Goods fleeing prevention method for construction equipment sharing platform

Abstract

The invention relates to the field of equipment sharing management, and relates to a construction equipment sharing platform goods fleeing prevention method. According to the method, the application instruction information received by the sharing platform is compared with the signing information, and whether the power construction equipment is started or not is judged; monitoring the moving track and the function use data in real time, acquiring deviation duration when the moving track deviates, comprehensively analyzing the probability of goods fleeing according to the deviation duration, the deviation distance and the function use data, and giving out early warning and locking the equipment when the probability of goods fleeing is higher than a set goods fleeing probability threshold value; analyzing the severity of the abnormal use behavior according to the operation data; and analyzing and calculating a credit attenuation degree based on the abnormal use behavior severity and the number of goods fleeing times, determining a current credit score in combination with the basic score, and taking a corresponding strategy according to the current credit score. The method achieves the dynamic and precise recognition of the channel flow behavior, achieves the risk pre-judgment through the quantitative credit management and control, improves the equipment management efficiency and compliance, and prevents the channel flow behavior.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of equipment sharing management, and relates to a construction equipment sharing platform anti-fraud method. BACKGROUND

[0002] With the expansion of power engineering construction scale and the development of equipment sharing economy, power construction equipment sharing platforms have gradually become popular. Such platforms integrate idle equipment resources to provide leasing services for different construction enterprises, effectively improving equipment utilization and reducing construction costs. However, in the actual operation process, fraud and abnormal equipment use problems occur frequently, seriously affecting platform operation order and equipment service life. Therefore, how to effectively prevent fraud has become a problem to be solved.

[0003] However, the prior art has the following problems: 1. Although the existing technology has a GPS-based equipment positioning monitoring scheme, it can only realize boundary alarm and boundary equipment locking, and cannot comprehensively judge the fraud probability by combining equipment movement trajectory and function use data, which is easy to trigger false alarm and equipment locking due to normal behaviors such as temporary avoidance and short-distance adjustment, or miss due to fraud behavior disguise.

[0004] 2. The existing technology mainly checks the abnormal use condition through appearance inspection after the equipment is returned, which can only identify obvious damage and does not consider the cumulative effect of non-standard use, does not consider quantifying the abnormal degree based on operation logs and job data, and does not consider multi-dimensional evaluation of credit score, which leads to the fact that abnormal use behavior cannot be discovered in time, and it is difficult to link user credit to form a closed loop of control, resulting in that high-risk users violate the use of high-quality equipment resources on the platform, affecting the use experience of compliant users. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art, provide a construction equipment sharing platform anti-fraud method, realize accurate identification and control of fraud behavior, and quantify the degree of abnormal equipment use to form a linkage constraint mechanism between user credit and use behavior.

[0006] The technical solution adopted by the present application to solve its technical problems is: a construction equipment sharing platform anti-fraud method, comprising: S1, comparing the equipment geographic position and the applicant identity information in the power construction equipment start application instruction received by the sharing platform with the signed project construction area and the signed identity information respectively, to determine whether to start the power construction equipment.

[0007] S2, real-time monitoring of the movement trajectory and function use data of the power construction equipment, when the movement trajectory of the power construction equipment exceeds the activity area of the power construction equipment, and recording the deviation duration of the power construction equipment activity area.

[0008] S3, analyze the probability of the power construction equipment existing string goods according to the deviation time, the moving track and the function use data, when the probability of existing string goods is higher than the set string goods probability threshold, a warning is sent and the power construction equipment is locked.

[0009] S4, obtain the operation log in the whole rental period after the power construction equipment is returned, obtain the work data of each operation record from the operation log, and analyze the severity of abnormal use behavior according to the work data.

[0010] S5, take the historical credit score of the applicant as a basis score, calculate the current credit score in combination with the severity of abnormal use behavior and the number of string goods, and take corresponding strategies according to the current credit score.

[0011] Compared with the prior art, the present application has the following beneficial effects: (1) the present application compares the equipment geographic position and the applicant identity information in the power construction equipment starting application instruction received by the sharing platform with the signed project construction area and the signed identity information respectively, judges whether to start the power construction equipment, avoids string behavior from the source, avoids cross-region and unauthorized use, and reduces the probability of string goods occurrence from the source.

