Integral incentive method based on intelligent garbage collection

Abstract

The invention discloses an integral excitation method based on intelligent garbage collection, and belongs to the technical field of garbage classification and collection. The invention discloses an integral incentive method based on intelligent garbage collection. The method comprises the following steps of performing information verification on a user and judging a credit parameter of the user; monitoring and recording delivery behaviors of the user to obtain parameters of the delivery behaviors of the user; performing mass analysis and calculation on the garbage delivered by the user in the intelligent garbage recycling system to obtain an initial point obtained by the user; calculating the initial credits in combination with credit parameters of the user and delivery behaviors of the user to obtain final credits of the user; and after delivery is finished, the user checks delivery details and redeems credits.

Description

Technical Field

[0001] The present invention belongs to the technical field of garbage classification and recycling, and more specifically, relates to a point incentive method based on intelligent garbage recycling. Background Art

[0002] With the acceleration of urbanization, the amount of garbage generated continues to increase, and garbage disposal has become an urgent problem to be solved. Traditional garbage recycling methods mainly rely on manual recycling, which is inefficient and prone to recycling different types of garbage together. The use of intelligent garbage recycling equipment can improve the efficiency and accuracy of garbage recycling.

[0003] Existing intelligent garbage recycling equipment can monitor the garbage dumping situation in real time 24 hours a day, effectively classify various types of garbage, effectively reduce the cost of garbage disposal, and at the same time, users can get points rewards to exchange for daily necessities when they put out garbage, encouraging users to actively participate in garbage sorting; however, the existing intelligent garbage sorting and processing system is not accurate enough in its incentives for users. Relying solely on the intelligence of the equipment cannot completely solve the problem of garbage recycling. It is also necessary to encourage users to actively participate in garbage recycling and correctly dispose of garbage. At present, although there are some garbage recycling incentives, most of them have problems such as unclear incentive effects and low user participation; taking an intelligent garbage sorting and recycling method and system (CN202010599285.0) disclosed in a Chinese invention patent document as an example, the invention evaluates the depreciation rate of old items, and obtains the garbage recycling value evaluation result by weighing and calculating, and calculates the corresponding points value or cash value according to the evaluation result; however, the scheme only obtains the corresponding points value based on the quality of the garbage itself, and cannot evaluate the user's delivery behavior and credit. The distinction between inaccurate delivery and accurate delivery is not clear enough, which has dampened the enthusiasm of some users who deliver accurately.

[0004] Therefore, we need an intelligent garbage collection method with a more reasonable point incentive mechanism, which can evaluate user credit and delivery behavior, thereby improving users' awareness of correct classification and delivery. Summary of the invention

[0005] The purpose of the present invention is to provide a point incentive method based on intelligent garbage recycling. The point incentive mechanism is more reasonable. Points are added based on the user's own credit evaluation. The point reward gradient is set according to the accuracy of delivery behavior and pre-classification. A dynamic point pricing mechanism is constructed according to garbage supply and demand factors. The point incentive method is set in combination with users, behaviors and terminals covering various stages of the garbage recycling process, so as to enhance user participation and enthusiasm, improve garbage recycling efficiency and quality, optimize resource allocation and utilization, and enhance environmental awareness.

[0006] To achieve the above object, the present invention provides the following technical solutions: A point incentive method based on intelligent garbage collection of the present invention comprises the following steps: S1. Verify user information and judge the user's credit; S2. Monitor and record the user's delivery behavior and obtain the parameters of the user's delivery behavior; S3. Calculate the initial points obtained by the user by analyzing the quality of the garbage delivered by the user in the intelligent garbage collection; S4. Calculate the initial points based on the user's credit and the user's delivery behavior to obtain the user's final points; S5. After delivery is completed, the user can check the delivery details and redeem points.

[0007] As a further improvement of the present invention, the credit of the user described in step S1 is a reflection of the feedback of the historical delivery situation recorded by the user within a period of time, and depends on the accuracy of the user's previous deliveries. The closer the delivery is to the most recent time, the greater the reference weight; the credit parameter is a quantitative indicator of the accuracy of the comprehensive user's time-series delivery behavior, and is used to evaluate the user's credit.

