Technical label and dark label electronic review method based on multi-dimensional dynamic dislocation sorting

Abstract

The invention relates to the technical field of intelligent bid evaluation, in particular to a technical bid and dark bid electronic review method based on multi-dimensional dynamic dislocation sequencing, and the method comprises the steps: obtaining review data of a bid invitation project, the review data comprising scoring point information, bidding unit information and review expert information; for the first scoring point, randomly sorting all bidding units and all review experts; according to the sequence of the scoring points, performing random sorting operation of all the bidding units and all the review experts on each following scoring point until all the scoring points complete the random sorting operation; and reviewing each scoring point based on the randomly sorted bidding units and reviewing experts. The identity recognition possibility in the review process can be eliminated to the maximum extent, human factor interference is reduced, independent review of each judge is achieved, and therefore the fairness in the review process is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of intelligent bid evaluation technology, and in particular to an electronic evaluation method for technical bids and blind bids based on multi-dimensional dynamic staggered sorting. Background Art

[0002] In the bidding process, technical bid review is an important part of evaluating the quality of bidding schemes, which is directly related to the implementation effect of the project and the scientific nature of subsequent management. With the development of information technology, technical bid review has gradually introduced electronic systems to improve review efficiency and ensure the fairness and standardization of the review. The electronic blind bid review mode has gradually become the mainstream method of technical bid review because it can effectively reduce the review deviation caused by subjective factors of the judges during the review process.

[0003] At present, the electronic blind bidding evaluation system usually adopts a one-dimensional anonymous evaluation mechanism, shielding the identity information of the bidding units through anonymization processing to reduce the interference of human emotional factors on the evaluation results. The evaluation system usually uses encrypted unit numbers (such as "Unit 1", "Unit 2", etc.) in the technical bid content instead of the actual bidding unit name, and displays the scoring content of each bidding unit according to each scoring point, and the judges independently score each scoring point.

[0004] The existing electronic review method for technical bids still has certain limitations, especially under the one-dimensional sorting mechanism, when experts review, they may still infer the identity of the bidder by observing pattern recognition, thus generating a tendency to score subjectively. Despite the implementation of anonymization, the fixed scoring order and review method may still lead to deviations in the review process, increasing the interference of human factors, thus seriously affecting the fairness of the review results. Summary of the invention

[0005] This application provides an electronic review method for technical bids based on multi-dimensional dynamic staggered sorting, which can eliminate the possibility of identity recognition in the review process to the greatest extent, reduce interference from human factors, and achieve independent review by each judge, thereby ensuring fairness in the review process. This application provides the following technical solutions: In a first aspect, the present application provides a method for electronic review of technical bids and blind bids based on multi-dimensional dynamic staggered sorting, the method comprising: Obtaining evaluation data of the bidding project, wherein the evaluation data includes scoring point information, bidding unit information, and evaluation expert information; For the first scoring point, all bidding units and all evaluation experts are randomly sorted; Perform random sorting of all bidders and review experts for each of the following scoring points in the order of the scoring points until all scoring points have completed the random sorting operation; The bidders and evaluation experts will evaluate each scoring point based on the random ranking.

[0006] In a specific implementation scheme, the acquisition of evaluation data of the bidding project, the evaluation data including scoring point information, bidding unit information and evaluation expert information includes: The scoring point information includes the specific content of the scoring point and the total number of scoring points. Each scoring point is assigned a unique identifier. , the scoring point information is recorded as ,in Indicates the number of scoring points; The bidding unit information includes the name of the bidding unit and the total number of bidding units. Each bidding unit is also assigned a unique identifier. , the bidding unit information is recorded as ,in Indicates the number of bidding units; The reviewer information includes the number of reviewers, and each reviewer is also assigned a unique identifier. , the review expert information is recorded as ,in Indicates the number of review experts.

[0007] In a specific implementation scheme, for the first scoring point, all bidding units are randomly sorted including: Randomly sort the bidding units and initialize a list including the identifiers of all bidding units ; Use the Fisher-Yates shuffle algorithm to shuffle the list, randomly select an element from the last element to swap, and set the current index to , use the random number generator to randomly generate an index ,in ; Swap the index in the list and index The elements of Subtract 1 and repeat the previous index operation until ; After the disruption, a new order of bidding units is formed.