[0012] (2) the present application obtains the probability of the power construction equipment existing string goods by comprehensively calculating according to the deviation time, the deviation distance and the function use data anomaly degree, and correcting according to the moving track; when the probability of existing string goods is higher than the set string goods probability threshold, a warning is sent and the power construction equipment is locked; effectively distinguish between temporary avoidance and other normal behaviors and deliberate string behavior, and significantly improve the accuracy of string goods identification.

[0013] (3) the present application obtains the operation log in the whole rental period after the power construction equipment is returned, obtains the work data of each operation record from the operation log, obtains the frequency of frequently starting the equipment, the degree of running overtime and the number of abnormal use time periods through work data analysis, and then calculates the severity of abnormal use, which is convenient for evaluating the potential long-term performance decline or hidden fault risk and providing data support for subsequent credit control.

[0014] (4) the present application takes the historical credit score as the basis, combines the severity of abnormal use behavior and the number of string goods, calculates the current credit score through exponential decay, takes corresponding strategies according to the current credit score of the applicant, forms a closed-loop management of use behavior-credit score-control measures, effectively restricts high-risk user behavior, protects the reasonable allocation of high-quality equipment resources of the platform, and effectively reduces the probability of string goods occurrence. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings described in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without any creative effort.

[0016] Figure 1 The method flowchart of the present application.

[0017] Figure 2 The method flowchart of analyzing the probability of existence of the string goods of the power construction equipment in the present application.

[0018] Figure 3 The method flowchart of analyzing the severity of the abnormal use behavior in the present application. DETAILED DESCRIPTION

[0019] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. Note that the relative arrangement, numerical expressions, and numerical values of the components and steps set forth in these embodiments are not intended to limit the scope of the present application unless otherwise specifically stated. Also, it should be understood that the dimensions of the various parts shown in the drawings are not drawn to scale for the sake of convenience of description.

[0020] The following description of at least one example embodiment is merely illustrative in nature and is in no way intended to limit the scope of the application or its application or uses. Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but should be considered part of the specification when appropriate.

[0021] In all examples shown and discussed herein, any specific values should be interpreted as merely illustrative, and not as limiting. Thus, other examples of the example embodiments can have different values.

[0022] Referring to Figure 1 As shown, the present application provides a construction equipment sharing platform anti-stringing method, comprising: S1, according to the equipment geographic position and the application party identity information in the power construction equipment start application instruction received by the sharing platform, comparing them with the signed project construction area and the signed identity information respectively, and judging whether to start the power construction equipment.

[0023] It should be noted that the specific method of judging whether to start the power construction equipment is: according to the identification code of the power construction equipment corresponding to the start application instruction, obtaining the signed project construction area and the signed identity information in the signed file corresponding to the identification code.

[0024] According to the signed project construction area, the activity area of the power construction equipment is demarcated.

[0025] Wherein, the division of the activity area of the power construction equipment is: obtaining different historical signed documents which are the same as the signed project and similar to the signed project construction area, and extracting the moving track diagram of the power construction equipment in the entire lease period from each historical signed document.

[0026] Wherein, the determination method of whether the signed project construction area is similar is: calculating the ratio of the signed project construction area to the square of the perimeter as a shape factor, and when the signed project construction area corresponding to a certain historical signed project is the same as the signed project construction area and the difference of the shape factors is less than a set difference threshold, it is determined that the construction area is similar, wherein in the present application, the difference threshold can be set to 0.1.

[0027] Obtaining the maximum moving track range contained in each moving track diagram, and overlapping the maximum moving track range of each moving track diagram with the center point of the project construction area as the standard point to form a track range diagram covering all the maximum moving track ranges.

[0028] Drawing the minimum circumscribed polygon on the track range diagram, which is recorded as the activity area of the power construction equipment.

[0029] In a specific embodiment, first, each moving track diagram is connected by using an alpha-shape algorithm to form an irregular closed curve that fits the edge of the track, completely wrapping all the construction tracks of the project, and the closed curve is recorded as the track envelope. Then, each moving track diagram is overlapped with the center point of each project construction area as the standard point to obtain a track range diagram showing all the project track envelopes, and a minimum circumscribed polygon containing all the envelopes is drawn. The midpoint of the polygon coincides with the center point of the signed project construction area, and the range contained in the polygon is recorded as the activity area of the power construction equipment. This division method integrates historical experience data, and the final activity area not only conforms to the construction practice, but also provides more accurate and reliable spatial reference for string identification.