[0008] As a further improvement of the present invention, the calculation formula of the credit parameter is: Credit parameter CP ; where P i Indicates the accuracy of the i-th delivery before this delivery, ranging from 0 to 1; T i It represents the weight of the i-th delivery before the current delivery, ranging from 0.4 to 1, and decreases as i increases.

[0009] As a further improvement of the present invention, the monitoring of the user's delivery behavior in step S2 includes monitoring the user's delivery accuracy and the user's garbage pre-classification behavior, the delivery accuracy is the proportion of the number of garbage correctly classified and placed in the corresponding category delivery port to the total number of garbage delivered; the user's garbage classification delivery qualification rating is divided into excellent, good, medium and poor according to the delivery accuracy, and different qualification ratings obtain different point multiples N; the excellent rating corresponds to a delivery accuracy of 95%-100%, and obtains a 1.1-1.2 point multiple; the good rating corresponds to a delivery accuracy of 80%-95%, and obtains a 1.0-1.1 point multiple; the medium rating corresponds to a delivery accuracy of 60%-80%, and obtains a 0.9-1.0 point multiple; the poor rating delivery accuracy is 59% and below, and obtains a 0.8-0.9 point multiple; the user's garbage pre-classification behavior refers to the user's behavior of preliminarily classifying the garbage before delivering it to the intelligent garbage recycling equipment, which is used to improve the overall garbage recycling efficiency.

[0010] As a further improvement of the present invention, the user's garbage pre-classification behavior is quantified by a pre-classification index, and the calculation formula of the pre-classification index is: CPI=a×PL+b×PA, wherein a represents the pre-classification readiness weight; PL represents the pre-classification readiness, representing the user's readiness to classify the garbage before delivering the garbage, ranging from 0 to 1; b represents the weight of the pre-classification accuracy; PA represents the user's pre-classification accuracy, ranging from 0 to 1.

[0011] As a further improvement of the present invention, the initial points obtained by the user in step S3 are the weight of the garbage delivered by the user multiplied by the point pricing of the garbage; the weight of the delivered garbage is sensed by the weight sensor inside the smart garbage recycling box, and the point pricing of the garbage is determined by the garbage recycling demand, the value of the garbage itself and the difficulty of garbage disposal.

[0012] As a further improvement of the present invention, the calculation formula of the garbage point pricing is: garbage point pricing A= ; Where k represents the adjustment coefficient, a constant determined according to local actual conditions and policy factors, used to fine-tune the overall pricing, with a value range of 0.5-2. If the local area hopes to strongly encourage the recycling of a certain type of garbage, the value will be appropriately increased. Conversely, if the recycling pressure of a certain type of garbage is relatively high, the value will be lowered; D is the garbage recycling demand factor, which reflects the market or garbage disposal system's demand for a specific type of garbage. It is determined by analyzing the processing capacity of local garbage disposal plants or the demand factors of resource recycling companies. The value range is 0-10, and it increases with the increase in demand; S is the garbage supply factor, which reflects the amount of garbage put into the market in a certain period of time. It is related to the living habits of local residents and seasonal factors. The value range is 0-10, and it increases with the increase in the amount put into the market; V is the value factor of the garbage itself, which is determined according to the material composition and recyclable value of the garbage. The value range can be set to 0-1 and increases with the increase in value.

[0013] As a further improvement of the present invention, step S4 calculates the product of the user's credit parameters, the user's delivery accuracy and the pre-classification index. If it is lower than the set delivery limit value L, the points obtained by the user for this delivery need to be frozen, and the L value is 0.5; if it is higher than the set delivery compensation value H, the user's previously frozen points need to be unfrozen, and the H value is 0.8; the unfrozen points value is adjusted based on the points value obtained after the delivery reaches the delivery compensation value H for this delivery.

[0014] As a further improvement of the present invention, the points finally obtained by the user in step S4 are obtained based on the point incentive method for garbage recycling based on the user, behavior and terminal coverage of each stage of the garbage recycling process, and the calculation formula is: the user obtains the final points W=m×A×CPI×N×CP; wherein m represents the weight of the garbage; A represents the point pricing of the garbage; CPI represents the user's pre-classification index, ranging from 0-1; N represents the delivery accuracy corresponding to the point multiplier, ranging from 0.8-1.2; CP represents the user's credit parameter, ranging from 0-1.