[0008] In a specific implementation scheme, for the first scoring point, all review experts are randomly sorted including: Randomly sort the review experts and initialize a list including the identifiers of all review experts ; Use the Fisher-Yates shuffle algorithm to shuffle the list, randomly select an element from the last element to swap, and set the current index to , use the random number generator to randomly generate an index ,in ; Swap the index in the list and index The elements of Subtract 1 and repeat the previous index operation until ; After the disruption, a new order of bidding units is formed.

[0009] In a specific possible implementation scheme, for the first scoring point, after randomly sorting all the bidders and all the evaluation experts, the following steps are further included: After each sorting is completed, the current sorting result is saved in a history record; For each new sort result, check whether it already appears in the history record; If the sorting result is found to appear in the historical records, it will be randomly sorted again until the sorting result is not repeated.

[0010] In a specific implementation scheme, for the first scoring point, all bidding units are randomly sorted including: Collection of bidding units Need to sort, introduce two parameters, the size of the set, that is, the number of bidding units and interference factor set ,in , each is a randomly generated floating point number with a value range of ; Pair Collection Each bidding unit ,in , calculate the priority value , the formula is as follows: ; in, is the current timestamp, Indicates the current number and interference factors The product of Based on the calculated priority value Sort all bidding units from largest to smallest.

[0011] In a specific implementation scheme, for the first scoring point, all bidding units are randomly sorted including: Collection of bidding units Need to sort, introduce two parameters, the size of the set, that is, the number of bidding units and interference factor set ,in , each is a randomly generated floating point number with a value range of ; Pair Collection Each bidding unit ,in , calculate the priority value , the formula is as follows: ; in, Indicates the current number and interference factors The product of is a constant, Represents the modulus operation in mathematics, used to find Divide by The remainder after Indicates the use of the existing FNV-1a hash algorithm for Generated random numbers; Based on the calculated priority value Sort all bidding units from largest to smallest.

[0012] In the second aspect, the present application provides an electronic review system for technical bids and blind bids based on multi-dimensional dynamic staggered sorting, which adopts the following technical solutions: An electronic review system for technical and hidden bids based on multi-dimensional dynamic staggered sorting, including: A data acquisition module is used to acquire the evaluation data of the bidding project, wherein the evaluation data includes scoring point information, bidding unit information and evaluation expert information; The first random sorting module is used to randomly sort all the bidding units and all the evaluation experts for the first scoring point; The second random sorting module is used to perform random sorting operations on all bidding units and all evaluation experts for each subsequent scoring point according to the order of the scoring points until the random sorting operation is completed for all scoring points; The electronic review module is used to review each scoring point based on the randomly sorted bidders and review experts.

[0013] In a third aspect, the present application provides an electronic device comprising a processor and a memory; the memory stores a program, and the program is loaded and executed by the processor to implement a method for electronic review of technical bids and blind bids based on multi-dimensional dynamic staggered sorting as described in the first aspect.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, in which a program is stored. When the program is executed by a processor, it is used to implement a method for electronic review of technical bids and hidden bids based on multi-dimensional dynamic staggered sorting as described in the first aspect.

[0015] In summary, the beneficial effects of this application include at least: 1) By randomly sorting the bidders and the review experts at each scoring point, the bias in the review process is eliminated. The traditional one-dimensional sorting mechanism may cause the review experts to have potential subjective influence based on the order of the bidders, such as inferring the identity information of the bidders through the order of the scoring points, thereby affecting the objectivity of the scoring. This application randomizes the order of the bidders and the review experts through the Fisher-Yates shuffle algorithm, so that the scoring is completely independent of the order of the bidders and the bias of the review experts, thereby improving the fairness of the review.