[0030] When the geographic location of the power construction equipment is within the designated activity area and the applicant identity information is completely consistent with the signed identity information, it is determined that the power construction equipment is allowed to start; otherwise, it is determined that the power construction equipment cannot be started.

[0031] The present application compares the equipment geographic location and the applicant identity information in the power construction equipment start application instruction received by the sharing platform with the signed project construction area and the signed identity information, respectively, to determine whether to start the power construction equipment; avoids string behavior from the source, avoids cross-regional and unauthorized use, and reduces the probability of string occurrence from the source.

[0032] S2, real-time monitoring of the moving track and function use data of the power construction equipment, when the moving track of the power construction equipment exceeds the activity area of the power construction equipment, and recording the deviation duration of exceeding the activity area of the power construction equipment.

[0033] It needs to be pointed out that the method of judging whether the moving track exceeds the activity area of the power construction equipment is that the real-time positioning point of the equipment moving track is compared with the activity area of the power construction equipment, when there is a detection point on the moving track outside the activity area of the power construction equipment, it is judged that the moving track exceeds the activity area of the power construction equipment. The analysis of the moving track and the activity area of the power construction equipment can avoid the applicant to seize the time interval of real-time monitoring of the geographic position, and eliminate the risk of missing judgment; through the trajectory analysis, the short-time border crossing, intermittent out-of-border and other disguised moving behaviors can be accurately identified, effectively avoiding the malicious user to carry out string goods by evading continuous monitoring and manufacturing compliance false appearance, and strengthening the accurate interception of stringing behavior.

[0034] The present application can judge whether the activity area of the power construction equipment deviates after starting by real-time monitoring of the moving track and function use data of the power construction equipment, when the activity area of the power construction equipment exceeds, and recording the deviation duration of exceeding the activity area of the power construction equipment, which provides a data basis for subsequent analysis of the probability of power construction equipment existing string goods.

[0035] S3, according to the deviation duration, moving track and function use data, the probability of power construction equipment existing string goods is analyzed, when the probability of string goods is higher than the set string goods probability threshold, the power construction equipment is warned and locked.

[0036] The set string goods probability threshold can be set to 0.6, that is, when the probability of string goods is higher than 0.6, the power construction equipment is warned and locked.

[0037] As shown in Figure 2 The specific way of analyzing the probability of power construction equipment existing string goods is: W1, comparing the function use data with the allowed function use data in the signed file, and calculating the function use data anomaly degree.

[0038] It needs to be pointed out that the calculation method of the function use data anomaly degree is that the current use function set in the function use data and the use characteristic value corresponding to each use function are compared with the allowed use function set of the power construction equipment in the signed file and the use characteristic value allowed range corresponding to each allowed use function.

[0039] If a use function in the current use function set is not within the allowed use function set or the use function corresponds to a use characteristic value that is outside the allowed range of use characteristic values, the use function is recorded as an abnormal use function.

[0040] The number of abnormal use functions is counted, and the ratio of the number of functions to the number of functions in the current use function set is taken as the degree of abnormality of function use data.

[0041] W2, calculate the deviation distance by moving the trajectory and the activity area of the power construction equipment, respectively standardize the deviation time and the deviation distance, and then weight the deviation time, the deviation distance, and the degree of abnormality of function use data, and record the calculation result as the initial string probability of the power construction equipment.

[0042] In one specific example, the standardization method of deviation time and deviation distance is: based on a large number of historical string case rental data of the platform, the deviation time and deviation distance in the string case are screened, and the average value of each deviation time is taken as the deviation time reference value, and the average value of the deviation distance is taken as the deviation distance reference value. These two reference values represent the extreme deviation situation that is enough to trigger high suspicion in business cognition. When the deviation time is greater than the deviation time reference value, the standardization result of the deviation time is set to 1, otherwise, the ratio of the deviation time to the deviation time reference value is taken as the standardization result of the deviation time. Similarly, when the deviation distance is greater than the deviation distance reference value, the standardization result of the deviation distance is set to 1, otherwise, the ratio of the deviation distance to the deviation distance reference value is taken as the standardization result of the deviation distance; to ensure that the value of the initial string probability calculated later is a value less than or equal to 1.

