CN117973957A

CN117973957A - Weighing data analysis method, device, equipment and storage medium

Info

Publication number: CN117973957A
Application number: CN202410201686.4A
Authority: CN
Inventors: 何铭
Original assignee: Shanghai Zhongtongji Network Technology Co Ltd
Current assignee: Shanghai Zhongtongji Network Technology Co Ltd
Priority date: 2024-02-23
Filing date: 2024-02-23
Publication date: 2024-05-03

Abstract

The invention discloses a weighing data analysis method, a weighing data analysis device, weighing data analysis equipment and a storage medium. The method comprises the following steps: acquiring a data set to be analyzed, wherein the data set to be analyzed at least comprises current binding weighing data, historical binding weighing data and unbound weighing data acquired from the last binding weighing data corresponding to the current binding weighing data in the historical binding weighing data to the current binding weighing data; determining a first result through discrete conditions of historical binding weighing data and current binding weighing data, wherein the first result indicates whether a first operation abnormality exists or not; determining a second result according to the current binding weighing data, the last binding weighing data and the unbound weighing data, wherein the second result indicates whether a second operation abnormality and a weighing equipment abnormality exist or not; and determining whether the current binding weighing data is abnormal or not based on the first result and the second result. The method improves the reliability of weighing data sources while reducing the cost.

Description

Weighing data analysis method, device, equipment and storage medium

Technical Field

The embodiment of the invention relates to the technical field of logistics, in particular to a weighing data analysis method, a weighing data analysis device, weighing data analysis equipment and a storage medium.

Background

In the field of logistics, it is often necessary to acquire the weight of an article and to settle the cost based on the weight. The weight of the article can be obtained by at least the following ways: binding of the article and the weight is completed through dynamic weighing, binding of the article and the weight is completed through a bag supplying table, and binding of the article and the weight is completed through artificial combination of intelligent scanning equipment and weighing equipment (such as Bluetooth weighing).

For the situation that the intelligent scanning equipment and the weighing equipment are combined manually to finish the binding of the object and the weight, the problem of weighing data abnormality caused by irregular personnel operation or abnormal weighing equipment may exist. In the prior art, a camera can be installed in a manually weighed area, and monitoring personnel operate and weight of weighing equipment are read to perform abnormal analysis of weighing data, but the manpower and material resources consumed by floor implementation of the scheme are large, and the output and the payment are not in direct proportion.

Disclosure of Invention

The invention provides a weighing data analysis method, a weighing data analysis device, weighing data analysis equipment and a storage medium, which can improve the reliability of weighing data sources at the same time of low cost.

In a first aspect, an embodiment of the present invention provides a method for analyzing weighing data, including:

acquiring a data set to be analyzed, wherein the data set to be analyzed at least comprises current binding weighing data, historical binding weighing data and unbound weighing data acquired from last binding weighing data corresponding to the current binding weighing data in the historical binding weighing data to the current binding weighing data, and the binding weighing data is the weighing data of a binding weighing article;

Determining a first result according to the discrete condition of the historical binding weighing data and the current binding weighing data, wherein the first result indicates whether a first operation abnormality exists or not;

Determining a second result according to the current binding weighing data, the last binding weighing data and the unbound weighing data, wherein the second result indicates whether a second operation abnormality and a weighing equipment abnormality exist or not;

And determining whether the current binding weighing data is abnormal or not based on the first result and the second result.

In a second aspect, an embodiment of the present invention provides a weighing data analysis apparatus, including:

The acquisition module is used for acquiring a data set to be analyzed, wherein the data set to be analyzed at least comprises current binding weighing data, historical binding weighing data and unbound weighing data acquired from last binding weighing data corresponding to the current binding weighing data in the historical binding weighing data to the current binding weighing data, and the binding weighing data is the weighing data of a binding weighing object;

The first determining module is used for determining a first result according to the discrete condition of the historical binding weighing data and the current binding weighing data, and the first result indicates whether a first operation abnormal condition exists or not;

The second determining module is used for determining a second result according to the current binding weighing data, the last binding weighing data and the unbound weighing data, wherein the second result indicates whether a second operation abnormality and a weighing equipment abnormality exist or not;

and the third determining module is used for determining whether the current binding weighing data is abnormal or not based on the first result and the second result.