[0015] As a further improvement of the present invention, the delivery details described in step S5 include the five-level administration and site name of the delivery point, the type of garbage delivered, the time of garbage delivery, the amount of garbage delivered, the points generated by delivery, the delivery behavior evaluation and the frozen points status information, so that users can understand their delivery situation and the basis for obtaining points, enhance the transparency of the entire incentive mechanism and the user's right to know, and improve the user's trust in the system.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: the point incentive method based on intelligent garbage recycling involved in the present invention can collect the user's own credit and the user's delivery behavior and calculate the user's final points according to the current garbage point pricing, and obtain the user's credit parameter through the user's previous delivery accuracy, so as to adjust the user's final point value by the size of the credit parameter, so as to achieve the purpose of incentivizing users to maintain a high delivery accuracy rate, and to change the final point value by monitoring the user's current delivery accuracy rate to encourage users to classify garbage more accurately and improve the quality of garbage recycling, and to motivate users to preliminarily classify garbage before delivery by monitoring the user's garbage pre-classification behavior and quantifying it through the pre-classification index, so as to improve the overall garbage recycling efficiency and reduce the manual secondary classification cost and pre-classification behavior in the garbage recycling process. , and by adjusting the pricing of recycling points through the supply and demand relationship of garbage, it can balance the amount of garbage recycled, maintain the stability and sustainability of garbage recycling, encourage residents to sort garbage more effectively, and improve recycling efficiency; through parameters based on the user's own credit and the user's delivery behavior, the points obtained by users who do not meet the minimum delivery requirements are frozen, and the user's frozen points are unfrozen after exceeding the set minimum delivery requirements, which has a restraining effect on the user's delivery behavior and encourages users to continue to meet certain environmental protection behavior standards; by displaying the five-level administrative and site names of the delivery point, the type of garbage delivered, the time of garbage delivery, the amount of garbage delivered, the points generated by delivery, the delivery behavior evaluation and the frozen points status information, users can understand their delivery situation and the basis for obtaining points, which enhances the transparency of the entire incentive mechanism and the user's right to know, and improves the user's trust in the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the steps of a point incentive method based on intelligent garbage collection according to the present invention; Figure 2 It is a schematic diagram of the process of obtaining the final score of the present invention. DETAILED DESCRIPTION

[0018] Specific embodiment 1: Please refer to Figure 1-Figure 2 A point incentive method based on intelligent garbage collection, comprising the following steps: S1. Verify user information and judge the user’s credit.

[0019] Specifically, the information verification in step S1 is the verification of the user's points card, mobile phone number, facial information or WeChat account during the registration or binding process; the credit data is stored in the form of a digital type field; the credit data adopts a data access layer design pattern or framework, and encapsulates the database access operation in the data access layer through MyBatis or Hibernate. The application accesses the user credit data by calling the method of the data access layer, which can improve the maintainability and scalability of the code, and also facilitates the switching and optimization of the database.

[0020] Specifically, the credit of the user described in step S1 is a reflection of the feedback of the historical delivery situation recorded by the user over a period of time, and depends on the accuracy of the user's previous deliveries. The closer the delivery is to the most recent time, the greater the reference weight; the credit parameter is a quantitative indicator of the accuracy of the user's time-series delivery behavior, and is used to evaluate the user's credit.

[0021] Specifically, the calculation formula of the credit parameter is: Credit parameter CP ; where P i Indicates the accuracy of the i-th delivery before this delivery, ranging from 0 to 1; T i represents the weight of the i-th delivery before the current delivery, ranging from 0.4 to 1, and decreases as i increases; for example, if n=5, T 1 =1, T 2 =0.9, T 3 =0.8, T 4 =0.6, T 5 = 0.4, based on the recorded data P 1 =90%, P 2 =95%, P 3 =88%, P 4 =97%, P 5 =80%, then the credit parameter CP= 0.908.

[0022] S2. Monitor and record the user's delivery behavior and obtain the parameters of the user's delivery behavior.

