Equipment commonly-use evaluation method and device based on multi-source information matching

Abstract

The invention discloses an equipment common evaluation method and device based on multi-source information matching, and relates to the technical field of equipment management and application. According to the method, when a vehicle maintenance mechanism prepares support repair and rescue repair under the field condition, maintenance resources such as carried machine tools and equipment are effectively evaluated and analyzed through the commonly-used performance of the equipment, and the carrying and using effectiveness of the equipment can be maximized. The method provided by the invention can provide effective theoretical support and technical support for management and use of vehicle maintenance mechanism equipment.

Description

Technical Field

[0001] The invention discloses a method and a device for evaluating the commonality of equipment based on multi-source information matching, and relates to the technical field of equipment management and application. Background Art

[0002] When vehicle maintenance organizations carry out maintenance and production operations and perform vehicle maintenance, they cannot do without various types of vehicle maintenance equipment, such as tool boxes, vernier calipers and other repair tools and measuring tools, engine, gearbox and other large component brackets, multimeter, oscilloscope and other general testing equipment, panel testers, comprehensive testers and other special testing equipment, as well as centerline correctors, special bearing pullers and other repair equipment.

[0003] When vehicle maintenance organizations carry out vehicle maintenance and production operations, they can rely on various types of machinery and equipment within the organization to effectively complete maintenance and repair work; however, in the field, due to the lack of tools, equipment, facilities, support vehicles and equipment and consumables, the efficiency and quality of vehicle maintenance have declined. In particular, when vehicle maintenance organizations organize some personnel to complete long-term field maintenance and support tasks, the various maintenance and support resources carried by their teams are the main factors that restrict the types and quantities of maintenance and support tasks they carry out, and are the key links that limit the completion of field rescue and repair tasks and affect field support and support capabilities.

[0004] For the detachment group that carries out rescue and repair tasks in the field environment, it is necessary to focus on the various types of maintenance equipment that need to be carried during the time period of field work. Only through comprehensive and professional methods, means and technical support of maintenance equipment can the rescue and repair work be carried out effectively. The vehicle maintenance organization mainly judges the selection of equipment to be carried based on the attributes such as commonality, importance (criticality) and comprehensiveness (systematicity). For the evaluation of equipment commonality, it is currently mainly through the subjective consciousness and experience accumulation of relevant business personnel to qualitatively determine the commonality of various types of equipment. Its accuracy and credibility need to be further investigated. That is, there is currently no reasonable, feasible, scientific, reliable and practical quantitative evaluation method for equipment commonality. Therefore, in order to provide a scientific selection method and practical technical support for the allocation and use of maintenance support resources for maintenance detachments in the field environment of vehicle maintenance organizations, a method for evaluating equipment commonality based on multi-source information matching is now invented to solve the above problems. Summary of the invention

[0005] In view of the problems of the prior art, the present invention provides a method and device for evaluating the commonality of equipment based on multi-source information matching, and the technical solution adopted is:

[0006] In a first aspect, a method for evaluating the commonality of equipment based on multi-source information matching is provided, the method comprising:

[0007] S1, establishing an effective set of information sources by determining the information source data set used by the equipment based on the recorded information of the vehicle maintenance organization in the vehicle maintenance production operation;

[0008] S2, according to the information source data set, classifying the data elements into key elements and general elements, determining the content of the data elements in each category, and establishing a data element set of the equipment commonness index;

[0009] S3, based on the data evaluation index of equipment commonness, establish a data evaluation index system by classifying data validity index and data matching index;

[0010] S4, constructing a mathematical model for evaluating equipment commonality through a data evaluation index system based on the data element set.

[0011] In some implementations, in S2, the key factors include the number of devices owned, the number of times they are used annually, and the total annual usage time; the general factors include the type of equipment, the number of times they are maintained annually, the total annual maintenance time, and the volume, weight, and portability.

[0012] In some implementations, in S3, the data validity indicator includes the reliability of the information source, the accuracy of the record, and the completeness of the record;

[0013] The data matching indicators include same information determination, same information fusion and missing information fitting.

[0014] In some implementations, the data matching index includes same information determination, same information fusion and missing information fitting, specifically including:

[0015] According to the data information of different information source carriers, determining whether the same information represents the same information is performed by determining whether the same information represents the same information;

[0016] According to the same data information of different information source carriers, the same information is merged into an independent usable information;

[0017] According to each independent piece of information data and the data element set, a complete piece of available information is obtained through statistical information fitting.

[0018] In a second aspect, an embodiment of the present invention provides a device for evaluating the commonality of equipment based on multi-source information matching, the device comprising:

[0019] An effective set establishment module, used to establish an effective set of information sources by determining an information source data set used by equipment according to recorded information of a vehicle maintenance organization in a vehicle maintenance production operation;

[0020] A data element set establishment module, used to classify the data elements into key elements and general elements according to the information source data set, determine the content of the data elements in each category, and establish a data element set of the equipment commonness index;

[0021] An evaluation index system establishment module is used to establish a data evaluation index system based on the data evaluation index of equipment commonness by classifying data validity index and data matching index;

[0022] The evaluation model building module is used to construct a mathematical model for evaluating the commonality of equipment based on the data element set and through a data evaluation index system.