In a third aspect, an embodiment of the present invention provides a weighing data analysis apparatus, including:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method according to the first aspect.

In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements a method as described in the first aspect.

According to the technical scheme, additional hardware and labor cost are not needed, the first result indicating whether the first operation abnormality exists or not and the second result indicating whether the second operation abnormality and the weighing equipment abnormality exist or not can be determined through analysis of the data in the data set to be analyzed, the abnormality analysis of the weighing data is realized based on the first result and the second result, and therefore the reliability of the weighing data source is improved while the cost is low.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for analyzing weighing data according to a first embodiment of the present invention;

FIG. 2 is a flow chart of a method for analyzing weighing data according to a second embodiment of the present invention;

Fig. 3 is a schematic diagram of an intelligent scanning device acquiring weighing data of a weighing device according to a second embodiment of the present invention;

FIG. 4 is a schematic illustration of a situation where there is no second operational anomaly and no weighing apparatus anomaly provided in accordance with a second embodiment of the present invention;

FIG. 5 is a schematic diagram of a situation where there is no second operational anomaly but there is a weighing apparatus anomaly provided in accordance with a second embodiment of the present invention;

FIG. 6 is a schematic diagram of another situation where there is no second operational anomaly but there is a weighing apparatus anomaly provided in accordance with embodiment two of the present invention;

FIG. 7 is a schematic diagram of a second operational anomaly provided in accordance with a second embodiment of the present invention;

FIG. 8 is a flow chart of another method for analyzing weighing data according to a second embodiment of the present invention;

fig. 9 is a schematic structural view of a weighing data analysis apparatus according to a third embodiment of the present invention;

Fig. 10 is a schematic structural view of a weighing data analysis apparatus embodying an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like herein are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a method for analyzing weighing data according to an embodiment of the present invention, where the method may be applied to a case of performing anomaly analysis on weighing data, and the method may be performed by a weighing data analysis apparatus, which may be implemented in the form of software and/or hardware and integrated in a weighing data analysis apparatus.

As shown in fig. 1, the method includes:

S110, acquiring a data set to be analyzed, wherein the data set to be analyzed at least comprises current binding weighing data, historical binding weighing data and unbound weighing data acquired from last binding weighing data corresponding to the current binding weighing data in the historical binding weighing data to the current binding weighing data, and the binding weighing data is weighing data of binding weighing objects.

When the weight of an article needs to be acquired in the fields of logistics and the like, the article can be weighed through the weighing equipment, and the data obtained by weighing through the weighing equipment can be considered as weighing data. The weighing device may be a device for weighing, such as a bluetooth scale or the like.

The weighing device can continuously transmit the acquired weighing data to the intelligent scanning device, for example, according to the time sequence of acquiring the weighing data, the intelligent scanning device is connected through Bluetooth to realize data transmission.

The intelligent scanning device may be a device for scanning an identification corresponding to a weighed item, such as a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA). The intelligent scanning device can bind the scanned weighing object with the weighing data of the weighing device during scanning to obtain binding weighing data when the identification (such as a bar code) corresponding to the weighing object is scanned. The binding weighing data may be weighing data of the binding weighing object, and the binding may be understood as that the weighing data is bound with the identification of the weighing object, so as to indicate that the binding weighing data is the weighing data for the corresponding weighing object.

Among the weighing data acquired by the intelligent scanning device, the other weighing data are unbound weighing data which are not identified and bound except the binding weighing data of the binding weighing object, and the part of the weighing data can be understood as data which are only acquired by the weighing data but not bound with the weighing object.

The binding weighing data and the unbinding weighing data determined by the intelligent scanning equipment can be synchronized to the weighing data analysis equipment according to the time sequence of data acquisition, so that the weighing data is subjected to abnormal analysis by the weighing data analysis equipment.