[0023] Specifically, the monitoring of the user's delivery behavior in step S2 includes monitoring the user's delivery accuracy and the user's garbage pre-classification behavior, the delivery accuracy is the ratio of the number of garbage correctly classified and placed in the corresponding category delivery port to the total number of garbage delivered; the user's garbage classification delivery qualification rating is divided into excellent, good, medium and poor according to the delivery accuracy, and different qualification ratings obtain different point multiples N; the excellent rating corresponds to a delivery accuracy of 95%-100%, and obtains a 1.1-1.2 point multiple; the good rating corresponds to a delivery accuracy of 80%-95%, and obtains a 1.0-1.1 point multiple; the medium rating corresponds to a delivery accuracy of 60%-80%, and obtains a 0.9-1.0 point multiple; the poor rating delivery accuracy is 59% and below, and obtains a 0.8-0.9 point multiple; the user's garbage pre-classification behavior refers to the user's behavior of preliminarily classifying the garbage before delivering it to the intelligent garbage recycling equipment, which is used to improve the overall garbage recycling efficiency.

[0024] Specifically, the user's garbage pre-sorting behavior is quantified by the pre-sorting index. The calculation formula of the pre-sorting index is: CPI=a×PL+b×PA, where a represents the weight of pre-sorting readiness; PL represents pre-sorting readiness, which represents the user's readiness to sort garbage before delivering it, ranging from 0-1; b represents the weight of pre-sorting accuracy; PA represents the user's pre-sorting accuracy, ranging from 0-1; for example, setting a=0.4 and b=0.6 for this delivery, it means that the pre-sorting behavior focuses more on the pre-sorting accuracy, while PL=0.8 represents a moderate level of pre-sorting readiness, and PA=0.9 represents a high level of pre-sorting accuracy; then the pre-sorting index CPI=0.4×0.8+0.6×0.9=0.86.

[0025] Specifically, multiple high-definition surveillance cameras are installed inside and outside the garbage recycling bin, covering different angles and areas to ensure that the user's delivery behavior can be clearly captured; computer vision technology is used to take pictures and analyze the garbage thrown into the trash can, identify the type and appearance characteristics of the garbage, and compare them with the standard images of various types of garbage in the database; at the same time, the monitoring video is analyzed in real time, and the image recognition algorithm is used to identify the type of garbage thrown by the user, the delivery action, etc., to identify whether the user has performed classification operations before delivery and whether it meets the standardized pre-classification requirements. Through learning and analysis of large amounts of video data, the system can continuously improve the accuracy and reliability of recognition.

[0026] S3. The initial points obtained by the user are calculated by analyzing the quality of the garbage delivered by the user in the intelligent garbage collection system.

[0027] Specifically, the initial points obtained by the user in step S3 are the weight of the garbage delivered by the user multiplied by the point pricing of the garbage; the weight of the delivered garbage is sensed by the weight sensor inside the smart garbage recycling box, and the point pricing of the garbage is determined by the garbage recycling demand, the value of the garbage itself and the difficulty of garbage disposal.

[0028] Specifically, the calculation formula for garbage point pricing is: Garbage point pricing A= ; k is the adjustment coefficient, which is a constant determined according to local actual conditions and policy factors. It is used to fine-tune the overall pricing. The value range is between 0.5-2. For example, if the local area hopes to encourage the recycling of iron, plastic products and cardboard, the k value will be appropriately increased; D is the garbage recycling demand factor, which reflects the market or garbage disposal system’s demand for a specific type of garbage. It is determined by analyzing the processing capacity of local garbage disposal plants or the demand factors of resource recycling companies. The value range is 0-10 and increases with the increase in demand. For example, the demand for mineral water bottles, beverage bottles, aluminum cans and iron cans is large, which increases the D value; S is the garbage supply factor, which reflects the amount of garbage put in during a certain period of time, and is related to the living habits and seasons of local residents. The value range is 0-10, and it increases with the increase of the amount of garbage put in. For example, in summer, the amount of melon peel kitchen waste or fish, shrimp and shellfish waste in the season of abundant fishing in the south increases, and the S value increases; V is the value factor of the garbage itself, which is determined according to the material composition and recyclable value of the garbage. The value range can be set to 0-1, and it increases with the increase of value. For example, recyclable metals such as iron, copper, aluminum and tin are given higher points, and glass slag, plastic bags and toilet paper are given lower points; for example, for a certain type of garbage, k=1.2 means that the local area hopes to encourage recycling, D=8 means that the demand for this garbage is high, S=4 means that the amount of garbage put in is low, V=0.8 means that the value of the garbage itself is high, then the integral pricing calculation formula is: The integral pricing of garbage A= =1.92 points / kg.