[0023] In some implementations, in the data element set establishment module, the key elements include the number of devices owned, the number of annual uses, and the total annual usage time; the general elements include the equipment type, the number of annual maintenance times, the total annual maintenance time, and the volume, weight, and portability.

[0024] In some implementations, in the evaluation index system establishment module, the data validity index includes the reliability of the information source, the accuracy of the record, and the completeness of the record;

[0025] The data matching indicators include same information determination, same information fusion and missing information fitting.

[0026] In some implementations, the data matching index includes same information determination, same information fusion and missing information fitting, specifically including:

[0027] An information determination unit, configured to determine whether the same information represents the same piece of information based on the data information of different information source carriers, and to perform the same information determination;

[0028] An information fusion unit is used to fuse the same data information from different information source carriers into an independent usable information;

[0029] The information fitting unit is used to obtain a complete available information through statistical information fitting according to each independent information data and the data element set.

[0030] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein when the one or more computer instructions are executed by the processor, the method described in the first aspect above is implemented.

[0031] In a fourth aspect, an embodiment of the present invention provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it is used to implement the method described in the first aspect.

[0032] One or more embodiments of the present invention can bring at least the following beneficial effects:

[0033] The method of the present invention determines the effective equipment use information source and establishes the effective set of information sources by analyzing the equipment-related record information in the vehicle maintenance organization; divides the data elements into two categories, namely key elements and general elements, and determines the specific data element content in each category to establish the equipment common use index data element set; divides the equipment common use data evaluation index into two categories, namely data validity index and data matching index, and provides the numerical calculation method of each index; comprehensively considers the functional relationship between the elements, establishes the overall equipment common use evaluation mathematical model, and obtains the common use evaluation model function formula that is intuitive, easy to understand, clear and easy to calculate.

[0034] The method of the present invention can effectively evaluate and analyze the maintenance resources such as tools and equipment carried by vehicle maintenance organizations when preparing to support repairs and emergency repairs under field conditions through the commonness of the equipment, so as to maximize the utility of carrying and using the equipment. The method provided by the present invention can provide effective theoretical and technical support for the management and use of equipment in vehicle maintenance organizations. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0036] Figure 1 is a flow chart of a method for evaluating the commonality of equipment based on multi-source information matching provided by an embodiment of the present invention;

[0037] Figure 2 It is a flow chart for establishing an equipment commonality information data evaluation index system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The technical solutions 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 only a part of the embodiments of the present invention, rather than all the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0039] Embodiment 1:

[0040] Figure 1 A flow chart of a method for evaluating the commonality of equipment based on multi-source information matching is shown. Figure 1 As shown, the device commonality evaluation method based on multi-source information matching provided in this embodiment includes:

[0041] S1, establishing an effective set of information sources by determining the information source data set used by the equipment based on the recorded information of the vehicle maintenance organization in the vehicle maintenance production operation;

[0042] S2, according to the information source data set, classifying the data elements into key elements and general elements, determining the content of the data elements in each category, and establishing a data element set of the equipment commonness index;

[0043] S3, based on the data evaluation index of equipment commonness, establish a data evaluation index system by classifying data validity index and data matching index;

[0044] S4, constructing a mathematical model for evaluating equipment commonality through a data evaluation index system based on the data element set.

[0045] When using the method of the present invention, first, according to S1, determine the equipment use information source data set, analyze the equipment-related record information in the maintenance workshop during the daily vehicle maintenance production operation of the vehicle maintenance organization, determine the effective equipment use information source, and establish the effective set of information sources;

[0046] Then, according to S2, determine the data element set of the equipment commonness index, divide the data elements into two categories, namely, key elements and general elements, based on the equipment usage information source data set, and determine the specific data element content in each category to establish the data element set of the equipment commonness index;

[0047] Next, according to S3, the data evaluation index of equipment commonness is determined. The data evaluation index of equipment commonness includes two types: data validity index and data matching index. Through further analysis, a data evaluation index system is established, and the numerical calculation method of each index is given.

[0048] Finally, according to S4, a mathematical model for equipment commonality evaluation is constructed. On the basis of processing the validity and matching of data elements, the interaction between the elements is comprehensively considered to establish an overall mathematical model for equipment commonality evaluation. This can obtain an intuitive, easy-to-understand, clear, and easy-to-calculate commonality evaluation model function formula.

[0049] Furthermore, the specific implementation process of S1 is as follows:

[0050] The daily use and management data of machinery and equipment by vehicle maintenance organizations can be reflected from many aspects of records. For example, in the maintenance and production workshop, the "Equipment Use and Maintenance Registration Book" stored in the dispatch room or maintenance station is the main source of equipment use and management data. Regardless of the size of the equipment, as long as the equipment is used, it is necessary to record it in the "Equipment Use and Maintenance Registration Book" to clarify the use items, use time, use unit, user and use purpose. For large and medium-sized equipment, such as forklifts, cranes and special comprehensive testers, it is usually necessary to register and record them in the "Large and Medium-sized Equipment Record Sheet" at the equipment positioning site, so as to more accurately grasp the use and operation status of large and medium-sized equipment, so as to more effectively maintain it and keep it in good condition, so as to more accurately clarify the management responsibility and use responsibility. For some relatively advanced and highly informationized equipment, it has its own use record function after power-on, which can effectively record the use time, use function, inspection and test results, and generate analysis reports, etc., and can extract useful use information for common analysis. In addition, in order to fully understand the usage of all existing equipment in vehicle maintenance organizations and make the information data fully cover and include all equipment, the backbone experts of the vehicle maintenance organization are also required to subjectively conduct a simple, rough and general assessment of the equipment's common use to ensure the integrity and comprehensiveness of the data.