In this step, the weighing data analysis device may continuously acquire data transmitted by the intelligent scanning device, and the acquired data set may be understood as a data set to be analyzed, and the acquired data may be stored locally for performing anomaly analysis of the data. The data set to be analyzed may be understood as a data set required in analyzing whether the current binding weighing data is abnormal or not.

The current binding weighing data included in the data set to be analyzed may be binding weighing data acquired at the current time. The historical binding weight data may be binding weight data acquired prior to the time of acquisition of the current binding weight data (i.e., the current time), which may include a plurality of binding weight data. The last binding weight data may be last binding weight data acquired before the current binding weight data in the history binding weight data. The unbind weighing data obtained from the last binding weighing data to the current binding weighing data can be understood as the weighing data which is obtained from the last binding weighing data to the current binding weighing data and is not subjected to identification binding in the time period before the current binding weighing data is obtained, and the number of the unbind weighing data can be one or more, and the unbind weighing data is not limited herein.

The weighing data 1 is the last binding weighing data (namely the last binding weighing data in the historical binding weighing data) acquired before the current moment, and the weighing data is the weighing data bound with the article A; after the weighing data 1, continuing to acquire weighing data 2, 3, 4, 5 and 6 until weighing data 7 is acquired; the weighing data 7 are binding weighing data (namely current binding weighing data) acquired at the current moment, and the weighing data are weighing data bound with the article B; the data set to be analyzed may include at least the current bound weight data (weight data 7), the last bound weight data (weight data 1), and unbound weight data (weight data 2, 3, 4, 5, 6) obtained between the last bound weight data and the current bound weight data.

S120, determining a first result according to the discrete condition of the historical binding weighing data and the current binding weighing data, wherein the first result indicates whether a first operation abnormal condition exists or not.

In this step, the discrete condition of the historical binding weighing data and the current binding weighing data can be reflected by the standard deviation of the historical binding weighing data and the current binding weighing data. And comparing the standard deviation of the historical binding weighing data and the current binding weighing data with preset parameters to determine a first result.

Wherein the first result may be a result indicating whether there is a case of the first operation abnormality. The first operation abnormality may be understood that the weighing object corresponding to the currently bound weighing data and the weighing object weighed before the current moment may be the same object, and the standard deviation is smaller than the preset parameter when the first operation abnormality occurs. The preset parameters may be determined according to practical application requirements, and are not limited herein.

S130, determining a second result according to the current binding weighing data, the last binding weighing data and the unbound weighing data, wherein the second result indicates whether a second operation abnormality and a weighing equipment abnormality exist or not.

In this step, the second result may be determined by determining the maximum and minimum values of the current binding weight data, the previous binding weight data, and the unbinding weight data, and then comparing the maximum value with the minimum value, comparing the maximum value with zero, and comparing the minimum value with zero. The second result may be a result indicating whether there is a second operational anomaly and a weighing apparatus anomaly.

The second operation anomaly may be understood as a complete operation flow in which the operation user does not take the weighing object corresponding to the last binding weighing data out of the weighing device after the last weighing is completed, and then places the weighing object corresponding to the current binding weighing data on the weighing device. And when the second operation is abnormal, the maximum value is consistent with the minimum value, and the operation user is instructed not to execute the complete operation flow of taking up and putting down the weighing object.

A weighing device anomaly may be understood as a weighing device return-to-zero anomaly. Under normal conditions, the operation user takes the weighing object corresponding to the last binding weighing data out of the weighing equipment, and then places the weighing object corresponding to the current binding weighing data on the weighing equipment. The process involves the zeroing of the weighing device, the weighing data of the weighing device is the minimum value during zeroing, and if the minimum value is not zero, the zeroing abnormality of the weighing device is indicated.

S140, determining whether the current binding weighing data is abnormal or not based on the first result and the second result.