[0043] It should be noted that the weight setting of deviation time, deviation distance and degree of abnormality of function use data can be obtained by randomly obtaining part of the historical rental data in the sharing platform. First, the number of string events and normal use events is obtained; then, the information entropy of the whole is calculated by the number of string events and normal use events, and the conditional entropy corresponding to the respective values of the three features of deviation time, deviation distance and degree of abnormality of function use data is calculated, and the information gain of deviation time, deviation distance and degree of abnormality of function use data is calculated by the difference between the information entropy and the respective conditional entropy of deviation time, deviation distance and degree of abnormality of function use data. This gain reflects the contribution degree of the feature to string identification; finally, the information gain of deviation time, deviation distance and degree of abnormality of function use data is normalized and summed to 1, and the information gain of deviation time, deviation distance and degree of abnormality of function use data after normalization is taken as the respective weight, so that the weight setting is more reasonable and has basis.

[0044] It also needs to be supplemented that the specific way of calculating the deviation distance is that each detection point on the moving track is evenly divided, the shortest Euclidean geometric distance from each detection point on the moving track of the power construction equipment to the activity area of the power construction equipment is calculated, and the maximum value is selected as the deviation distance.

[0045] W3, type classification of the moving track is obtained to obtain a moving track type, the moving track type includes a continuous deviation type, a deviation and return type and a long time stay type, and the initial string probability is corrected by the moving track type to obtain the probability of the existence of the string of the power construction equipment.

[0046] In a specific embodiment, the classification method of the moving track type is that the distance between each detection point of the moving track and the activity area of the power construction equipment is obtained, which is recorded as a track distance, a track distance sequence of the track distance and time is constructed, when the track distance of a certain moving track continuously increases, the moving track is classified as a continuous deviation type, when the track distance of a certain moving track gradually decreases at a certain time, the moving track is classified as a deviation and return type, and when the track distance of a certain moving track remains unchanged, the moving track is classified as a long time stay type, and the above method can clearly distinguish the type of each moving track, and provide a condition basis for the subsequent correction of the initial string probability.

[0047] It needs to be supplemented that the specific method for correcting the initial string probability includes that when the moving track type is the deviation and return type, the shortest Euclidean distance from the geographic position of the current detection point in the moving track to the activity area of the power construction equipment is obtained, which is recorded as a correction coefficient, and the product of the initial string probability and the correction coefficient is recorded as the probability of the existence of the string of the power construction equipment.

[0048] When the moving track type is the continuous deviation type or the long time stay type, the initial string probability is taken as the probability of the existence of the string of the power construction equipment.

[0049] The present application obtains the probability of the existence of the string of the power construction equipment by comprehensively calculating according to the deviation time, the deviation distance and the function use data anomaly degree, and correcting according to the moving track, and when the probability of the existence of the string is higher than a set string probability threshold, an early warning is given and the power construction equipment is locked, so that the normal behaviors such as temporary avoidance and the intentional string behaviors are effectively distinguished, and the accuracy of the string identification is significantly improved.

[0050] S4, the operation log in the entire rental period after the power construction equipment is returned is obtained, the work data of each operation record is obtained therefrom, and the severity of the abnormal use behavior is analyzed according to the work data.

[0051] As Figure 3As shown, the specific method for analyzing the severity of the abnormal use behavior is: S41, extracting the continuous use duration of each start from the operation data, and comparing it with the normal use allowed duration range of the equipment specified in the signed document.

[0052] S42, obtaining the frequency of starting below the normal use allowed duration range of the equipment specified in the signed document, and recording it as the frequent start equipment frequency.

[0053] Among them, because when the continuous use duration is lower than the normal use allowed duration range of the equipment, it means that the equipment use duration is too short, and there may be frequent start of the equipment, which will increase the risk of equipment damage.

[0054] S43, obtaining the deviation value of each duration higher than the normal use allowed duration of the equipment specified in the signed document and the maximum value of the allowed duration range, and recording the ratio of the maximum value in the deviation value to the maximum value of the normal use allowed duration range as the running overtime degree.

[0055] Among them, the continuous use duration is too high, which may cause the temperature of the power construction equipment to rise, and the probability of equipment failure due to overload and fatigue operation will be greater; for subsequent calculation of the severity of abnormal use behavior through data basis.