[0051] Therefore, it is determined that the effective information source for evaluating the equipment usability of vehicle maintenance organizations is the equipment use and maintenance registration book S B , Large and Medium Equipment Record Sheet S L , Equipment usage record W and subjective evaluation of key experts E , establish the overall effective set of information sources as

[0052] S={S B ,S L ,S W ,S E}

[0053] Furthermore, the specific implementation process of S2 is as follows:

[0054] Through the vehicle maintenance organization's equipment usage data information source, the equipment's usage and maintenance data information can be found, and various types of data information can be classified and summarized to obtain two types of valid data elements - key elements and general elements of equipment usage data.

[0055] The key elements of equipment usage data include the number of equipment in stock, the number of annual mobilizations, and the total annual usage time. The more equipment in stock, the more investment the vehicle maintenance organization has made in this type of equipment, that is, the higher the equipment's commonality, usefulness, and value; the more annual mobilizations of equipment, the more frequent the use of the equipment, that is, the equipment can solve many common vehicle maintenance problems and the equipment is more commonly used; the longer the total annual usage time of the equipment, the more dependent the vehicle maintenance and repair is on the equipment, that is, the equipment can complete the main and critical repair and maintenance tasks in the vehicle maintenance and repair process, which is also an important manifestation of the commonality of the equipment.

[0056] The general elements of equipment usage data include equipment type, annual maintenance times, annual total maintenance time, volume, weight and portability. Different equipment types directly affect the common characteristics of the equipment to a certain extent. For example, tool and measuring equipment are usually used more times and more frequently than comprehensive testing equipment. The more annual maintenance times and the longer the annual total maintenance time of the equipment, the higher the maintenance cost invested by the vehicle maintenance organization on the equipment, that is, the greater the role and contribution of the equipment to the maintenance and production operations of the vehicle maintenance organization, which means that the equipment may be more commonly used. The volume, weight and portability of the equipment refers to whether the equipment is easy to obtain, whether it is easy to transfer from the storage location to the maintenance station, whether it is easy to be transported to the field mission area by the vehicle, and whether it is easy to deploy and withdraw. That is, the higher the volume, weight and portability of the equipment, the more commonly used it may be.

[0057] Therefore, the data elements for evaluating the common use of equipment in vehicle maintenance institutions are determined to have key elements E K and general element E A , among which the key element E K The number of equipment owned is E M 、Annual mobilization times E N and the total annual usage time E T , general element E A With device type E Y 、Annual maintenance times E S 、Annual total maintenance time E U and volume, weight and portability C The data element set of equipment commonness index is established as follows:

[0058] E={E K ,E A}

[0059] E K ={E M ,E N ,E T}, E A ={E Y ,E S ,E U ,E C}

[0060] Furthermore, the specific implementation process of S3 is as follows:

[0061] like Figure 2 As shown, an equipment common information data evaluation index system is established. In this embodiment, the index system mainly considers two categories and six items of data validity index and data matching index;

[0062] First, according to the data validity analysis in S31, in vehicle maintenance organizations, most of the records of equipment use management are still manually recorded by operators or managers. Whether it is paper data or electronic system data, data validity issues need to be considered. Data validity evaluation mainly includes the credibility of the information source, the accuracy of the record, and the completeness of the record.

[0063] Among them, S311, the reliability of information sources includes:

[0064] The credibility of the information source refers to whether the source of various equipment use management data is reliable, that is, the credibility of the corresponding information source, which mainly depends on the daily use and management of the equipment by the vehicle maintenance organization, the quality and efficiency of the equipment management, the commonness of the equipment use record method, and the professionalism and responsibility of the equipment management personnel. Assuming that there is a certain equipment record data x, when the quality and efficiency of the equipment management of the vehicle maintenance organization A is determined M 、Commonality of equipment use recording methodsζ R and the professionalism of equipment management personnel CS and degree of responsibility CD , then the device record data considering the credibility of the information source is

[0065] x| X =λ X x=A M ζ R (ξ CS +ξ CD )x

[0066] Right now

[0067] λ X =AM ζ R (ξ CS +ξ CD )

[0068] Among them, S312, the accuracy of records includes:

[0069] The accuracy of records refers to the accuracy of each record and the authenticity of the data in each equipment use management data information source, that is, the accuracy and authenticity of the data reflecting the equipment use management situation. This mainly depends on the quality and efficiency of vehicle maintenance organization information record data management, the timeliness of data records, and the professionalism and responsibility of the recorder. Assuming that there is a certain equipment record data x, when the quality and efficiency of vehicle maintenance organization information record data management A is determined I , Timeliness of data records T and the professionalism of the recorder RS and degree of responsibility RD , then the device recording data considering recording accuracy is