In the step, if the first result indicates that the first operation abnormality does not exist and the second result indicates that the second operation abnormality and the weighing equipment abnormality do not exist, the fact that the current binding weighing data is not abnormal is indicated, that is, in the process of acquiring the current binding weighing data, personnel operation is not abnormal and the weighing equipment is not abnormal, and the acquired current binding weighing data is reliable in source.

If the first result indicates that the first operation is abnormal, the second result indicates that the second operation is abnormal, and the second result indicates that the weighing equipment is abnormal, one of the three conditions occurs, the condition that the current binding weighing data is abnormal and the reason for causing the data abnormality is that the condition occurs in the three conditions, and one or more of the three conditions can be adopted.

In one embodiment, the method further comprises: and marking the current binding weighing data under the condition that the current binding weighing data is determined to be abnormal.

The mode of marking the abnormal data is not limited, for example, the specific reason of the abnormality of the current binding weighing data can be marked directly; as another example, only the status of the data exception may be marked but not the specific cause of the exception.

The method has the advantages that the abnormal data is marked, the subsequent downstream system marking based on the data is facilitated, and the abnormal data is further analyzed by combining a plurality of dimensions such as an operation user corresponding to the abnormal data, a unique identification code of weighing equipment, an operation time period, a network point/transfer center and the like.

Example two

Fig. 2 is a flowchart of a weighing data analysis method according to a second embodiment of the present invention, which is further refined on the basis of the first embodiment, and as shown in fig. 2, the method includes:

S111, taking data transmitted by the intelligent scanning device as a data set to be analyzed; the intelligent scanning equipment is used for acquiring weighing data transmitted by the weighing equipment; the intelligent scanning equipment is used for scanning the identifier corresponding to the weighing object and binding the weighing data of the weighing equipment with the identifier of the scanned weighing object.

Fig. 3 is a schematic diagram of an intelligent scanning device acquiring weighing data of a weighing device according to a second embodiment of the present invention. As shown in fig. 3, the weighing device is a bluetooth scale, and the weighing device is connected with the intelligent scanning device through a bluetooth socket (also called BluetoothSocket) so as to establish a data transmission channel. In the data transmission channel, the weighing device continuously transmits weighing data to an application program of the intelligent scanning device, and the application program continuously receives and updates the weighing data.

The weight 1 (i.e. the weighing data 1) shown in fig. 3 can be understood as the last binding weighing data acquired before the current moment, wherein the last binding weighing data is obtained by binding the weight 1 with the article a by the intelligent scanning device through scanning the waybill number of the article a; continuing to acquire unbound weighing data, such as weights 2, 3, 4, 5, 6, after weight 1; continuously acquiring weighing data to obtain weight 7, wherein the weight 7 can be understood as current binding weighing data, and the current binding weighing data is obtained by binding the weight 7 with the article B by the intelligent scanning equipment through scanning the waybill number of the article B; the data set to be analyzed may include at least the current bound weight data (weight 7), the last bound weight data (weight 1), and unbound weight data (weights 2, 3, 4, 5, 6) acquired between the last bound weight data and the current bound weight data.

The illustration in fig. 3 can be understood as a complete data transmission from the last weighing to the current weighing, only as an exemplary illustration of part of the data set to be analyzed. Optionally, the data set to be analyzed may further include one or more binding weight data acquired before the last binding weight data, which is not shown in fig. 3.

The binding weighing data and the unbinding weighing data determined by the intelligent scanning equipment can be synchronized to the weighing data analysis equipment according to the time sequence of data acquisition.

S121, determining the discrete condition, wherein the discrete condition is determined based on standard deviations of a plurality of binding weighing data included in the historical binding weighing data and the current binding weighing data.

The standard deviation of the plurality of binding weighing data included in the historical binding weighing data and the current binding weighing data can be determined by the following formula:

Wherein σ represents the final calculated standard deviation; n represents the number of the plurality of binding weight data included in the historical binding weight data, for example, N may be 10; x _i represents each binding weight data included in the historical binding weight data; mu represents the current binding weighing data.

It should be noted that, because we need to calculate the degree of dispersion of the plurality of binding weighing data included in the historical binding weighing data and the current binding weighing data, μ does not use the average value of all the data included in the historical binding weighing data in the present invention.