[0016] 2) A sorting verification mechanism is introduced to ensure that the results of each sorting are unique, avoiding repeated sorting results affecting the randomness of the review. In traditional methods, the random sorting of the review process may affect fairness due to accidental repetition, while the verification step ensures the uniqueness of each sorting. By constantly checking the historical sorting results and shuffling them, it ensures that all sortings have good randomness, thereby improving the transparency of the review process and the credibility of the system. This technical effect ensures the fairness of all bidders in each sorting and avoids the impact of accidental factors on the results.

[0017] By randomly sorting the bidders and review experts, subjective bias and human interference in the review process are effectively reduced, thereby ensuring the fairness and impartiality of the review results. Specifically, in the review process of each scoring point, this method first randomly sorts the bidders and review experts, and uses the Fisher-Yates shuffling algorithm to ensure the uniqueness of each sorting result. By repeating this random sorting process for all scoring points, the deviation that may be caused by the review order is further eliminated. In addition, a sorting result verification mechanism is introduced to ensure that the results after each sorting are independent and non-repetitive. This technical solution eliminates potential identification problems caused by fixed order through dynamic staggered sorting, avoids experts from inferring the identity of the bidder by observing the sorting, thereby effectively improving the objectivity and transparency of the review, and reducing the interference of human emotional factors on the review results, solving the problem of review process deviation existing in the prior art.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present application in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the electronic review method of technical bids and blind bids based on multi-dimensional dynamic staggered sorting in the embodiment of the present application.

[0020] Figure 2 It is a schematic diagram of the overall process of the electronic review method for technical bids and blind bids based on multi-dimensional dynamic staggered sorting in the embodiment of the present application.

[0021] Figure 3 It is a structural block diagram of the electronic review system for technical bids and hidden bids based on multi-dimensional dynamic staggered sorting in the embodiment of the present application.

[0022] Figure 4 It is a block diagram of an electronic device for electronic review of technical bids and blind bids based on multi-dimensional dynamic staggered sorting in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0024] Optionally, the present application takes the electronic review method for technical bids and blind bids based on multi-dimensional dynamic staggered sorting provided in various embodiments as an example of being used in an electronic device, where the electronic device is a terminal or a server, and the terminal may be a mobile phone, a computer, a tablet computer, etc. This embodiment does not limit the type of electronic device.

[0025] Reference Figure 1 , is a flow chart of a method for electronic review of technical bids and blind bids based on multi-dimensional dynamic staggered sorting provided by an embodiment of the present application, the method comprising at least the following steps: Step S101: Acquire the evaluation data of the bidding project, where the evaluation data includes scoring point information, bidding unit information, and evaluation expert information.

[0026] In step S101, evaluation data is obtained and sorted from the bidding project, including scoring point information, bidding unit information, and evaluation expert information.

[0027] First, extract all the scoring point information from the technical bid of the bidding project. The scoring point information includes the specific content of the scoring point and the total number of scoring points. Each scoring point represents an evaluation indicator, such as "construction schedule planning", "environmental assurance measures", etc. The number of scoring points is usually predetermined and closely related to the specific requirements of the project. In this application, each scoring point is assigned a unique identifier , the scoring point information is recorded as ,in Indicates the number of rating points.

[0028] Secondly, the bidding unit information is extracted from all the bidding units in the bidding project. The bidding unit information includes the name of the bidding unit and the total number of bidding units. The number of bidding units is usually determined and disclosed by the bidding party. Each bidding unit is also assigned a unique identifier. , the bidding unit information is recorded as ,in Indicates the number of bidding units.

[0029] Finally, collect the information of all review experts involved in the review, including the number of review experts, and each review expert will also be assigned a unique identifier , the review expert information is recorded as ,in Indicates the number of review experts.

[0030] Step S102: For the first scoring point, all bidding units and all evaluation experts are randomly sorted.