[0070] x| A =λ A x=A I ζ T (ξ RS +ξ RD )x

[0071] Right now

[0072] λ A =A I ζ T (ξ RS +ξ RD )

[0073] Among them, S313, the integrity of the record includes:

[0074] The integrity of the record refers to the comprehensiveness and details of each record data in each device usage management data information source, that is, whether the data content can fully represent all items in the data set. The integrity of the data record shows the contribution of the record to the data validity, that is, the more complete the data record, the more effective the common calculation evaluation can be. The integrity of the record is mainly determined by two factors: the comprehensiveness of the data in the information source and the comprehensiveness of the data in the total information source. The comprehensiveness of the data in the information source is determined by the comprehensiveness of the data in the information source η TS It means the ratio of the number of data items of this information to the total number of data items of this information source.

[0075]

[0076] The comprehensiveness of data in the total information source is determined by the data comprehensiveness η in the total information source.AS It is the ratio of the number of this information data item to the total number of all information source data items.

[0077]

[0078] Assume that a device records data x. When the data comprehensiveness η of the information source is determined TS and the comprehensiveness of data in the total information source η AS , then the device record data considering record integrity is

[0079] x| O =λ O x=η TS η AS x

[0080] Right now

[0081] λ O =η TS η AS

[0082] Therefore, the data correction coefficient considering the validity of data information can be obtained as

[0083] λ E =λ X λ A λ O =A M ζ R (ξ CS +ξ CD )·A I ζ T (ξ RS +ξ RD )·η TS η AS

[0084] Then, according to S32, data matching analysis is performed, and the data matching indicators include same information determination, same information fusion and missing information fitting. In addition to considering the validity of data information, it is also necessary to consider the data matching and fusion problem between multi-source information. For data information on different information source carriers, S32 specifically includes:

[0085] S321 first needs to perform the same information determination to determine whether it represents the same piece of information, that is, whether it is the same information recorded on different information sources;

[0086] S322 then needs to perform the same information fusion, and the same data information on different information source carriers needs to be fused into an independent usable information;

[0087] S323, finally, it is necessary to fit the missing information, compare each independent information data with the equipment commonality indicator data element set, and obtain a complete and available information through statistical information fitting.

[0088] Wherein, the same information is determined according to S321:

[0089] Identical information determination refers to comparing various pieces of data information on different information source carriers, and determining whether each piece of data information represents the same event, that is, whether it represents the same data information, through the similarity determination of the selected data reference items. This determination and identification process is the identical information determination. The processing method for identical information determination is mainly to analyze and evaluate the similarity of the primary matching items, secondary matching items, and auxiliary matching items. The primary matching items are two items: device name and device usage time. The secondary matching items are two items: device number and user. The auxiliary matching items are two items: number of device uses and device usage time.

[0090] Considering the validity of multi-source information data records, when the data information of the main matching item is comprehensive and complete, the data matching degree analysis can be performed directly through the two items of the main matching item; when the data information of the main matching item is incomplete, the data matching degree analysis needs to be performed through the two items of the secondary matching item; if the data information of the secondary matching item is also incomplete, the auxiliary matching item needs to be used to complete the information matching degree evaluation. The analysis and evaluation calculation process is as follows:

[0091] When the main matching item data information is complete, that is, γ M =2, data matching The matching degree of the main matching item data Then there is

[0092]

[0093] When the main matching item data information is incomplete, incomplete, or one item is missing, that is, γ M =1, and the data information of the secondary matching item is comprehensive and complete, that is, γ N =2, data matching The matching degree of the main matching item data The degree of match with the sub-match data The results of the joint assessment are

[0094]

[0095] When the primary matching item data and the secondary matching item data information are incomplete, incomplete, or one item is missing, that is, γ M =1 and γ N =1, and the auxiliary matching item data information is comprehensive and complete, that is, γ L =2, data matching The matching degree of the primary matching item data and the matching degree of the secondary matching item data The matching degree with auxiliary matching item data The results of the joint assessment are

[0096]

[0097] When the main matching item has no data information, that is, γ M =0, it means that this information is invalid data information, and it is impossible to conduct matching evaluation and equipment usability evaluation, so this data needs to be removed.

[0098] When the main matching item data information is incomplete, incomplete, or one item is missing, that is, γ M =1, and when the secondary match has no data information, that is, γ N =0, it means that this information is invalid data information, and it is impossible to conduct matching evaluation and equipment usability evaluation, so this data needs to be removed.

[0099] When the primary matching item data and the secondary matching item data information are incomplete, incomplete, or one item is missing, that is, γ M =1 and γ N = 1, and the auxiliary matching item data is incomplete or has no data information, that is, γ L ≠2, it means that this information is invalid data information, and it is impossible to conduct matching evaluation and equipment usability evaluation, so this data needs to be removed.

[0100] Therefore, the data matching degree considering data fusion between multi-source information can be obtained as

[0101]

[0102] Among them, the same information fusion is performed according to S322:

[0103] For data that are determined to be the same information through the same information judgment, it is necessary to fuse the data in different information source carriers to obtain a comprehensive, relatively accurate and effective data information. There are three main processing methods for multi-source information matching and fusion: full fusion method, main fusion method and reliable method.