S122, determining that the first result indicates that the first operation abnormality does not exist under the condition that the standard deviation is larger than or equal to a preset parameter; otherwise, determining that the first result indicates that a first operation abnormality exists; and the first operation abnormality indicates that the weighing object corresponding to the current binding weighing data is repeatedly weighed.

If the standard deviation is greater than or equal to the preset parameter, the discrete degree of the plurality of historical binding weighing data and the current binding weighing data is larger, the weighing objects corresponding to the current binding weighing data and the weighing objects weighed before the current moment are not close, the weighing objects are not likely to be weighed by the same object, and the first result is determined to indicate that the first operation abnormality does not exist.

If the standard deviation is smaller than the preset parameter, the discrete degree of the plurality of historical binding weighing data and the current binding weighing data is smaller, the closer the weight of the weighing object corresponding to the current binding weighing data and the weight of the weighing object weighed before the current moment is, the more likely the weighing object is to be weighed by the same object, and the first result is determined to indicate that the first operation abnormality exists.

S131, determining the maximum value and the minimum value in the current binding weighing data, the last binding weighing data and the unbound weighing data.

The determination mode of the maximum value or the minimum value is not limited, and the maximum value and the minimum value in the current binding weighing data, the last binding weighing data and the unbound weighing data (one or more) can be determined through any algorithm capable of determining the maximum value or the minimum value.

S132, determining a second result based on the maximum value and the minimum value.

In one embodiment, determining the second result based on the maximum value and the minimum value comprises:

determining that the second result indicates that there is no second operational anomaly and no weighing device anomaly if the maximum value is not equal to the minimum value, the maximum value is greater than zero, and the minimum value is equal to zero;

Determining that the second result indicates that there is no second operational anomaly but there is a weighing device anomaly if the maximum value is not equal to the minimum value, the maximum value is greater than zero, and the minimum value is not equal to zero;

In the case where the maximum value is equal to the minimum value, it is determined that the second result indicates that there is a second operational anomaly.

In one embodiment, the absence of the second operation anomaly indicates that the weighing object corresponding to the last binding weighing data is taken away from the weighing device by the operation user, and the weighing object corresponding to the current binding weighing data is placed on the weighing device by the operation user after the taking away;

The weighing device anomaly indicates a zeroing anomaly of the weighing device. The weighing equipment is reset to zero abnormality, and the weighing equipment can be reset to zero to be a negative number and reset to be a positive number.

The determination of the second result based on the maximum and minimum values is described in detail in connection with the following figures 4-7, wherein figures 4-7 can be understood as a data wave diagram obtained by a complete data transmission process from the last weighing (weighing of item a) to the current weighing (weighing of item B) shown in figure 3.

Fig. 4 is a schematic diagram of a situation where there is no second operation abnormality and no weighing apparatus abnormality according to the second embodiment of the present invention. As shown in fig. 4, the maximum value is not equal to the minimum value, the maximum value is greater than zero, and the minimum value is equal to zero, it is determined that the operation user performs the complete operation flow of taking up and putting down the weighing object, and the bluetooth scale returns to zero normally, that is, there is no second operation abnormality and no weighing device abnormality.

Fig. 5 is a schematic diagram of a case where there is no second operation abnormality but there is a weighing apparatus abnormality according to the second embodiment of the present invention. As shown in fig. 5, the maximum value is not equal to the minimum value, the maximum value is greater than zero, and the minimum value is greater than zero, so that it is determined that the operation user performs the complete operation flow of taking up and putting down the weighing object, but the bluetooth scale returns to zero to be a positive number, which results in a larger actual weighing result, i.e. no second operation abnormality is present but the weighing device is present.