[0031] In step S102, the bidding units are first randomly sorted and a list is initialized, including the identifiers of all bidding units. , then use the Fisher-Yates shuffle algorithm to shuffle the list, randomly select an element from the last element to swap, and set the current index to , use the random number generator to randomly generate an index ,in , swap the index in the list and index The elements of Subtract 1 and repeat the previous index operation until . After the disruption, a new order of bidding units is formed, for example Then the review experts are randomly sorted, and similarly, a list is initialized, including the identifiers of all review experts. , then use the Fisher-Yates shuffle algorithm to shuffle the list, randomly select an element from the last element to swap, and set the current index to , use the random number generator to randomly generate an index ,in , swap the index in the list and index The elements of Subtract 1 and repeat the previous index operation until . After the disruption, a new order of bidding units is formed, for example .

[0032] In addition, as a preferred method, after randomly sorting all bidders and all review experts in implementation, a verification step can be added to ensure that there is no duplication in the sorting results of bidders or review experts. The specific verification step is: check whether the sorting order of bidders or review experts is the same as the previous sorting order. If they are the same, it means there is duplication and reshuffle is required.

[0033] Specifically, after each sort is completed, the current sort result and Save in a history record (such as a list or set), and for each new sort result, check whether it has appeared in the history record. If repeated sorting occurs, it means that the sorting results are the same, and the Fisher-Yates shuffle algorithm needs to be re-executed. If repeated sorting results are found, the Fisher-Yates shuffle algorithm is re-executed until the sorting results are not repeated. By introducing a verification step, it is ensured that the results after each sorting are unique, thereby avoiding accidental sorting duplications and enhancing randomness and fairness. The introduction of a verification step helps to further increase the randomness of random sorting results, avoid multiple occurrences of the same sorting result, and improve the fairness of the sorting.

[0034] In a feasible embodiment, the following method may also be used to randomly sort all bidding units and all evaluation experts. Since the method of randomly sorting bidding units or evaluation experts is the same, the following description will only be made by taking bidding units as an example: gather Need to sort, introduce two parameters, the size of the set, that is, the number of bidding units and interference factor set ,in , each is a randomly generated floating point number with a value range of Then, the set Each bidding unit ,in , calculate the priority value , the formula is as follows: ; in, Indicates the current number and interference factors The product of , the role of the product is to increase the randomness of the sorting by applying a unique random perturbation to each bidding unit. It is a constant, a positive integer, and its default value is set to 2, which can be adjusted according to the actual scenario. It is a parameter used to introduce additional disturbances in the sorting process. Its core function is to group the collections to a certain degree of logic according to the total number of bidding units or evaluation experts, thereby increasing the level of randomness in the sorting process. When the value is small, the disturbance is small, and the sorting logic depends more on the interference factor and hash disturbance. When the value is large, the randomness of the sorting is further enhanced, but it may introduce too much complexity. For example, in a scenario with a large number of bidding units, by reasonably selecting The value can prevent the sorting results from being too concentrated in some local areas. Represents the modulus operation in mathematics, used to find Divide by The remainder after this operation introduces hierarchical perturbations in the sorting, which can be based on Different values ​​of adjust the sorting hierarchy. Indicates the use of the existing FNV-1a hash algorithm for The generated random number. The hash algorithm is used to further increase randomness and avoid excessive repetition of results in calculations.

[0035] Finally, based on the calculated priority value The bidders are sorted from largest to smallest, and the priority value of each bidder is determined based on its number, interference factor, and disturbance factor (generated by modulo operation and hash function). Therefore, the sorting result will have a strong randomness, avoiding the fixity or bias of the order of bidders, and ensuring that each review expert sees a different order of bidders when reviewing different scoring points. Similarly, the random sorting method of the review experts is the same as above, and will not be repeated here.

[0036] Among the above methods, the Fisher-Yates shuffle algorithm is a classic random permutation algorithm, which is widely used to generate a random order of a set. It works by selecting elements in the set in turn and exchanging them with an element in the remaining elements to finally obtain a random order. However, a potential problem with the Fisher-Yates algorithm is that if the order is generated based on the same initial set when multiple review experts review, then even if the order seen by each review expert is random, the actual order generation process may have certain regularity or repeatability. Especially at the same scoring point, multiple experts may see the same order, which will affect the fairness of the review results.