[0104] The full fusion method refers to the fusion of all data items determined to be the same information, on the basis of considering the validity of the data from each information source, all data are equally and without priority difference, and the final information data is obtained through unweighted average calculation. Assuming that there is a certain device recording data x, which is determined to be the same information data in N information sources, after considering the data correction of the validity of the data information, the final information data calculated by the full fusion method is

[0105]

[0106] The main fusion method refers to the consideration of the recording characteristics and usage characteristics of different information sources on the basis of the full fusion method, focusing on the commonly used and main information sources, and appropriately considering the role of other information source data, that is, all data are considered to be primary and secondary characteristics, with priority differences, and the final information data is obtained through weighted average calculation. Assuming that there is a certain device recording data x, which is determined to be the same information data in N information sources, after considering the data correction of the data information validity, the weight coefficient λ of each information source data is considered S , the final information data calculated by the main fusion method is

[0107]

[0108] The credibility method refers to considering the credibility indicators such as data validity, data source contribution, data reliability and data timeliness of different information sources, and determining the most credible information source for the data through comprehensive evaluation and calculation, and taking the value of the data in this information source as the final information data. Assuming that a certain device records data x, it is determined to be the same information data in N information sources. After data correction considering the validity of the data information, after the information source data validity λ E , data source contribution δ U , data reliabilityλ L and data timeliness λ R Comprehensive evaluation, using the credibility method to calculate the final information data is

[0109]

[0110] Among them, the missing information is fitted according to S323:

[0111] Missing information fitting means that in the data recorded in each item of the equipment information source, after the same information judgment, the same information fusion and the data information validity correction, under the condition that the equipment name information in each item of data is known, there is one or more missing key elements or general elements of equipment usage data such as the number of equipment owned, annual usage times, volume, weight and portability. Through certain data fitting methods, the data of various elements can be effectively supplemented and improved to make the data recorded in each item comprehensive and complete.

[0112] For situations with fewer data records, the total data is used as the basis for uniformly considering the fitting of missing information. For situations with more data records, the device type is used as the basis for distinguishing different types of equipment, and the fitting of missing information is considered separately to improve the data information.

[0113] Here, it is assumed that the number of equipment in stock, the number of annual activations, and the total annual usage time are simplified to be linear relationships between each other, and the ratio of the total annual maintenance time to the equipment type maintenance time is linearly related to the annual maintenance times; the equipment type can be derived from the equipment name information, and it is assumed that the volume, weight, and portability are functional relationships related to the equipment type. Therefore, the missing information can be fitted through the above two types of data relationships to obtain complete data information.

[0114] Assume that the device type is

[0115] E Y ∈{E Y,GJ ,E Y,ZJ ,E Y,TJ ,E Y,ZJ ,E Y,XL}

[0116] The amount of equipment type maintenance hours associated with the equipment type can be determined

[0117] T Y ∈{T Y,GJ ,T Y,ZJ ,T Y,TJ ,T Y,ZJ ,T Y,XL}

[0118] After the data information processing before fitting is completed, the linear relationship between the data is determined by the least squares method, and the linear slope and intercept are obtained as

[0119]

[0120] The curve fitted Fitting calculates missing data information.

[0121] The volume weight portability can be obtained by averaging the volume weight portability of each type of equipment or all equipment. The portability can be obtained by the correlation function between volume weight portability and equipment type or total equipment:

[0122] E C ∈{E C,GJ ,E C,ZJ ,E C,TJ ,E C,ZJ ,E C,XL}

[0123] Next, according to S4, a mathematical model for evaluating the common use of equipment is constructed through a data evaluation index system based on the data element set.

[0124] After S3 corrects the validity and matching of the common use evaluation data element information established by S2, reliable, scientific and comprehensive data information is obtained, based on which a vehicle maintenance organization equipment common use evaluation model can be established to complete the evaluation calculation of equipment common use.

[0125] In this paper, the physical analysis method is used to establish the evaluation model. For the key elements and general elements, the element category coordination coefficient is introduced, which is:

[0126] ρ K +ρ A =1,1≥ρ K ≥ρ A ≥0

[0127] The key factors are mainly the number of annual uses, and the general factors are mainly the number of annual maintenance and volume, weight and portability. The evaluation model is established as follows:

[0128]

[0129] Thus, a mathematical model for evaluating the equipment commonality of vehicle maintenance organizations based on multi-source information matching was established.

[0130] Embodiment 2:

[0131] An embodiment of the present invention provides a device for evaluating the commonality of equipment based on multi-source information matching, the device comprising:

[0132] An effective set establishment module, used to establish an effective set of information sources by determining an information source data set used by equipment according to recorded information of a vehicle maintenance organization in a vehicle maintenance production operation;

[0133] A data element set establishment module, used to classify the data elements into key elements and general elements according to the information source data set, determine the content of the data elements in each category, and establish a data element set of the equipment commonness index;

[0134] An evaluation index system establishment module is used to establish a data evaluation index system based on the data evaluation index of equipment commonness by classifying data validity index and data matching index;

[0135] The evaluation model building module is used to construct a mathematical model for evaluating the commonality of equipment based on the data element set and through a data evaluation index system.