Fig. 6 is a schematic diagram of another case where there is no second operation abnormality but there is a weighing apparatus abnormality provided according to the second embodiment of the present invention. As shown in fig. 6, the maximum value is not equal to the minimum value, the maximum value is greater than zero, and the minimum value is less than zero, so that the complete operation flow of taking up and putting down the weighing object by the operation user is determined, but the bluetooth is reset to be negative, which results in a smaller actual weighing result, namely, the condition that the second operation abnormality is not present but the weighing device abnormality is present.

Fig. 7 is a schematic diagram of a case where there is a second operation abnormality according to the second embodiment of the present invention. As shown in fig. 7, from the last weighing to the current weighing bluetooth weighing, no change is made, and it is determined that the operation user does not perform the complete operation flow of taking up and putting down the weighed item, that is, there is a second operation abnormality.

Whether the current binding weighing data is abnormal or not is determined based on the first result and the second result is explained by the following example. Table 1 shows a description of determining whether the current binding weighing data is abnormal, wherein 0.5 is a preset parameter.

Table 1 Condition illustrations of determining whether the currently bound weigh data is anomalous

It should be noted that there is a special case that the weighing object is a standard component of express delivery, and its size and weight are all consistent, at this time, the image of the express delivery and a specific weighing scene can be combined to perform the judgment of data abnormality, which is not specifically described herein.

According to the technical scheme, analysis of the degree of dispersion of the statistical standard deviation is carried out through historical binding weighing data and current binding weighing data, and whether the first operation abnormality exists is determined; determining whether a second operation abnormality and a weighing equipment abnormality exist or not according to fluctuation conditions of data from the last binding weighing to the current binding weighing; by combining the two conditions to analyze the abnormality of the current binding weighing data, the reliability of the weighing data source can be improved while the cost is low, and the accuracy of weighing weight charging is further improved.

It should be noted that, the above embodiment is directed to data anomaly determination for weighing by an operator in combination with an intelligent scanning device and a weighing device (bluetooth scale). For the situation that the Bluetooth scale is not needed to be used for weighing, such as the situation that other weighing equipment such as a dynamic scale is used, the weighing objects are placed on the conveyor belt, and whether the operation of an operator is abnormal or not (namely, the situation that the first operation abnormality and the second operation abnormality do not exist) is not related, so that whether the weighing equipment is abnormal or not can be judged only through the fluctuation condition of all data in the current weighing time period. The current weighing period may be a period of weighing the current weighing object, which is not limited herein.

Fig. 8 is a flowchart of another weighing data analysis method according to the second embodiment of the present invention, where the method shown in fig. 8 is for anomaly analysis of different application scenarios, and the application scenarios include weighing by an operator in combination with an intelligent scanning device (PDA) and a weighing device (bluetooth scale), and weighing by a dynamic scale or other weighing devices.

The scenario for weighing by an operator in combination with a PDA and a bluetooth scale (i.e., the scenario for a receive weigh scan or a receive weigh scan of the center/dot of the left branch shown in fig. 8) is described as follows:

When an operator scans a face sheet of a current weighing object through the PDA to bind weighing data, abnormal analysis of the data is carried out by acquiring a data set to be analyzed. Specifically, due to the need for manual operations, there may be abnormal operations such as operator lazy and the like (e.g., find a fixed object on the scale, unwilling to bend down for carrying, weigh with another convenient object), and whether the first operation is abnormal may be determined by the total standard deviation of the historical data (i.e., the historical binding weighing data) and the current data (i.e., the current binding weighing data). By judging the fluctuation of the whole weighing flow data, the operation (such as the picking up and putting down of the article) of a user and the return-to-zero condition of the scale in the operation process of the user can be restored, and whether the second operation abnormality and the weighing equipment abnormality exist or not can be determined. The fluctuation of the whole weighing flow data can be determined by determining the maximum value and the minimum value in the current binding weighing data, the last binding weighing data and the unbinding weighing data, comparing the maximum value with the minimum value, comparing the maximum value with zero and comparing the minimum value with zero.