[0056] S44, extracting the use period of each start from the operation data, comparing each use period with the allowed use period of the equipment specified in the signed document, and recording the number of times exceeding the allowed use period of the equipment specified in the signed document as the use period abnormal number.

[0057] S45, the frequent start equipment frequency, the running overtime degree, and the use period abnormal number are used to calculate the severity of the abnormal use behavior.

[0058] Among them, it needs to be supplemented that the calculation method of the severity of the abnormal use behavior includes: screening the historical operation logs of the same type of power construction equipment from the sharing platform, obtaining the maximum frequent start equipment frequency, and recording the ratio of the frequent start equipment frequency to the maximum frequent start equipment frequency as the frequent start abnormal degree.

[0059] The ratio of the use period abnormal number to the total start number in the entire rental period is used as the use period abnormal degree.

[0060] The frequent start abnormal degree, the running overtime degree, and the use period abnormal degree are weighted and summed to obtain the severity of the abnormal use behavior.

[0061] The calculation method can intuitively reflect the proportional relationship between the frequent start abnormal degree, the running overtime degree, the use period abnormal degree and the abnormal behavior severity, that is, the higher the frequency of the frequently started equipment, the longer the running overtime length and the more the use period abnormal times, the higher the abnormal behavior severity.

[0062] The weight setting mode of the weighted sum can be obtained by obtaining the historical operation log data of the severity of different abnormal use behaviors from the sharing platform, screening the frequency of frequent start, the running overtime degree, the use period abnormal degree and the severity of abnormal use behaviors, taking the frequency of frequent start, the running overtime degree and the use period abnormal degree as independent variables, and taking the severity of abnormal use behaviors as dependent variables, constructing a multiple linear regression model for quantitative analysis, fitting to obtain the regression coefficients corresponding to the frequency of frequent start, the running overtime degree and the use period abnormal degree respectively and the constant term, and then normalizing the regression coefficients corresponding to the frequency of frequent start, the running overtime degree and the use period abnormal degree and summing to 1, and taking the regression coefficients corresponding to the frequency of frequent start, the running overtime degree and the use period abnormal degree after normalization as the weights of the frequency of frequent start, the running overtime degree and the use period abnormal degree respectively.

[0063] The application obtains the operation log in the whole rental period after the electric power construction equipment is returned, obtains the work data of each operation record therefrom, analyzes the work data to obtain the frequency of frequent start equipment, the running overtime degree and the number of abnormal use periods, and then calculates the severity of abnormal use, so as to evaluate the potential long-term performance degradation or hidden failure risk and provide data support for subsequent credit control.

[0064] S5, taking the historical credit score of the applicant as a basic score, combining the severity of abnormal use behavior and the number of serial goods to calculate the current credit score, and taking corresponding strategies according to the current credit score.

[0065] It should be noted that the method for calculating the current credit score comprises: when the severity of abnormal use behavior is greater than the set maximum severity, taking the set maximum severity as the severity of abnormal use behavior, wherein the value of the set maximum severity is 1.

[0066] The severity of abnormal use behavior and the number of serial goods are subjected to exponential analysis to obtain a credit attenuation degree.

[0067] In a specific embodiment, the way of exponential analysis to obtain the credit attenuation degree is to bring the severity of abnormal use behavior and the number of serial goods into a credit score attenuation formula, wherein the credit score attenuation formula is as follows: .

[0068] wherein, representing the credit attenuation degree, which reflects the proportion of credit to be deducted, is a natural constant, , representing the severity of abnormal use behavior, representing the number of string goods.

[0069] In the above formula , the attenuation law with a natural number as the base number is constructed by the inverse of the sum of the severity of abnormal use behavior and the number of string goods, so that its range is [0, 1]; then by calculating the difference between it and 1, the credit attenuation degree is obtained, which is used as the basis to determine the proportion of the credit score of the applicant to be deducted, so that the higher the severity of abnormal use behavior and the more the number of string goods, the higher the credit attenuation degree.

[0070] The product of the applicant's basic score and the credit score attenuation degree is taken as the credit deduction value, and the difference between the applicant's basic score and the credit deduction value is taken as the current credit score.