[0037] Based on this, by introducing a set of interference factors, the ranking weight of each bidding unit is affected by a random floating factor, so that even for the same bidding unit, its ranking priority is different due to the unique interference factor of each bidding unit. This random floating greatly enhances the randomness of the sorting, avoids the repetitiveness or fixed pattern of the sorting results, and thus improves fairness. At the same time, additional perturbations are added to the sorting through modular operations and hash functions. Modular operations refine the granularity of sorting by adjusting the relationship between the total number and the perturbation factor, while hash functions provide unpredictable perturbations, so that the sorting process is not just a simple exchange based on the initial order, but is reconstructed through additional irreversible perturbations. In addition, since the formula introduces multiple perturbation factors, this method can adjust the sorting rules according to the actual scenario and is suitable for review environments of different scales and complexities.

[0038] In another feasible embodiment, the following method can also be used to randomly sort all the bidding units and all the evaluation experts. Since the method of randomly sorting the bidding units or the evaluation experts is the same, the following still takes the bidding units as an example for explanation: Based on the second embodiment, similarly, the set Need to sort, introduce two parameters, the size of the set, that is, the number of bidding units and interference factor set ,in , each is a randomly generated floating point number with a value range of Then, the set Each bidding unit ,in , calculate the priority value , the formula is as follows: ; in, The current timestamp will increase over time. Since the timestamp increases by 1 every second, it is a monotonically increasing integer. The timestamp of each second is unique, representing the number of seconds from a fixed starting point to the current moment. is an adjustment coefficient used to control the influence range of the timestamp in the priority calculation. In this application, it is used to adjust so that the calculation result of the above formula is in the same range as the calculation result in the second embodiment. Finally, based on the calculated priority value The bid units are sorted in order from largest to smallest. This step is made more dynamic and unique by introducing a timestamp sorting process. The timestamp changes every second, meaning that a new and unique time factor is involved each time the sorting occurs. Even for the same bid unit, the sorting results will vary over time, enhancing randomness and unpredictability.

[0039] Optionally, the priority value can also be calculated using the second embodiment and the third embodiment respectively. After that, the final priority value is obtained by adding and averaging, and then the bidding units are sorted in descending order according to the final priority value. This operation makes the two embodiments have a certain complementary effect, and the priority value finally obtained will be more comprehensive. The previous embodiments rely on interference factors and certain mathematical operations (such as hashing, modular operations, etc.) to disrupt the order of sorting. These methods effectively increase the disturbance and randomness, but are still static. Once the disturbance factor and random operation are set, the sorting result will be fixed to a certain extent. After the introduction of the timestamp, every second of change will introduce new disturbances, making the sorting results more dynamic and avoiding the disadvantages of long-term dependence on the same random factor. The timestamp provides real-time and constantly changing disturbances, so that the priority results of each sorting have differences in the time dimension. In addition, the adjustment coefficient ensures that the timestamp will not have too much or too little impact on the sorting results. The adjustment coefficient makes the calculation results of the two formulas within the same range and will not produce a situation where the results are too different.

[0040] Step S103: Perform a random sorting operation on all the bidding units and all the evaluation experts for each of the following scoring points in the order of the scoring points until the random sorting operation is completed for all the scoring points.

[0041] In step S103, for each scoring point , randomly sort the bidders and the review experts in turn according to the order of the scoring points until all the scoring points are sorted. The specific operation is the same as step S102, and the random sorting of the bidders and the review experts is performed independently for each scoring point to ensure that the sorting of each scoring point is random.

[0042] Step S104: The randomly sorted bidders and review experts review each scoring point.

[0043] In step S104, for each scoring point, the random sorting of the set of bidders and the set of review experts has been completed, so the order of the review experts and the bidders has been fixed at the scoring point. For each scoring point, the review experts will score all bidders in turn according to their review criteria for the scoring point. Each review expert scores each bidder in turn, and the scoring results will be recorded according to the expert's judgment criteria. After all review experts have completed the review of all bidders, all scoring data will be organized into the final review results. At this point, the scoring data is not only based on the quality of the bidders and the judgment of the review experts, but also affected by the random sorting process, ensuring the randomness and fairness of the sorting.