The following is described for a scenario of weighing by a dynamic scale or the like weighing device (i.e., a scenario of a receive weigh scan or a receive weigh scan of the center/dot of the right branch shown in fig. 8):

The weighing objects are placed on the conveyor belt and are irrelevant to whether the operation of an operator is abnormal (namely, the condition that the first operation abnormality and the second operation abnormality do not exist), so that whether the weighing equipment is abnormal or not can be judged only by the fluctuation condition of all data in the current weighing time period. If the minimum value is not zero, the weighing device is abnormal in a zeroing mode.

After the two scenes are subjected to abnormality judgment, abnormal weighing data can be marked, the marked weighing data and the weighing objects are bound again, a final waybill number scanning weighing record (namely the marked binding weighing data) is obtained, and the generated data is transmitted to a downstream system.

The downstream system can further analyze the abnormal data based on marking of the data and combining a plurality of dimensions such as an operation user corresponding to the abnormal data, a unique identification code of the weighing equipment, an operation time period, a network point/transfer center and the like. Aiming at the situation that an operator combines a PDA and a Bluetooth scale, a downstream system can obtain the conclusion that the operation abnormality and the Bluetooth scale return to zero abnormality exist; aiming at the weighing conditions of weighing equipment such as dynamic weighing and the like, a downstream system can obtain the conclusion that the weighing return to zero abnormality exists.

Illustratively, further analysis of anomaly data is performed in conjunction with multi-dimensions: for example, for a certain operation user, marking is carried out on the weighing data which are scanned and bound, so that the operation of the user can be in an irregular condition; for example, marking exists on data which is scanned and bound by a certain automatic weighing equipment of a certain website, which indicates that abnormality may exist in the zeroing of the equipment.

The scheme has the following advantages:

the weighing data of each anomaly can be marked in real time by realizing the data anomaly analysis without perception of a user;

only the application program needs to be adjusted, no extra hardware or manpower is needed, and the cost is negligible;

the state of the balance (such as abnormal back skin, weight not being 0 when no article is placed, etc.) can be additionally marked.

Example III

Fig. 9 is a schematic structural diagram of a weighing data analysis apparatus according to a third embodiment of the present invention, where the present embodiment is applicable to a case of performing anomaly analysis on weighing data, as shown in fig. 9, the specific structure of the apparatus includes:

The obtaining module 91 is configured to obtain a data set to be analyzed, where the data set to be analyzed at least includes current binding weighing data, historical binding weighing data, and unbound weighing data obtained from last binding weighing data corresponding to the current binding weighing data in the historical binding weighing data to the current binding weighing data, and the binding weighing data is weighing data of a binding weighing article;

A first determining module 92, configured to determine a first result according to the discrete situations of the historical binding weighing data and the current binding weighing data, where the first result indicates whether a first abnormal operation situation exists;

a second determining module 93, configured to determine a second result according to the current binding weighing data, the last binding weighing data, and the unbound weighing data, where the second result indicates whether a second operation abnormality and a weighing device abnormality exist;

a third determining module 94 is configured to determine whether the current binding weighing data is abnormal based on the first result and the second result.

According to the weighing data analysis device provided by the embodiment, the acquisition module is used for acquiring the data set to be analyzed, the data set to be analyzed at least comprises current binding weighing data, historical binding weighing data and unbound weighing data acquired from the last binding weighing data corresponding to the current binding weighing data in the historical binding weighing data to the current binding weighing data, and the binding weighing data is the weighing data of the binding weighing object; determining a first result through a first determining module according to the discrete condition of the historical binding weighing data and the current binding weighing data, wherein the first result indicates whether a first abnormal operation condition exists or not; determining a second result through the current binding weighing data, the last binding weighing data and the unbound weighing data by a second determining module, wherein the second result indicates whether a second operation abnormality and a weighing equipment abnormality exist or not; and determining whether the current binding weighing data is abnormal or not based on the first result and the second result through a third determining module. The first result indicating whether the first operation abnormality exists or not and the second result indicating whether the second operation abnormality and the weighing equipment abnormality exist or not can be determined through analysis of the data in the data set to be analyzed without additional hardware and labor cost, and then the abnormality analysis of the weighing data is realized based on the first result and the second result, so that the reliability of the weighing data source is improved while the cost is low.