[0071] In a specific embodiment, the specific method steps of taking corresponding strategies according to the current credit score include: counting the current credit score into the applicant's information file, and matching the corresponding credit score level according to the credit score grading range set by the platform. The setting of the credit score grading range can be: when the credit score is lower than 60 points, the credit score level is low quality level, when the credit score is 60-89 points, the credit score level is qualified level, and when the credit score is higher than 90 points, the credit score level is high quality level.

[0072] When renting power construction equipment in the future, when the credit score level of the applicant is high quality level, the equipment can be normally used.

[0073] When the credit score level of the applicant is qualified level, the rental deposit and the rent are increased, and the applicant is reminded to use normally, and the credit score can be increased through training examination. The increase proportion of the rental deposit and the rent can be set according to the credit score value.

[0074] When the credit score level of the applicant is low quality level, the rental qualification is suspended.

[0075] The application takes the historical credit score as the basis, combines the severity of abnormal use behavior and the number of string goods, calculates the current credit score through exponential attenuation, takes corresponding strategies according to the current credit score of the applicant, forms a closed-loop management of use behavior-credit score-control measures, effectively restricts high-risk user behavior, protects the reasonable allocation of high-quality equipment resources of the platform, and effectively reduces the probability of string goods.

[0076] The above formulas are dimensionless values, and the formulas are obtained by software simulation of a large amount of data to obtain a formula closest to the actual situation, and the preset parameters in the formula are set by the person skilled in the art according to the actual situation.

[0077] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product.

[0078] Those skilled in the art can realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized in electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0079] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0080] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0081] Finally, the above is merely preferred embodiments of the present application, and is not intended to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for preventing cross-selling of construction equipment on a shared platform, characterized in that, Includes the following steps: S1. Based on the equipment's geographical location and the applicant's identity information in the power construction equipment start-up application instruction received by the sharing platform, compare them with the signed project construction area and the signed identity information to determine whether to start the power construction equipment. S2. Real-time monitoring of the movement trajectory and function usage data of power construction equipment. When the movement trajectory of the power construction equipment exceeds the activity area of ​​the power construction equipment, the deviation time of exceeding the activity area of ​​the power construction equipment is recorded. S3. Based on the deviation time, movement trajectory and function usage data, comprehensively analyze the probability of cross-selling of power construction equipment. When the probability of cross-selling is higher than the set cross-selling probability threshold, issue an early warning and lock the power construction equipment. S4. Obtain the operation logs for the entire rental period after the power construction equipment is returned, extract the operation data for each operation record, and analyze the severity of abnormal usage behavior based on the operation data. S5. The current credit score is calculated based on the applicant's historical credit score, combined with the severity of abnormal usage behavior and the number of times of cross-selling, and corresponding strategies are adopted according to the current credit score.

2. The method for preventing cross-selling of construction equipment on a shared platform according to claim 1, characterized in that, The specific method for determining whether to activate the power construction equipment is as follows: Based on the identification code of the power construction equipment corresponding to the application instruction, obtain the construction area and signatory identity information of the signed project in the signed document corresponding to the identification code; The activity area for power construction equipment shall be delineated according to the construction area of ​​the signed project. If the geographical location of the power construction equipment is within the designated activity area and the applicant's identity information is completely consistent with the signing identity information, it is determined that the power construction equipment can be started; otherwise, it is determined that the power construction equipment cannot be started.

3. The method for preventing cross-selling of construction equipment on a shared platform according to claim 2, characterized in that, The method for dividing the activity area of ​​the power construction equipment is as follows: Obtain different historical signing documents that are the same as the signed project and have similar construction areas to the signed project, and extract the movement trajectory map of the power construction equipment throughout the entire rental period from each historical signing document; Obtain the maximum range of movement trajectory contained in each movement trajectory map, and overlap the maximum range of movement trajectory of each movement trajectory map with the center point of the project construction area as the standard point to form a trajectory range map covering all maximum ranges of movement trajectory. Draw the smallest bounding polygon on the trajectory range map and denote it as the activity area of ​​the power construction equipment.