[0044] In summary, combined with Figure 2 , by randomly sorting the bidders and evaluation experts, the subjective bias and human interference in the evaluation process are effectively reduced, thereby ensuring the fairness and impartiality of the evaluation results. Specifically, in the evaluation process of each scoring point, this method first randomly sorts the bidders and evaluation experts, and uses the Fisher-Yates shuffling algorithm to ensure the uniqueness of each sorting result. By repeating this random sorting process for all scoring points, the deviation that may be caused by the evaluation order is further eliminated. In addition, a sorting result verification mechanism is introduced to ensure that the results after each sorting are independent and non-repetitive. This technical solution eliminates the potential identification problems caused by fixed order through dynamic staggered sorting, avoids experts from speculating on the identity of the bidder by observing the sorting, thereby effectively improving the objectivity and transparency of the review, and reducing the interference of human emotional factors on the review results, solving the problem of review process deviation existing in the prior art.

[0045] Figure 3 This is a structural block diagram of a technical bid and blind bid electronic review system based on multi-dimensional dynamic staggered sorting provided by an embodiment of the present application. The system includes at least the following modules: The data acquisition module is used to obtain the evaluation data of the bidding project, which includes scoring point information, bidding unit information and evaluation expert information; The first random sorting module is used to randomly sort all the bidding units and all the evaluation experts for the first scoring point; The second random sorting module is used to perform random sorting operations on all bidding units and all evaluation experts for each subsequent scoring point according to the order of the scoring points until the random sorting operation is completed for all scoring points; The electronic review module is used to review each scoring point based on the randomly sorted bidders and review experts.

[0046] For relevant details, refer to the above method embodiment.

[0047] Figure 4 4 is a block diagram of an electronic device provided by an embodiment of the present application. The device at least includes a processor 401 and a memory 402.

[0048] The processor 401 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 401 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 401 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 401 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 401 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0049] The memory 402 may include one or more computer-readable storage media, which may be non-transitory. The memory 402 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 402 is used to store at least one instruction, which is used to be executed by the processor 401 to implement the electronic review method of the technical bid and blind bid based on multi-dimensional dynamic misalignment sorting provided in the method embodiment of the present application.

[0050] In some embodiments, the electronic device may further optionally include: a peripheral device interface and at least one peripheral device. The processor 401, the memory 402 and the peripheral device interface may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface via a bus, a signal line or a circuit board. Schematically, the peripheral devices include but are not limited to: a radio frequency circuit, a touch display screen, an audio circuit, and a power supply.

[0051] Of course, the electronic device may also include fewer or more components, which is not limited in this embodiment.

[0052] Optionally, the present application also provides a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the electronic review method for technical bids and blind bids based on multi-dimensional dynamic staggered sorting of the above method embodiment.

[0053] Optionally, the present application also provides a computer product, which includes a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the electronic review method for technical bids and blind bids based on multi-dimensional dynamic staggered sorting of the above-mentioned method embodiment.

[0054] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0055] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for electronic review of technical bids and blind bids based on multi-dimensional dynamic staggered sorting, characterized in that: The method comprises: Obtaining evaluation data of the bidding project, wherein the evaluation data includes scoring point information, bidding unit information, and evaluation expert information; For the first scoring point, all bidding units and all evaluation experts are randomly sorted; Perform random sorting of all bidders and review experts for each of the following scoring points in the order of the scoring points until all scoring points have completed the random sorting operation; The bidders and evaluation experts will evaluate each scoring point based on the random ranking.

2. The electronic review method for technical bids and hidden bids based on multi-dimensional dynamic staggered sorting according to claim 1 is characterized in that: The acquisition of evaluation data of the bidding project, wherein the evaluation data includes scoring point information, bidding unit information and evaluation expert information, includes: The scoring point information includes the specific content of the scoring point and the total number of scoring points. Each scoring point is assigned a unique identifier. , the scoring point information is recorded as ,in Indicates the number of scoring points; The bidding unit information includes the name of the bidding unit and the total number of bidding units. Each bidding unit is also assigned a unique identifier. , the bidding unit information is recorded as ,in Indicates the number of bidding units; The reviewer information includes the number of reviewers, and each reviewer is also assigned a unique identifier. , the review expert information is recorded as ,in Indicates the number of review experts.