[0029] S4. Calculate the initial points based on the user's credit and the user's delivery behavior to obtain the final points earned by the user.

[0030] Specifically, Figure 2Step S4 shown is to calculate the product of the user's credit parameter, the user's delivery accuracy and the pre-classification index; if the delivery is lower than the set delivery limit value L, the points obtained by the user for this delivery need to be frozen, and the L value is 0.5; if the delivery is higher than the set delivery compensation value H, the user's previously frozen points need to be unfrozen, and the H value is 0.8; the unfrozen points value is adjusted based on the points value obtained after the delivery reaches the delivery compensation value H; for example, if the pre-classification index CPI of a delivery is 0.86, the delivery accuracy is 93%, and the user's credit parameter CP is 0.908, then 0.86× 93%×0.908=0.726, then the points for this delivery will not be frozen; if the pre-classification index CPI of a certain delivery is 0.7, the delivery accuracy is 85%, and the user credit parameter CP=0.75, then 0.7×85%×0.75=0.446, then the points for this delivery will be frozen; if the pre-classification index CPI of a certain delivery is 0.9, the delivery accuracy is 97%, and the user credit parameter CP=0.95, then 0.9×97%×0.95=0.829, if there were points frozen before, the frozen points will be unfrozen based on the current points value.

[0031] Specifically, the points finally obtained by the user in step S4 are obtained based on the point incentive method for garbage recycling at various stages of the garbage recycling process covered by the user, behavior and terminal, and the calculation formula is: the user obtains the final points W=m×A×CPI×N×CP; wherein m represents the weight of the garbage; A represents the point pricing of the garbage; CPI represents the user's pre-classification index, ranging from 0-1; N represents the point multiplier corresponding to the delivery accuracy, ranging from 0.8-1.2; CP represents the user's credit parameter, ranging from 0-1; for example, when delivering garbage in a certain time, m=10kg; A=1.92 points / kg; CPI=0.86, the user's pre-classification index is relatively high; N=1.1, the user's delivery accuracy is relatively high and the rating is good; CP=0.908, the user's credit parameter is relatively high; then the user obtains the final points W=10×1.92×0.86×1.1×0.908=16.49 points.

[0032] S5. After the delivery is completed, the user can view the delivery details and points redemption. Specifically, the delivery details in step S5 include the five-level administration and site name of the delivery point, the type of garbage delivered, the time of garbage delivery, the amount of garbage delivered, the points generated by the delivery, the evaluation of the delivery behavior and the frozen points status information, so that the user can understand his delivery situation and the basis for obtaining points, which enhances the transparency of the entire incentive mechanism and the user's right to know, and improves the user's trust in the system.

[0033] The user data and other data involved in this application have been obtained with full consent and authorization, and the collection, use and processing of relevant information comply with the relevant laws, regulations and standards of relevant countries and regions.

[0034] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A point incentive method based on intelligent garbage collection, characterized in that: The following steps are involved: S1. Verify user information and judge the user's credit; S2. Monitor and record the user's delivery behavior and obtain the parameters of the user's delivery behavior; S3. Calculate the initial points obtained by the user by analyzing the quality of the garbage delivered by the user in the intelligent garbage collection; S4. Calculate the initial points based on the user's credit and the user's delivery behavior to obtain the user's final points; S5. After delivery is completed, the user can check the delivery details and redeem points.

2. The point incentive method based on intelligent garbage collection according to claim 1, characterized in that: The user's credit in step S1 is a reflection of the feedback of the user's historical delivery situation recorded over a period of time, and depends on the accuracy of the user's previous deliveries. The closer the delivery is to the most recent time, the greater the reference weight; The credit parameter is a quantitative indicator of the accuracy of the user's time-series delivery behavior, which is used to evaluate the user's credit.

3. The point incentive method based on intelligent garbage collection according to claim 2 is characterized in that: The calculation formula of the credit parameter is: Credit parameter CP ; where P i Indicates the accuracy of the i-th delivery before this delivery, ranging from 0 to 1; T i It represents the weight of the i-th delivery before the current delivery, ranging from 0.4 to 1, and decreases as i increases.