[0136] Furthermore, in the data element set establishment module, the key elements include the number of equipment owned, the annual number of uses and the total annual usage time; the general elements include equipment type, annual maintenance times, total annual maintenance time and volume, weight and portability.

[0137] Furthermore, in the evaluation index system establishment module, the data validity index includes the reliability of the information source, the accuracy of the record and the completeness of the record;

[0138] The data matching indicators include same information determination, same information fusion and missing information fitting.

[0139] Furthermore, the data matching index includes same information determination, same information fusion and missing information fitting, specifically including:

[0140] An information determination unit, configured to determine whether the same information represents the same piece of information based on the data information of different information source carriers, and to perform the same information determination;

[0141] An information fusion unit is used to fuse the same data information from different information source carriers into an independent usable information;

[0142] The information fitting unit is used to obtain a complete available information through statistical information fitting according to each independent information data and the data element set.

[0143] Embodiment three:

[0144] This embodiment further provides an electronic device, including a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions implement the method of embodiment 1 when executed by the processor;

[0145] In practical applications, the processor can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller unit (MCU), a microprocessor or other electronic components to execute the methods in the above embodiments.

[0146] The method implemented in this embodiment is as shown in the content of the first embodiment.

[0147] Embodiment 4:

[0148] This embodiment further provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by one or more processors, the method of the first embodiment is implemented;

[0149] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0150] The method implemented in this embodiment is as shown in the content of the first embodiment.

[0151] Comparative Example 1:

[0152] According to the method described in Example 1, the following comparative test was carried out today.

[0153] S1, consider a typical vehicle maintenance organization, assuming that its equipment use records in a certain year are relatively complete and comprehensive, and its equipment use management is relatively formal and orderly. It is determined that its equipment record information sources include the "Equipment Use and Maintenance Registration Book" and the equipment's own use records. Two valid information sources, then the overall information source valid set for the year is established as

[0154] S={S B ,S W}

[0155] There is some equipment usage information in the sorted "Equipment Use and Maintenance Registration Book", such as Table 1 is the equipment usage information table in the "Equipment Use and Maintenance Registration Book":

[0156] Table 1

[0157]

[0158] Some of the equipment usage information in the equipment self-usage record after sorting is shown in Table 2. Table 2 is the equipment usage information table in the equipment self-usage record;

[0159] Table 2

[0160]

[0161]

[0162] S2, considering the characteristics of equipment use in vehicle maintenance institutions, among the two types of valid data elements, key elements and general elements of equipment use data, equipment maintenance is mainly carried out by specialized maintenance and repair departments, and usually only annual maintenance frequency data is available, while annual maintenance duration data records are not accurate enough to reflect the characteristics of equipment use, so this indicator element is excluded. Therefore, the data elements for evaluating the common use of equipment in vehicle maintenance institutions include key element E K and general element E A , among which the key element E K The number of equipment owned is E M 、Annual mobilization times E N and the total annual usage time E T , general element E A With device type E Y 、Annual maintenance times E S and volume, weight and portability C The equipment commonness index data element set is established as follows:

[0163] E={E K ,E A}

[0164] E K ={E M ,E N ,E T}, E A ={E Y ,E S ,E C}

[0165] By counting the data from the two information sources, we get a data element summary table as shown in Table 3. Table 3 is a summary table of equipment Shu Yong information data elements. The data in the table default to the data in the information source "Equipment Use and Maintenance Registration Book" (S1), and the data in the equipment's own use record (S2) needs to be specially marked.

[0166] Table 3

[0167]

[0168] S3, S31 data validity analysis

[0169] The credibility of the information source can be verified by the quality and efficiency of the vehicle maintenance organization's equipment management. M 、Commonality of equipment use recording methodsζ R and the professionalism of equipment management personnel CSand degree of responsibility CD To evaluate, the credibility coefficient of the information source obtained by evaluation is λ X The data information can be modified. For example, for the 6 equipment usage information in Table 3, the credibility evaluation of the information source for the data factor of "annual usage times" is shown in Table 4, which is a credibility evaluation table for data information sources:

[0170] Table 4

[0171]

[0172] The accuracy of records can be improved through the vehicle maintenance organization information record data management quality and efficiency A I , Timeliness of data records T and the professionalism of the recorder RS and degree of responsibility RD To evaluate, the record accuracy coefficient λ obtained by evaluation A The data information can be corrected. For example, for the 6 equipment usage information in Table 3, the record accuracy evaluation of the "annual usage times" data factor is shown in Table 5, which is a data record accuracy evaluation table:

[0173] Table 5

[0174]

[0175]

[0176] The integrity of the records can be determined by the vehicle maintenance organization information recorded in the information source. TS and the comprehensiveness of data in the total information source η AS To evaluate, the record integrity coefficient λ obtained by evaluation O The data information can be modified. For example, for the 6 equipment usage information in Table 3, the record integrity assessment of the "annual usage times" data factor is shown in Table 6, which is a data integrity assessment table:

[0177] Table 6

[0178]

[0179] (2) Data matching analysis

[0180] For the determination of identical information, based on the data information recorded by various information sources in the use of equipment by vehicle maintenance organizations, and taking into account the validity and unique certainty of data matching, it can be determined that the main matching items are two items: equipment name and equipment usage time; the secondary matching items are two items: equipment usage quantity and user; and the auxiliary matching items are two items: equipment number and equipment usage time.