4. The method for preventing cross-selling of construction equipment on a shared platform according to claim 1, characterized in that, The specific method for analyzing the probability of cross-selling of power construction equipment is as follows: The anomaly of the function usage data is calculated by comparing it with the function usage data allowed in the signed document. The deviation distance is calculated by comparing the movement trajectory with the activity area of ​​the power construction equipment. The deviation duration and deviation distance are standardized respectively. The deviation duration, deviation distance and functional usage data anomaly are weighted and calculated. The calculation result is recorded as the initial cross-selling probability of the power construction equipment. The movement trajectory is classified into movement trajectory types, including continuous deviation type, deviation and return type, and long-term stay type. The probability of cross-selling of power construction equipment is obtained by correcting the initial cross-selling probability based on the movement trajectory type.

5. The method for preventing cross-selling of construction equipment on a shared platform according to claim 4, characterized in that, The function uses the following method to calculate data anomaly: Compare the current set of functions in the function usage data and the usage characteristic values ​​corresponding to each function with the allowed set of functions for power construction equipment and the allowable range of usage characteristic values ​​corresponding to each allowed function in the signed document. If a function in the current set of functions is not in the set of allowed functions or the value of the function's corresponding usage characteristic exceeds the allowed range of usage characteristic values, the function is recorded as an abnormal function. The number of abnormally used functions is counted, and the ratio of this number to the number of functions currently in use is used as the abnormality degree of function usage data.

6. The method for preventing cross-selling of construction equipment on a shared platform according to claim 4, characterized in that, The specific method for calculating the deviation distance is as follows: The movement trajectory is evenly divided into detection points. The shortest Euclidean geometric distance from each detection point on the movement trajectory of the power construction equipment to the activity area of ​​the power construction equipment is calculated, and the maximum value is selected as the deviation distance.

7. The method for preventing cross-selling of construction equipment on a shared platform according to claim 4, characterized in that, The specific method for correcting the initial cross-selling probability includes: When the movement trajectory type is deviation and regression type, the shortest Euclidean distance from the geographical location of the current detection point in the movement trajectory to the activity area of ​​the power construction equipment is obtained, and the ratio of this distance to the deviation distance is recorded as the correction coefficient. The product of the initial cross-selling probability and the correction coefficient is recorded as the probability that the power construction equipment has cross-selling. When the movement trajectory type is continuous deviation or long-term stay, the initial cross-selling probability is taken as the probability that there is cross-selling of power construction equipment.

8. The method for preventing cross-selling of construction equipment on a shared platform according to claim 1, characterized in that, The specific method for analyzing the severity of abnormal usage behavior is as follows: Extract the continuous usage duration of each startup from the operation data and compare it with the range of normal usage duration of the equipment specified in the signed document; The frequency of equipment startups being conducted below the allowable normal operating time range specified in the signed documents shall be recorded as frequent equipment startups. Obtain the deviation values ​​of each duration exceeding the allowable normal use duration specified in the signed document from the maximum value of the allowable duration range. Record the ratio of the maximum value of the deviation values ​​to the maximum value of the allowable normal use duration range as the degree of operation timeout. Extract the usage period for each startup from the operation data, compare each usage period with the equipment's permitted usage period specified in the signed document, and record the number of times the equipment's permitted usage period is exceeded as the number of usage period exceptions; The severity of abnormal usage behavior is calculated by comprehensively considering the frequency of equipment startup, the degree of operation timeout, and the number of abnormal usage periods.

9. A method for preventing cross-selling of construction equipment on a shared platform according to claim 8, characterized in that, The severity of the abnormal usage behavior is calculated by including: Filter historical operation logs of similar power construction equipment from the shared platform to obtain the frequency of the most frequently started equipment. The ratio of the frequency of the most frequently started equipment to the frequency of the most frequently started equipment is recorded as the degree of frequent start anomaly. The ratio of the number of abnormal usage periods to the total number of startups during the entire rental period is used as the degree of abnormality during the usage period. The severity of abnormal usage behavior is obtained by weighting and summing the abnormality levels of frequent startup, timeout, and usage period.

10. A method for preventing cross-selling of construction equipment on a shared platform according to claim 1, characterized in that, The method for calculating the current credit score includes: When the severity of abnormal usage behavior exceeds the set maximum severity, the set maximum severity will be used as the severity of the abnormal usage behavior. Credit decay is obtained by exponentially analyzing the severity of abnormal usage behavior and the number of cross-selling incidents. The product of the applicant's base score and the credit score decay rate is used as the credit deduction value, and the difference between the applicant's base score and the credit deduction value is used as the current credit score.

Images