3. The electronic review method for technical bids and hidden bids based on multi-dimensional dynamic staggered sorting according to claim 2 is characterized in that: For the first scoring point, all bidders are randomly sorted to include: Randomly sort the bidding units and initialize a list including the identifiers of all bidding units ; Use the Fisher-Yates shuffle algorithm to shuffle the list, randomly select an element from the last element to swap, and set the current index to , use the random number generator to randomly generate an index ,in ; Swap the index in the list and index The elements of Subtract 1 and repeat the previous index operation until ; After the disruption, a new order of bidding units is formed.

4. The electronic review method for technical bids and hidden bids based on multi-dimensional dynamic staggered sorting according to claim 2 is characterized in that: For the first scoring point, all reviewers are randomly sorted as follows: Randomly sort the review experts and initialize a list including the identifiers of all review experts ; Use the Fisher-Yates shuffle algorithm to shuffle the list, randomly select an element from the last element to swap, and set the current index to , use the random number generator to randomly generate an index ,in ; Swap the index in the list and index The elements of Subtract 1 and repeat the previous index operation until ; After the disruption, a new order of bidding units is formed.

5. The electronic review method for technical bids and hidden bids based on multi-dimensional dynamic staggered sorting according to claim 1 is characterized in that: For the first scoring point, after randomly sorting all the bidding units and all the evaluation experts, the following steps are also included: After each sorting is completed, the current sorting result is saved in a history record; For each new sort result, check whether it already appears in the history record; If the sorting result is found to appear in the historical records, it will be randomly sorted again until the sorting result is not repeated.

6. The electronic review method for technical bids and hidden bids based on multi-dimensional dynamic staggered sorting according to claim 2 is characterized in that: For the first scoring point, all bidders are randomly sorted to include: Collection of bidding units Need to sort, introduce two parameters, the size of the set, that is, the number of bidding units and interference factor set ,in , each is a randomly generated floating point number with a value range of ; Pair Collection Each bidding unit ,in , calculate the priority value , the formula is as follows: ； in, is the current timestamp, Indicates the current number and interference factors The product of is the adjustment coefficient; Based on the calculated priority value Sort all bidding units from largest to smallest.

7. The electronic review method for technical bids and hidden bids based on multi-dimensional dynamic staggered sorting according to claim 2 is characterized in that: For the first scoring point, all bidders are randomly sorted to include: Collection of bidding units Need to sort, introduce two parameters, the size of the set, that is, the number of bidding units and interference factor set ,in , each is a randomly generated floating point number with a value range of ; Pair Collection Each bidding unit ,in , calculate the priority value , the formula is as follows: ； in, Indicates the current number and interference factors The product of is a constant, Represents the modulus operation in mathematics, used to find Divide by The remainder after Indicates the use of the existing FNV-1a hash algorithm for Generated random numbers; Based on the calculated priority value Sort all bidding units from largest to smallest.

8. An electronic review system for technical bids and blind bids based on multi-dimensional dynamic staggered sorting, characterized by: include: A data acquisition module is used to acquire the evaluation data of the bidding project, wherein the evaluation data includes scoring point information, bidding unit information and evaluation expert information; The first random sorting module is used to randomly sort all the bidding units and all the evaluation experts for the first scoring point; The second random sorting module is used to perform random sorting operations on all bidding units and all evaluation experts for each subsequent scoring point according to the order of the scoring points until the random sorting operation is completed for all scoring points; The electronic review module is used to review each scoring point based on the randomly sorted bidders and review experts.

9. An electronic device, characterized in that: The device includes a processor and a memory; a program is stored in the memory, and the program is loaded and executed by the processor to implement the electronic review method for technical bids and blind bids based on multi-dimensional dynamic dislocation sorting as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a program, which, when executed by the processor, is used to implement the electronic review method for technical bids and blind bids based on multi-dimensional dynamic staggered sorting as described in any one of claims 1 to 7.

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