[0181] Consider the equipment usage record information given in Table 7. Table 7 is the equipment record information table for investigation:

[0182] Table 7

[0183]

[0184] For the five record data in Table 7, the main matching item data of information No. 1, 2, 4, and 5 are complete, and the matching degree of the main matching item data can be examined.

[0185] The matching degree of the main matching item data of information No. 1 and No. 2 is

[0186] 1. The matching degree of the main matching item data of information No. 4 is

[0187] 1. The matching degree of the main matching item data of information No. 5 is

[0188] The matching degree of the main matching item data of information No. 2 and No. 4 is

[0189] 2. The matching degree of the main matching item data of information No. 5 is

[0190] The matching degree of the main matching item data of information No. 4 and No. 5 is

[0191] From the matching degree of the main matching item data, it can be seen that information No. 1 and No. 4 can be determined to be the same piece of data information, and information No. 2 and No. 5 can be determined to be the same piece of data information.

[0192] For record information No. 3, one of its main matching information is missing - the device usage time information is incomplete, so we can continue to use the secondary matching item data information to analyze its matching. From Table 7, we can see that the secondary matching item data information of record information No. 3 is complete, and the secondary matching item data information of record information No. 1 and No. 2 is complete, so we can examine the matching degree of its secondary matching item data.

[0193] 1. The matching degree of the data of the second matching item of information No. 3 is

[0194]

[0195] The matching degree of the data of the second matching item of information No. 2 and No. 3 is

[0196]

[0197] From the matching degree of the secondary matching item data, it can be seen that No. 3 is different from No. 1 and No. 2.

[0198] The secondary matching item data of record No. 3 is complete, but the secondary matching item data of record No. 4 and No. 5 are missing one item - the user of the device, so the matching can be analyzed by using the auxiliary matching item data. As can be seen from Table 7, the auxiliary matching item data of record No. 3 is also missing one item - the length of time the device is used, so the matching between record No. 3 and record No. 4 and record No. 5 cannot be determined.

[0199] It is thus determined that information No. 1 and No. 4 can be determined to be the same piece of data information, information No. 2 and No. 5 can be determined to be the same piece of data information, and information No. 3 is a separate piece of data information.

[0200] After the above identical information determination, the data determined to be the same information needs to be fused with different information records to obtain a comprehensive, relatively accurate and effective data information.

[0201] For the fusion of the same information, the main fusion method is used to fuse all data with primary and secondary characteristics and priority differences, and the final information data is obtained through weighted average calculation. For the data information No. 1, 2, 4, and 5 in Table 7, the corresponding equipment usage information data elements are obtained after sorting as shown in Table 8. Table 8 is the data element table of "No. 1, 2, 4, and 5":

[0202] Table 8

[0203] Serial number Device Name Number of times used Duration of use Device Type Maintenance times Volume, weight, portability 1 Multimeter W4ZD-02 1 1h General testing equipment 1 5 2 Multimeter W4ZD-03 1 20m General testing equipment 0 4 4 Multimeter W4ZD-02 1 1h General testing equipment 0 5 5 Multimeter W4ZD-03 1 20m General testing equipment 0 5

[0204] For the above data No. 1, 2, 4, and 5, data validity corrections such as data information source credibility, data record accuracy, and data record integrity are required, and their respective correction coefficients are:

[0205] λ E(1) =0.75,λ E(2) =0.83,λ E(4) =0.95,λ E(5) =0.95;

[0206] At the same time, the weight coefficient of the main fusion information is taken as λ S(S2) =1.5, the weighting coefficient of the secondary fusion information is λ S(S1) =0.5, the final information data calculated by the main fusion method is

[0207] The maintenance times of information records 1 and 4 are (0.75×0.5×1+0.95×1.5×0) / 2=0.19≈0

[0208] The volume weight portability of information record 2 and 5 is (0.83×0.5×4+0.95×1.5×5) / 2=4.39≈4

[0209] Thus, the recorded data elements after multi-source information fusion can be obtained as shown in Table 9, which is a table of recorded data elements after information fusion:

[0210] Table 9

[0211]

[0212]

[0213] For missing information fitting, the same type of equipment information fitting is taken as an example to illustrate the effectiveness of the missing information fitting method. The data information sorted and summarized after data validity correction, same information determination and same information fusion is shown in Table 10. Table 10 is a table of recorded data elements sorted and summarized after information fusion containing missing information:

[0214] Table 10

[0215]

[0216] By fitting each linear relationship using the least squares method, we get:

[0217] The fitting data of the annual usage frequency in the No. 2 information record is 29 times, and the fitting data of the annual total usage time is 21 hours;

[0218] The fitting data of annual maintenance times in information record No. 3 is 21 times;

[0219] The volumetric weight portability fitting data in information record No. 4 is 5.