Further, the historical binding weighing data includes a plurality of binding weighing data acquired before the current binding weighing data acquiring time, and accordingly, the first determining module 92 is specifically configured to:

Determining the discrete condition, wherein the discrete condition is determined based on standard deviations of a plurality of binding weighing data included in the historical binding weighing data and the current binding weighing data;

Determining that the first result indicates that the first operation abnormality does not exist under the condition that the standard deviation is greater than or equal to a preset parameter; otherwise, determining that the first result indicates that a first operation abnormality exists; and the first operation abnormality indicates that the weighing object corresponding to the current binding weighing data is repeatedly weighed.

Further, the second determining module 93 is specifically configured to:

Determining the maximum value and the minimum value in the current binding weighing data, the last binding weighing data and the unbound weighing data;

A second result is determined based on the maximum and the minimum.

Further, the second determining module 93 is specifically configured to:

Further, the absence of the second operation abnormality indicates that the weighing object corresponding to the last binding weighing data is taken away from the weighing device by the operation user, and the weighing object corresponding to the current binding weighing data is placed on the weighing device by the operation user after the taking away;

the weighing device anomaly indicates a zeroing anomaly of the weighing device.

Further, the obtaining module 91 is specifically configured to:

Taking the data transmitted by the intelligent scanning equipment as a data set to be analyzed;

The intelligent scanning equipment is used for acquiring weighing data transmitted by the weighing equipment; the intelligent scanning equipment is used for scanning the identifier corresponding to the weighing object and binding the weighing data of the weighing equipment with the identifier of the scanned weighing object.

Further, the device further comprises:

And the marking module is used for marking the current binding weighing data under the condition that the current binding weighing data is determined to be abnormal.

The weighing data analysis device provided by the embodiment of the invention can execute the weighing data analysis method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example IV

Fig. 10 is a schematic structural view of a weighing data analysis apparatus embodying an embodiment of the present invention. The weigh data analysis device may be a digital computer in various forms, such as a desktop computer, a workstation, a server, a blade server, a mainframe computer, and other suitable computers. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 10, the weighing data analysis apparatus 10 includes at least one processor 11, and a memory such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc. communicatively connected to the at least one processor 11, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the weighing data analysis apparatus 10 can also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

The various components in the weigh data analysis apparatus 10 are connected to an I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the weighing data analysis apparatus 10 to exchange information/data with other apparatuses via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the weigh data analysis method.

In some embodiments, the weigh data analysis method may be implemented as a computer program tangibly embodied on a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the weighing data analysis apparatus 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the weigh data analysis method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the weigh data analysis method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a weigh data analysis apparatus having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or a trackball) through which a user may provide input to the weigh data analysis apparatus. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method of weighing data analysis comprising:

2. The method of claim 1, wherein the historical binding weight data includes a plurality of binding weight data acquired prior to an acquisition time of the current binding weight data, and wherein determining the first result from the discrete instances of the historical binding weight data and the current binding weight data, respectively, includes:

3. The method of claim 1, wherein determining a second result from the current bound weight data, the last bound weight data, and the unbound weight data comprises:

A second result is determined based on the maximum and the minimum.

4. A method according to claim 3, wherein determining a second result based on the maximum and minimum values comprises:

5. The method of claim 4, wherein the step of determining the position of the first electrode is performed,

The second operation abnormality does not exist, the weighing object corresponding to the last binding weighing data is indicated to be taken away from the weighing equipment by an operation user, and the weighing object corresponding to the current binding weighing data is placed on the weighing equipment by the operation user after the weighing object is taken away;

the weighing device anomaly indicates a zeroing anomaly of the weighing device.

6. The method of claim 1, wherein acquiring the data set to be analyzed comprises:

7. The method as recited in claim 1, further comprising:

and marking the current binding weighing data under the condition that the current binding weighing data is determined to be abnormal.

8. A weighing data analysis apparatus, comprising:

9. A weighing data analysis apparatus, comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-7.