4. The point incentive method based on intelligent garbage collection according to claim 1 is characterized in that: Step S2 monitors the user's delivery behavior, including monitoring the user's delivery accuracy and the user's garbage pre-classification behavior. The delivery accuracy is the ratio of the number of garbage correctly classified and placed in the corresponding category delivery port to the total number of garbage delivered; the user's garbage classification delivery qualification rating is divided into excellent, good, medium and poor according to the delivery accuracy, and different qualification ratings obtain different point multiples N; the excellent rating corresponds to a delivery accuracy of 95%-100%, and obtains a 1.1-1.2 point multiple; the good rating corresponds to a delivery accuracy of 80%-95%, and obtains a 1.0-1.1 point multiple; the medium rating corresponds to a delivery accuracy of 60%-80%, and obtains a 0.9-1.0 point multiple; the poor rating delivery accuracy is 59% and below, and obtains a 0.8-0.9 point multiple; the user's garbage pre-classification behavior refers to the user's behavior of preliminarily classifying the garbage before delivering it to the intelligent garbage recycling equipment, which is used to improve the overall garbage recycling efficiency.

5. The point incentive method based on intelligent garbage collection according to claim 4 is characterized in that: The user's garbage pre-classification behavior is quantified by the pre-classification index, and the calculation formula of the pre-classification index is: CPI=a×PL+b×PA, where a represents the pre-classification readiness weight; PL represents the pre-classification readiness, which represents the user's readiness to classify the garbage before delivering it, ranging from 0 to 1; b represents the weight of the pre-classification accuracy; PA represents the accuracy of user pre-classification, ranging from 0 to 1.

6. The point incentive method based on intelligent garbage collection according to claim 1 is characterized in that: The initial points obtained by the user in step S3 are the weight of the garbage delivered by the user multiplied by the point pricing of the garbage; the weight of the delivered garbage is sensed by the weight sensor inside the smart garbage recycling box, and the point pricing of the garbage is determined by the supply and demand relationship of the garbage and the value of the garbage itself.

7. The point incentive method based on intelligent garbage collection according to claim 6 is characterized in that: The calculation formula of the garbage point pricing is: garbage point pricing A= ; k is the adjustment coefficient, which is a constant determined according to local actual conditions and policy factors. It is used to fine-tune the overall pricing and has a value range of 0.5-2; D is the garbage recycling demand factor, which reflects the market or garbage disposal system's demand for a specific type of garbage. It has a value range of 1-10 and increases with the increase in demand; S is the garbage supply factor, which reflects the amount of this type of garbage put into use in a certain period of time. It has a value range of 1-10 and increases with the amount of garbage put into use; V is the garbage value factor itself, which has a value range of 0-1 and increases with the increase in the value of the garbage itself.

8. The point incentive method based on intelligent garbage collection according to claim 1 is characterized in that: Step S4 calculates the product of the user's credit parameter, the user's delivery accuracy and the pre-classification index. If it is lower than the set delivery limit value L, the points obtained by the user for this delivery need to be frozen, and the L value is 0.5; if it is higher than the set delivery compensation value H, the user's previously frozen points need to be unfrozen, and the H value is 0.8; the unfrozen points value is adjusted based on the points value obtained after the delivery reaches the delivery compensation value H for this delivery.

9. The point incentive method based on intelligent garbage collection according to claim 1 is characterized in that: The points finally obtained by the user in step S4 are obtained based on the points incentive method for garbage collection at various stages of the garbage collection process covered by the user, behavior and terminal, and the calculation formula is: the final points obtained by the user W = m × A × CPI × N × CP; Among them, m represents the weight of the garbage; A represents the point pricing of the garbage; CPI represents the user's pre-classification index, ranging from 0-1; N represents the point multiplier corresponding to the delivery accuracy, ranging from 0.8-1.2; CP represents the user's credit parameter, ranging from 0-1.

10. The point incentive method based on intelligent garbage collection according to claim 1, characterized in that: The delivery details in step S5 include the five-level administration and site name of the delivery point, the type of garbage delivered, the time of garbage delivery, the amount of garbage delivered, the points generated by delivery, the delivery behavior evaluation and the frozen points status information.

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