[0220] S4, after data validity processing and data matching processing, the final available equipment usage record information data is obtained, and some data information is shown in Table 11. By using the common evaluation model to evaluate the data in Table 11, the common index of each recording device can be obtained, which can be used to complete the analysis and selection of equipment; Table 11 is the equipment usage record information table after processing:

[0221] Table 11

[0222]

[0223]

[0224] Assume that there is a factor category coordination coefficient ρ K =0.7,ρ A =0.3. A data table of the standard reference number of equipment, the standard reference annual total usage time, and the standard reference equipment type is established, based on which the equipment commonness evaluation values ​​of the various recorded data in Table 11 can be obtained, as shown in Table 12. Table 12 is the equipment commonness evaluation result table:

[0225] Table 12

[0226]

[0227]

[0228] From the above evaluation results, it can be seen that the common use evaluation results of various types of general tools and equipment have high scores, which are the more commonly used equipment types in vehicle maintenance organizations, which is in line with the actual situation; the engine dynamometer and the * vehicle 2-position wheel bearing are special maintenance and inspection equipment, which are only used in specific circumstances and are not used frequently, so their common use evaluation results have low scores, which is also consistent with the actual situation; from the overall situation, the common use evaluation results of various types of equipment can provide effective data basis for equipment management and use of vehicle maintenance organizations, and provide scientific and feasible decision-making basis for equipment analysis planning, procurement distribution and selection configuration.

[0229] The present invention adopts the above-mentioned equipment commonness evaluation method based on multi-source information matching, analyzes various commonly used equipment management and use record information sources of vehicle maintenance organizations, constructs an equipment commonness index data element set according to the format style, content items, etc. of the data records in each information source, performs data validity processing and data matching processing on the commonness index data elements, obtains corrected and sorted usable equipment record data, comprehensively considers the characteristics of the data elements, and establishes an overall equipment commonness evaluation mathematical model, thereby providing effective theoretical and technical support for equipment analysis planning, procurement distribution, management and use, and configuration selection of vehicle maintenance organizations.

[0230] In the several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed system and method can also be implemented in other ways. The system and method embodiments described above are merely illustrative.

[0231] It should be noted that, in this article, the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. The terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0232] Although the embodiments disclosed in the present invention are as above, the above contents are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for evaluating the commonality of equipment based on multi-source information matching, characterized in that: The method comprises: S1, establishing an effective set of information sources by determining the information source data set used by the equipment based on the recorded information of the vehicle maintenance organization in the vehicle maintenance production operation; S2, according to the information source data set, classifying the data elements into key elements and general elements, determining the content of the data elements in each category, and establishing a data element set of the equipment commonness index; S3, based on the data evaluation index of equipment commonness, establish a data evaluation index system by classifying data validity index and data matching index; S4, constructing a mathematical model for evaluating equipment commonality through a data evaluation index system based on the data element set.

2. The method according to claim 1, characterized in that In S2, the key factors include the number of equipment owned, the number of times it is used annually, and the total annual usage time; the general factors include the type of equipment, the number of times it is maintained annually, the total annual maintenance time, and the volume, weight, and portability.

3. The method according to claim 1, characterized in that In S3, the data validity indicators include the reliability of the information source, the accuracy of the records, and the completeness of the records; The data matching indicators include same information determination, same information fusion and missing information fitting.

4. The method according to claim 3, characterized in that The data matching indicators include same information determination, same information fusion and missing information fitting, specifically including: According to the data information of different information source carriers, determining whether the same information represents the same information is performed by determining whether the same information represents the same information; According to the same data information of different information source carriers, the same information is merged into an independent usable information; According to each independent piece of information data and the data element set, a complete piece of available information is obtained through statistical information fitting.

5. A device for evaluating the commonality of equipment based on multi-source information matching, characterized in that: The device comprises: An effective set establishment module, used to establish an effective set of information sources by determining an information source data set used by equipment according to recorded information of a vehicle maintenance organization in a vehicle maintenance production operation; A data element set establishment module, used to classify the data elements into key elements and general elements according to the information source data set, determine the content of the data elements in each category, and establish a data element set of the equipment commonness index; An evaluation index system establishment module is used to establish a data evaluation index system based on the data evaluation index of equipment commonness by classifying data validity index and data matching index; The evaluation model building module is used to construct a mathematical model for evaluating the commonality of equipment based on the data element set and through a data evaluation index system.

6. The device according to claim 5, characterized in that In the data element set establishment module, the key elements include the number of equipment owned, the number of annual uses and the total annual usage time; the general elements include equipment type, annual maintenance times, total annual maintenance time and volume, weight and portability.

7. The device according to claim 5, characterized in that In the evaluation index system establishment module, the data validity index includes the reliability of the information source, the accuracy of the record and the completeness of the record; The data matching indicators include same information determination, same information fusion and missing information fitting.

8. The device according to claim 7, characterized in that The data matching indicators include same information determination, same information fusion and missing information fitting, specifically including: An information determination unit, configured to determine whether the same information represents the same piece of information based on the data information of different information source carriers, and to perform the same information determination; An information fusion unit is used to fuse the same data information from different information source carriers into an independent usable information; The information fitting unit is used to obtain a complete available information through statistical information fitting according to each independent information data and the data element set.

9. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the method as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it is used to implement the method as described in any one of claims 1 to 4 above.

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