the judgment target to the camera module. The refresh module is used to refresh the system operation. Among them, the feedback module feeds back the judgment target to the camera module, and when the refresh module refreshes the system operation, the camera module runs again to collect truck image data. When the extracted truck license plate information is consistent with the truck identification information of any group of trucks corresponding to the judgment target, the system runs further to determine whether the truck is overweight. Otherwise, the refresh camera module runs to collect new truck image data. After all judgment targets are refreshed and run to determine whether they are overweight, the system decision layer runs again.

8. The intelligent management system based on overweight records of trucks according to claim 7 is characterized in that: The screening module screens and determines the target stage, processes the trucks that are directly placed in the target set, skips the screening stage, and directly serves as one of the determination targets fed back by the feedback module. The screening logic of the screening module is expressed as follows: Where: M1, M2 are the number of filtered processing targets in the processing target set, and the judgment result stored in the receiving module corresponds to the number of filtered processing targets in the set of trucks in the non-processing target set; m a ll is the total number of processing targets in the processing target set; m skip is the total number of trucks that skip the screening stage in the processing target set; (ε new ) q is the latest truck overweight tendency performance value corresponding to the qth truck group; (ε last ) q is the qth group of trucks compared to (ε new ) q The last calculated overweight tendency performance value of trucks; λ is a constant; mlost is the total number of trucks in the non-processing target set corresponding to the judgment result stored in the receiving module; The values ​​of M1 and M2 are rounded up, x[·] is the judgment function, when [·]>0, x[·] takes the value of 1, otherwise, x[·] takes the value of 0, 1>λ>0, and the value of constant λ is substituted into equation (1) and equation (2), and it obeys Among them, when the processing target set is screening the judgment target based on M1, the trucks with the largest overweight tendency performance value ε of the corresponding number of trucks in M1 are screened as the judgment target; when the judgment result stored in the receiving module corresponds to the set of trucks in the non-processing target set, when the judgment target is screened based on M2, the same number of trucks as M2 are randomly selected in the set as the judgment target.

9. The intelligent management system based on overweight records of trucks according to claim 1 is characterized in that: The receiving module is interactively connected to the determination module via a wireless network, the determination module is interactively connected to the weighing module and the camera module via a wireless network, the receiving module is interactively connected to the management module and the evaluation module via a wireless network, the evaluation module is interactively connected to the screening module via a wireless network, and the screening module is interactively connected to the feedback module and the refresh module via a wireless network.

10. A method for intelligent management of truck overweight records, the method being an implementation method of the method for intelligent management of truck overweight records as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect truck identification information and load information, and determine whether the truck is overweight based on the truck load information; Step 2: Obtain the determination result of whether the truck is overweight, record the determination result, mark the determination result using the truck identification information, and further distinguish the recorded determination results based on the marking result; Step 3: traverse the recorded judgment results, and evaluate the overweight tendency of the corresponding truck based on the judgment results; Step 31: Setting phase of truck overweight tendency assessment logic; Step 4: Based on the truck overweight tendency assessment results, the truck overweight determination targets are selected to form a determination target set, and the determination target set is used as a new round of truck overweight determination targets, and overweight determination is performed on each truck corresponding to the determination target set; Step 41: the stage of setting the target screening logic for truck overweight determination; Step 5: Refresh step execution.

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